TY - JOUR AU - Dong, Xueyan AU - Cao, Jiannong AU - Zhao, Weiheng TI - A review of research on remote sensing images shadow detection and application to building extraction JF - EUROPEAN JOURNAL OF REMOTE SENSING J2 - EUR J REMOTE SENS VL - 57 PY - 2024 IS - 1 PG - 22 SN - 2279-7254 DO - 10.1080/22797254.2023.2293163 UR - https://m2.mtmt.hu/api/publication/34614040 ID - 34614040 AB - Buildings are one of the most important habitats for humans, and therefore, accurate identification and extraction of building information in remote sensing images are crucial. Buildings in remote sensing images vary in shape and color due to differences in sensor acquisition methods, geographical location, and other factors. However, they all share a common feature - the presence of shadows. Obtaining accurate data from building shadows can provide a wealth of reliable information for building research. Consequently, it is crucial to review various methods for extracting building shadows, especially deep learning-based methods, to illustrate shadow implementation scenarios in building research: 1) building detection in very high resolution remote sensing images (VHRRSI); 2) building detection in SAR; 3) building change detection; 4) building damage assessment; 5) building height estimation; 6) building shadow removal; 7) other methods (such as building shadow data enhancement, detection of building shadows in ghost images, and conservation of historic buildings). This study discusses the advantages and disadvantages of building shadow detection methods and provides an overview of the datasets and evaluation metrics commonly used in studies of building shadow applications. We hope that this study will serve as a valuable reference for researchers in the field of building shadow studies. LA - English DB - MTMT ER - TY - JOUR AU - Ramalingam, Avudaiammal AU - George, Sam Varghese AU - Srivastava, Vandita AU - Alagala, Swarnalatha AU - Manickam, J. Martin Leo TI - Semantic Segmentation-Based Building Extraction in Urban Area Using Memory-Efficient Residual Dilated Convolutional Network JF - ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING J2 - ARAB J SCI ENG VL - & PY - 2024 SP - & SN - 2193-567X DO - 10.1007/s13369-023-08593-z UR - https://m2.mtmt.hu/api/publication/34525796 ID - 34525796 LA - English DB - MTMT ER - TY - JOUR AU - Srivastava, Vandita AU - Avudaiammal, R AU - V George, Sam TI - Investigations on extraction of buildings from RS imagery using deep learning models JF - INTERNATIONAL JOURNAL OF REMOTE SENSING J2 - INT J REMOTE SENS VL - 45 PY - 2024 IS - 1 SP - 68 EP - 100 PG - 33 SN - 0143-1161 DO - 10.1080/01431161.2023.2292016 UR - https://m2.mtmt.hu/api/publication/34525760 ID - 34525760 LA - English DB - MTMT ER - TY - JOUR AU - Cao, Yungang AU - Zhang, Shuang AU - Sui, Baikai AU - Xie, Yakun AU - Zhu, Jun TI - IBCO-Net: Integrity-Boundary-Corner Optimization in a General Multistage Network for Building Fine Segmentation From Remote Sensing Images JF - IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING J2 - IEEE T GEOSCI REMOTE VL - 61 PY - 2023 PG - 19 SN - 0196-2892 DO - 10.1109/TGRS.2023.3310534 UR - https://m2.mtmt.hu/api/publication/34288863 ID - 34288863 AB - Building extraction is a significant topic in high-resolution remote sensing. Insufficient integrity, irregular boundaries, and inaccurate corners remain a problem for existing methods. However, individually optimizing one of these aspects may leave problems in others. Unfortunately, few methods consider integrity, boundary, and corner simultaneously. In this study, we propose a three-stage network [integrity-boundary-corner optimization in a general multistage network (IBCO-Net)] incorporating integrity-boundary-corner optimization for fine segmentation of buildings. First, long-range dependent and spatial-continuous (LDSC) blocks are plugged into the decoder to enhance building integrity. Second, the direction field correction module (DFCM) controls the overall shape of the building by learning the direction field and executing an iterative correction algorithm. Finally, the multistrategy point refinement module (MSPRM) selects boundary and corner points for reclassification to further refine the boundary and relocate corners, and a hybrid loss function supervises IBCO-Net to optimize each stage. Comparative experiments were conducted on three datasets: the Massachusetts building dataset, the ISPRS Potsdam dataset, and the dataset of building instances of typical cities in China. We evaluated common pixel-level metrics and object-level boundary and corner metrics, with experimental results showing that IBCO-Net outperforms eight state-of-the-art convolution neural network (CNN) and transformer-based methods. In addition, the generality of the proposed method is demonstrated via its performance by applying nine existing backbone networks. LA - English DB - MTMT ER - TY - JOUR AU - Chen, Guoqing AU - Qian, Haizhong TI - A Method for Regularizing Buildings through Combining Skeleton Lines and Minkowski Addition JF - ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION J2 - ISPRS INT J GEO-INFORMATION VL - 12 PY - 2023 IS - 9 PG - 19 SN - 2220-9964 DO - 10.3390/ijgi12090363 UR - https://m2.mtmt.hu/api/publication/34288864 ID - 34288864 AB - With the increasing availability of remote sensing images, the regularization of jagged building outlines extracted from high-resolution remote sensing images has become a current research hotspot. Based on an existing method proposed earlier by this author for extracting the skeleton lines of buildings through integrating vector and raster data using jagged building skeleton lines as the input data, a new method is proposed here for regularizing building outlines through combining the skeleton lines with the Minkowski addition algorithm. Since the size and orientation of the structuring elements remain constant in the traditional morphological method, they can easily lead to large changes in the area between the regularized results and area of the original building. In this work, structuring elements are constructed with the adaptive adjustment of size and orientation. The proposed method has an outstanding ability to maintain the area of the original building. The orthogonal characteristics of the building can be better preserved via rotating the structuring elements. Finally, the angular bisector method is used to dissipate conflicts among the redundant vertices in the building outlines. In comparison to the simplification method used in QGIS software, the method proposed in this paper could reduce the variation in the area while maintaining the orthogonal characteristics of the building more significantly. LA - English DB - MTMT ER - TY - JOUR AU - Chen, Xijiang AU - Zhao, Bufan TI - An Efficient Global Constraint Approach for Robust Contour Feature Points Extraction of Point Cloud JF - IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING J2 - IEEE T GEOSCI REMOTE VL - 61 PY - 2023 PG - 16 SN - 0196-2892 DO - 10.1109/TGRS.2023.3308376 UR - https://m2.mtmt.hu/api/publication/34288865 ID - 34288865 AB - The contour feature points of object point clouds are the main features of human perception on target and play an important role in many fields, such as indoor model reconstruction, and object detection and location. In this article, we present a new method to extract the contour feature points of point cloud, which mainly includes two main contents: 1) the conspicuous and inconspicuous boundary points are extracted according to the characteristics of distribution of the azimuth between adjacent vectors in 2-D view and 2) according to the direction of main feature vector, a 2-D projection plane of adjacent points in the bounding sphere is constructed, and the crease points are extracted according to the constraint parameters model of distribution mechanism of adjacent points in the 2-D view. We evaluate the performance of the proposed method using objects of different sizes in real-world scenarios. Simultaneously, the extraction effect of contour feature points is compared with other methods, and the results show that the extraction and antinoise performance of the proposed method are superior to the other methods. Simultaneously, it is suitable not only for regular flat-shaped buildings but also for objects with irregular curvilinear architecture. Moreover, the proposed method involves only one parameter that needs to be tuned, and the parameter can be quickly obtained based on the distance resolution. LA - English DB - MTMT ER - TY - JOUR AU - Tang, Guijian AU - Jiang, Tingsong AU - Zhou, Weien AU - Li, Chao AU - Yao, Wen AU - Zhao, Yong TI - Adversarial patch attacks against aerial imagery object detectors JF - NEUROCOMPUTING J2 - NEUROCOMPUTING VL - 537 PY - 2023 SP - 128 EP - 140 PG - 13 SN - 0925-2312 DO - 10.1016/j.neucom.2023.03.050 UR - https://m2.mtmt.hu/api/publication/33899036 ID - 33899036 AB - Although Deep Neural Networks (DNNs)-based object detectors are widely used in various fields, espe-cially on aerial imagery object detections, it has been observed that a small elaborately designed patch attached to the images can mislead the DNNs-based detectors into producing erroneous output. However, the target detectors being attacked are quite simple, and the attack efficiency is relatively low in previous works, making it not practicable in real scenarios. To address these limitations, a new adversarial patch attack algorithm is proposed in this paper. Firstly, we designed a novel loss function using the intermediate outputs of the models rather than the model's final outputs interpreted by the detection head to optimize adversarial patches. The experiments conducted on the DOTA, RSOD, and NWPU VHR-10 datasets demonstrate that our method can significantly degrade the performance of the detectors. Secondly, we conducted intensive experiments to investigate the impact of different out-puts of the detection model on generating adversarial patches, demonstrating the class score is not as effective as the objectness score. Thirdly, we comprehensively analyzed the attack transferability across different aerial imagery datasets, verifying that the patches generated on one dataset are also effective in attacking another. Moreover, we proposed ensemble training to boost the attack's transferability across models. Our work alarms the application of DNNs-based object detectors in aerial imagery.(c) 2023 Elsevier B.V. All rights reserved. LA - English DB - MTMT ER - TY - JOUR AU - Xu, Bowen AU - Xu, Jiakun AU - Xue, Nan AU - Xia, Gui-Song TI - HiSup: Accurate polygonal mapping of buildings in satellite imagery with hierarchical supervision JF - ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING J2 - ISPRS J PHOTOGRAMM VL - 198 PY - 2023 SP - 284 EP - 296 PG - 13 SN - 0924-2716 DO - 10.1016/j.isprsjprs.2023.03.006 UR - https://m2.mtmt.hu/api/publication/33899038 ID - 33899038 AB - This paper studies the problem of the polygonal mapping of buildings by tackling the issue of mask reversibility, which leads to a notable performance gap between the predicted masks and polygons from the learning-based methods. We addressed such an issue by exploiting the hierarchical supervision (of bottom-level vertices, mid-level line segments, and high-level regional masks) and proposed a novel interaction mechanism of feature embedding sourced from different levels of supervision signals to obtain reversible building masks for polygonal mapping of buildings. As a result, we show that the learned reversible building masks take all the merits of the advances of deep convolutional neural networks for high-performing polygonal mapping of buildings. In the experiments, we evaluated our method on four public benchmarks, including the AICrowd, Open Cities, Shanghai, and Inria datasets. On the AICrowd, Open Cities, and Shanghai datasets, our proposed method obtains unanimous improvements on the metrics of AP, APboundary and PoLiS by large margins. For the Inria dataset, our proposed method also obtains very competitive results on the metrics of IoU and Accuracy. The models and source code are available at https://github.com/SarahwXU/HiSup. LA - English DB - MTMT ER - TY - JOUR AU - Xu, Lele AU - Li, Ye AU - Xu, Jinzhong AU - Zhang, Yue AU - Guo, Lili TI - BCTNet: Bi-Branch Cross-Fusion Transformer for Building Footprint Extraction JF - IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING J2 - IEEE T GEOSCI REMOTE VL - 61 PY - 2023 PG - 14 SN - 0196-2892 DO - 10.1109/TGRS.2023.3262967 UR - https://m2.mtmt.hu/api/publication/33899037 ID - 33899037 AB - Building footprint extraction in remote sensing remains challenging due to the diverse appearances of buildings and confusing scenarios. Recently, researchers have revealed that both the globality and locality are vitally important in building footprint extraction tasks and proposed to incorporate the local context and global long-range dependency in the segmentation models. However, inadequate integration of the globality and locality still leads to incomplete, fake, or missing extraction results. To alleviate these problems, a novel segmentation method named bi-branch cross-fusion transformer network (BCTNet) is proposed in this study. Two parallel branches of the convolutional encoder branch (CB) and the transformer encoder branch (TB) are designed to extract multiscale feature maps. A concatenation-then-cross-fusion transformer block (CCTB) is put forward to integrate the locality from the CB and globality from the TB in a cross-fusion way at each stage of the encoding process. Then, an adaptive gating module (AGM) is proposed to gate the feature maps from the CCTB to strengthen the important features while suppressing irrelevant interference information. After that, the segmentation results can be obtained through a simple decoding process. Comprehensive experiments on two benchmark datasets demonstrate that the proposed BCTNet can achieve superior performance compared with the current state-of-the-art (SOTA) segmentation methods. LA - English DB - MTMT ER - TY - JOUR AU - Yu, Dayu AU - Yue, Peng AU - Ye, Fan AU - Tapete, Deodato AU - Liang, Zheheng TI - Bidirectionally greedy framework for unsupervised 3D building extraction from airborne-based 3D meshes JF - AUTOMATION IN CONSTRUCTION J2 - AUTOMAT CONSTR VL - 152 PY - 2023 PG - 17 SN - 0926-5805 DO - 10.1016/j.autcon.2023.104917 UR - https://m2.mtmt.hu/api/publication/34288866 ID - 34288866 AB - Automatic building information extraction is an active research field in photogrammetry and remote sensing. However, most methods are proposed for supervised segmentation of point clouds or images, which can only capture limited building texture or geometric information, resulting in the obtained buildings being often fragmented. Therefore, we propose a bidirectionally greedy framework to extract spatial-continuous, geometry -complete, fine-textured 3D building models from large-scale 3D meshes captured by airborne in an unsupervised manner. The framework consists of two key steps in opposite directions, namely greedy culling and greedy re-covery. Greedy culling will maximize the removal of non-building primitives based on geometric and textural features. Greedy recovery is designed to maximize the detection of building primitives that are mistakenly removed by the greedy culling, by utilizing topological accessibility. The framework is assessed quantitatively and visually on five high-resolution datasets with different scenes. The results indicate the framework's effec-tiveness in accurately extracting fine-grained building models with complete geometry that can be visualized and analyzed for various 3D applications. LA - English DB - MTMT ER - TY - JOUR AU - dos Santos, Renato Cesar AU - Pessoa, Guilherme Gomes AU - Carrilho, Andre Caceres AU - Galo, Mauricio TI - Automatic Building Boundary Extraction From Airborne LiDAR Data Robust to Density Variation JF - IEEE GEOSCIENCE AND REMOTE SENSING LETTERS J2 - IEEE GEOSCI REMOTE S VL - 19 PY - 2022 PG - 5 SN - 1545-598X DO - 10.1109/LGRS.2020.3031397 UR - https://m2.mtmt.hu/api/publication/33237735 ID - 33237735 AB - The alpha-shape (alpha-shape) concept, which has its origin in computational geometry, is usually applied in building boundary extraction from airborne LiDAR data. However, the results depend on the appropriate choice of the parameter alpha. Despite several studies in the literature, the adaptive choice of the parameter alpha persists a challenge in boundary extraction, especially when abrupt density variations occur. To overcome this limitation, this letter proposes a new approach combining five estimation strategies. In the proposed method, these strategies are tested sequentially, prioritizing the one that provides greater level of details. The experiments were conducted considering buildings with different characteristics, which were selected from two LiDAR data sets with the average point densities of 12 points/m(2) and 4 points/m(2). The obtained results, presenting F-score. and PoLiS around 98% and 032 m, respectively, indicate the robustness of the proposed method even when abrupt density variation occurs. LA - English DB - MTMT ER - TY - JOUR AU - Khan, Sultan Daud AU - Alarabi, Louai AU - Basalamah, Saleh TI - An Encoder–Decoder Deep Learning Framework for Building Footprints Extraction from Aerial Imagery JF - ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING J2 - ARAB J SCI ENG VL - & PY - 2022 SP - & SN - 2193-567X DO - 10.1007/s13369-022-06768-8 UR - https://m2.mtmt.hu/api/publication/33095907 ID - 33095907 LA - English DB - MTMT ER - TY - JOUR AU - Li, Jiayi AU - Huang, Xin AU - Tu, Lilin AU - Zhang, Tao AU - Wang, Leiguang TI - A review of building detection from very high resolution optical remote sensing images JF - GISCIENCE AND REMOTE SENSING J2 - GISCI REMOTE SENS VL - 59 PY - 2022 IS - 1 SP - 1199 EP - 1225 PG - 27 SN - 1548-1603 DO - 10.1080/15481603.2022.2101727 UR - https://m2.mtmt.hu/api/publication/33088581 ID - 33088581 LA - English DB - MTMT ER - TY - CHAP AU - Wang, Qingyu AU - Feng, Guorui AU - Yin, Zhaoxia AU - Luo, Bin TI - Universal adversarial perturbation for remote sensing images T2 - 2022 IEEE 24TH INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING (MMSP) PB - IEEE CY - New York, New York SN - 9781665471893 T3 - IEEE International Workshop on Multimedia Signal Processing, ISSN 2163-3517 PY - 2022 PG - 6 DO - 10.1109/MMSP55362.2022.9948869 UR - https://m2.mtmt.hu/api/publication/33899039 ID - 33899039 AB - Recently, with the application of deep learning in the remote sensing image (RSI) field, the classification accuracy of the RSI has been dramatically improved compared with traditional technology. However, even the state-of-the-art object recognition convolutional neural networks are fooled by the universal adversarial perturbation (UAP). The research on UAP is mostly limited to ordinary images, and RSIs have not been studied. To explore the basic characteristics of UAPs of RSIs, this paper proposes a novel method combining an encoder-decoder network with an attention mechanism to generate the UAP of RSIs. Firstly, the former is used to generate the UAP, which can learn the distribution of perturbations better, and then the latter is used to find the sensitive regions concerned by the RSI classification model. Finally, the generated regions are used to fine-tune the perturbation making the model misclassified with fewer perturbations. The experimental results show that the UAP can make the classification model misclassify, and the attack success rate of our proposed method on the RSI data set is as high as 97.09%. LA - English DB - MTMT ER - TY - JOUR AU - Xiao, Xiao AU - Guo, Wenliang AU - Chen, Rui AU - Hui, Yilong AU - Wang, Jianing AU - Zhao, Hongyu TI - A Swin Transformer-Based Encoding Booster Integrated in U-Shaped Network for Building Extraction JF - REMOTE SENSING J2 - REMOTE SENS-BASEL VL - 14 PY - 2022 IS - 11 SP - 2611 SN - 2072-4292 DO - 10.3390/rs14112611 UR - https://m2.mtmt.hu/api/publication/33125029 ID - 33125029 AB - Building extraction is a popular topic in remote sensing image processing. Efficient building extraction algorithms can identify and segment building areas to provide informative data for downstream tasks. Currently, building extraction is mainly achieved by deep convolutional neural networks (CNNs) based on the U-shaped encoder–decoder architecture. However, the local perceptive field of the convolutional operation poses a challenge for CNNs to fully capture the semantic information of large buildings, especially in high-resolution remote sensing images. Considering the recent success of the Transformer in computer vision tasks, in this paper, first we propose a shifted-window (swin) Transformer-based encoding booster. The proposed encoding booster includes a swin Transformer pyramid containing patch merging layers for down-sampling, which enables our encoding booster to extract semantics from multi-level features at different scales. Most importantly, the receptive field is significantly expanded by the global self-attention mechanism of the swin Transformer, allowing the encoding booster to capture the large-scale semantic information effectively and transcend the limitations of CNNs. Furthermore, we integrate the encoding booster in a specially designed U-shaped network through a novel manner, named the Swin Transformer-based Encoding Booster- U-shaped Network (STEB-UNet), to achieve the feature-level fusion of local and large-scale semantics. Remarkably, compared with other Transformer-included networks, the computational complexity and memory requirement of the STEB-UNet are significantly reduced due to the swin design, making the network training much easier. Experimental results show that the STEB-UNet can effectively discriminate and extract buildings of different scales and demonstrate higher accuracy than the state-of-the-art networks on public datasets. LA - English DB - MTMT ER - TY - JOUR AU - dos Santos, Renato César AU - Galo, Mauricio AU - Carrilho, André Caceres AU - Pessoa, Guilherme Gomes TI - The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space JF - APPLIED GEOMATICS J2 - APPL GEOMAT VL - & PY - 2021 SP - & SN - 1866-9298 DO - 10.1007/s12518-021-00371-6 UR - https://m2.mtmt.hu/api/publication/32165073 ID - 32165073 N1 - Department of Cartography, São Paulo State University, Presidente Prudente/SP, Brazil Graduate Program on Cartographic Sciences (PPGCC), São Paulo State University, Presidente Prudente/SP, Brazil Export Date: 24 September 2021 Correspondence Address: dos Santos, R.C.; Department of Cartography, Brazil; email: renato.cesar@unesp.br LA - English DB - MTMT ER - TY - JOUR AU - Hammed, Hayder Makki AU - Hilal Almiahi, Osama Majeed AU - Shauchuk, Oksana TI - Detection of anthropogenic objects based on the spatial characteristics of their contour in aerial image JF - INDONESIAN JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE J2 - IJEECS VL - 23 PY - 2021 IS - 1 SP - 206 SN - 2502-4752 DO - 10.11591/ijeecs.v23.i1.pp206-215 UR - https://m2.mtmt.hu/api/publication/32164615 ID - 32164615 N1 - Export Date: 24 September 2021 Correspondence Address: Hammed, H.M.; University of BaghdadIraq; email: haidermakki300@yahoo.com LA - English DB - MTMT ER - TY - JOUR AU - Hao, Hanxiang AU - Baireddy, Sriram AU - Bartusiak, Emily AU - Gupta, Mridul AU - LaTourette, Kevin AU - Konz, Latisha AU - Chan, Moses AU - Comer, Mary L. AU - Delp, Edward J. TI - Building height estimation via satellite metadata and shadow instance detection JF - PROCEEDINGS OF SPIE - THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING J2 - PROCEEDINGS OF SPIE VL - 11729 PY - 2021 SN - 0277-786X DO - 10.1117/12.2585012 UR - https://m2.mtmt.hu/api/publication/32165083 ID - 32165083 LA - English DB - MTMT ER - TY - THES AU - Widyaningrum, E. TI - Automatic Object Extraction from Airborne Laser Scanning Point Clouds for Digital Base Map Production PY - 2021 UR - https://m2.mtmt.hu/api/publication/33125045 ID - 33125045 LA - English DB - MTMT ER - TY - JOUR AU - Cazzato, Dario AU - Cimarelli, Claudio AU - Sanchez-Lopez, Jose Luis AU - Voos, Holger AU - Leo, Marco TI - A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles JF - JOURNAL OF IMAGING J2 - J IMAGING VL - 6 PY - 2020 IS - 8 PG - 38 SN - 2313-433X DO - 10.3390/jimaging6080078 UR - https://m2.mtmt.hu/api/publication/31689293 ID - 31689293 AB - The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed. LA - English DB - MTMT ER - TY - JOUR AU - Chen, Li AU - Li, Haifeng AU - Zhu, Guowei AU - Li, Qi AU - Zhu, Jiawei AU - Huang, Haozhe AU - Peng, Jian AU - Zhao, Lin TI - Attack Selectivity of Adversarial Examples in Remote Sensing Image Scene Classification JF - IEEE ACCESS J2 - IEEE ACCESS VL - 8 PY - 2020 SP - 137477 EP - 137489 PG - 13 SN - 2169-3536 DO - 10.1109/ACCESS.2020.3011639 UR - https://m2.mtmt.hu/api/publication/31485920 ID - 31485920 AB - Remote sensing image (RSI) scene classification is the foundation and important technology of ground object detection, land use management and geographic analysis. During recent years, convolutional neural networks (CNNs) have achieved significant success and are widely applied in RSI scene classification. However, crafted images that serve as adversarial examples can potentially fool CNNs with high confidence and are hard for human eyes to interpret. For the increasing security and robust requirements of RSI scene classification, the adversarial example problem poses a serious problem for the classification results derived from systems using CNN models, which has not been fully recognized by previous research. In this study, to explore the properties of adversarial examples of RSI scene classification, we create different scenarios by testing two major attack algorithms (i.e., the fast gradient sign method (FGSM) and basic iterative method (BIM)) trained on different RSI benchmark datasets to fool CNNs (i.e., InceptionV1, ResNet and a simple CNN). In the experiment, our results show that CNNs of RSI scene classification are also vulnerable to adversarial examples, and some of them have a fooling rate of over 80%. These adversarial examples are affected by the architecture of CNNs and the type of RSI dataset. InceptionV1 has a fooling rate of less than 5%, which is lower than the others. Adversarial examples generated on the UCM dataset are easier than other datasets. Importantly, we also find that the classes of adversarial examples have an attack selectivity property. Misclassifications of adversarial examples of RSIs are related to the similarity of the original classes in the CNN feature space. Attack selectivity reveals potential classes of adversarial examples and provides insights into the design of defensive algorithms in future research. LA - English DB - MTMT ER - TY - CHAP AU - dos Santos, R. C. AU - Galo, M. AU - Carrilho, A. C. AU - Pessoa, G. G. AU - de Oliveira, R. A. R. TI - Automatic Building Change Detection Using Multi-Temporal Airborne Lidar Data T2 - 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) SN - 9781728143507 PY - 2020 SP - 54 EP - 59 PG - 6 DO - 10.1109/LAGIRS48042.2020.9165628 UR - https://m2.mtmt.hu/api/publication/31965034 ID - 31965034 N1 - São Paulo State University - UNESP, Graduate Program in Cartographic Sciences, Presidente Prudente, São Paulo, Brazil São Paulo State University - UNESP, Dept. of Cartography, Presidente Prudente, São Paulo, Brazil Cited By :1 Export Date: 11 May 2021 São Paulo State University - UNESP, Graduate Program in Cartographic Sciences, Presidente Prudente, São Paulo, Brazil São Paulo State University - UNESP, Dept. of Cartography, Presidente Prudente, São Paulo, Brazil Cited By :3 Export Date: 24 September 2021 Correspondence Address: Dos Santos, R.C.; São Paulo State University - UNESP, Brazil; email: renato.cesar@unesp.br LA - English DB - MTMT ER - TY - JOUR AU - dos Santos, Renato Cesar AU - Galo, Mauricio AU - Habib, Ayman F. TI - Regularization of Building Roof Boundaries from Airborne LiDAR Data Using an Iterative CD-Spline JF - REMOTE SENSING J2 - REMOTE SENS-BASEL VL - 12 PY - 2020 IS - 12 PG - 21 SN - 2072-4292 DO - 10.3390/rs12121904 UR - https://m2.mtmt.hu/api/publication/31485923 ID - 31485923 AB - Building boundaries play an essential role in many applications such as urban planning and production of 3D realistic views. In this context, airborne LiDAR data have been explored for the generation of digital building models. Despite the many developed strategies, there is no method capable of encompassing all the complexities in an urban environment. In general, the vast majority of existing regularization methods are based on building boundaries that are made up of straight lines. Therefore, the development of a strategy able to model building boundaries, regardless of their degree of complexity is of high importance. To overcome the limitations of existing strategies, an iterative CD-spline (changeable degree spline) regularization method is proposed. The main contribution is the automated selection of the polynomial function that best models each segment of the building roof boundaries. Conducted experiments with real data verified the ability of the proposed approach in modeling boundaries with different levels of complexities, including buildings composed of complex curved segments and point cloud with different densities, presentingF(score)andPoLiSaround 95% and 0.30 m, respectively. LA - English DB - MTMT ER - TY - JOUR AU - Li, Junjun AU - Cao, Jiannong AU - Zhu, Yingying AU - Feyissa, Muleta Ebissa AU - Chen, Beibei TI - Superpixel-based imaging for residential area detection of high spatial resolution remote sensing imagery JF - JOURNAL OF APPLIED REMOTE SENSING J2 - J APPL REMOTE SENS VL - 14 PY - 2020 IS - 2 PG - 17 SN - 1931-3195 DO - 10.1117/1.JRS.14.026507 UR - https://m2.mtmt.hu/api/publication/31485917 ID - 31485917 AB - The precise and efficient location of residential areas using high spatial resolution remote sensing imagery is a popular research area in the field of Earth observation. Most of the existing approaches are supervised or semisupervised and use data training. Among the unsupervised approaches, corner density-based mapping using kernel density estimate has been widely employed to predict the presence of built-up areas. However, it is computationally time-consuming and the statistical threshold segmentation makes it difficult to obtain a stable and accurate output. To overcome this deficiency, a new two-stage object-oriented residential area extraction scheme was designed. First, a set of corners was extracted using the Gabor filter bank with structural tensor analysis to indicate candidate buildings. Then, instead of pixel units, our method takes superpixel-based image partitions as the primary calculation elements, and an object-oriented weighted sparse spatial voting technique was proposed to accelerate the generation of a residential area presence index. It was demonstrated that the superpixel-based voting strategy was not only efficient in accelerating the calculation process, but it also reduced the false negative rate in the final detection result. Second, a graph-cut method was employed to address the residential area segmentation by integrating a density map as a prior cue that preserves the boundary accuracy better than traditional statistical threshold methods. The effectiveness of the proposed method was evaluated using a series of experiments on the sets of high-resolution Google Earth, IKONOS, and GaoFen-2 (GF2) satellite imagery. The results showed that the proposed approach outperforms the existing algorithms in terms of computational speed and accuracy. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE) LA - English DB - MTMT ER - TY - JOUR AU - Widyaningrum, Elyta AU - Peters, Ravi Y. AU - Lindenbergh, Roderik C. TI - Building outline extraction from ALS point clouds using medial axis transform descriptors JF - PATTERN RECOGNITION J2 - PATTERN RECOGN VL - 106 PY - 2020 PG - 15 SN - 0031-3203 DO - 10.1016/j.patcog.2020.107447 UR - https://m2.mtmt.hu/api/publication/31485921 ID - 31485921 N1 - Cited By :1 Export Date: 24 September 2021 CODEN: PTNRA Correspondence Address: Widyaningrum, E.; Geoscience and Remote Sensing, Stevinweg 1, 2628CN Delft, Netherlands; email: e.widyaningrum@tudelft.nl AB - Automatic building extraction and delineation from airborne LiDAR point cloud data of urban environments is still a challenging task due to the variety and complexity at which buildings appear. The Medial Axis Transform (MAT) is able to describe the geometric shape and topology of an object, but has never been applied for building roof outline extraction. It represents the shape of an object by its centerline, or skeleton structure instead of its boundary. Notably, end points of the MAT in principle coincide with corner points of building outlines. However, the MAT is sensitive to small boundary irregularities, which makes shape detection in airborne point clouds challenging. We propose a robust MAT-based method for detecting building corner points, which are then connected to form a building boundary polygon. First, we approximate the 2D MAT of a set of building edge points acquired by the alpha-shape algorithm to derive a so-called building roof skeleton. We then propose a hierarchical corner-aware segmentation to cluster skeleton points based on their properties which are the so-called separation angle, radius of the maximally inscribe circle, and defining edge point indices. From each segment, a corner point is then estimated by extrapolating the position of the zero radius inscribed circle based on the skeleton point positions within the segment. Our experiment uses point cloud datasets of Makassar, Indonesia and EYE-Amsterdam, The Netherlands. The average positional accuracy of the building outline results for Makassar and EYE-Amsterdam is 65 cm and 70 cm, respectively, which meet one-meter base map accuracy criteria. The results imply that skeletonization is a promising tool to extract relevant geometric information on e.g. building outlines even from far from perfect geographical point cloud data. (C) 2020 The Author(s). Published by Elsevier Ltd. LA - English DB - MTMT ER - TY - JOUR AU - Zhang, K. AU - Chen, H. AU - Xiao, W. AU - Sheng, Y. AU - Su, D. AU - Wan, P. TI - Building extraction from high-resolution remote sensing images based on Grabcut with automatic selection of foreground and background samples JF - PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING J2 - PHOTOGRAMM ENG REM S VL - 86 PY - 2020 IS - 4 SP - 235 EP - 245 PG - 11 SN - 0099-1112 DO - 10.14358/PERS.86.4.235 UR - https://m2.mtmt.hu/api/publication/32239310 ID - 32239310 N1 - Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, China School of Geography, Nanjing Normal University, Nanjing, China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, China State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), China Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, China School of Geography, Nanjing Normal University, Nanjing, China School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom Cited By :2 Export Date: 24 September 2021 CODEN: PERSD Correspondence Address: Zhang, K.; Key Laboratory of Virtual Geographic Environment, China; email: zhangka81@126.com LA - English DB - MTMT ER - TY - JOUR AU - Carrilho, André Caceres AU - Dos Santos, Renato César AU - Pessoa, Guilherme Gomes AU - Galo, Mauricio TI - Extração Automática de Contornos de Edificações a partir de Dados LiDAR Aerotransportado JF - REVISTA BRASILEIRA DE CARTOGRAFIA / BRAZILIAN JOURNAL OF CARTOGRAPHY J2 - REVISTA BRASILEIRA DE CARTOGRAFIA / BRAZIL J CARTOGRAPHY VL - 71 PY - 2019 IS - 3 SP - 832 EP - 855 PG - 24 SN - 0560-4613 DO - 10.14393/rbcv71n3-46515 UR - https://m2.mtmt.hu/api/publication/31208256 ID - 31208256 LA - English DB - MTMT ER - TY - JOUR AU - Hao, Lechuan AU - Zhang, Ye AU - Cao, Zhimin TI - Active Cues Collection and Integration for Building Extraction With High-Resolution Color Remote Sensing Imagery JF - IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING J2 - IEEE J-STARS VL - 12 PY - 2019 IS - 8 SP - 2675 EP - 2694 PG - 20 SN - 1939-1404 DO - 10.1109/JSTARS.2019.2926738 UR - https://m2.mtmt.hu/api/publication/30905896 ID - 30905896 N1 - Cited By :5 Export Date: 24 September 2021 Correspondence Address: Cao, Z.; Department of Information Engineering, China; email: dahai0464@sina.com AB - Building extraction from high-resolution color remote sensing imagery (HRCRSI) is important in city planning, building reconstruction, and other applications. However, the performance of the state-of-the-art methods is often passively dependent on the accuracy and reliability of the initial edges/regions acquired by general edge/region extraction methods. But the performance of these general methods is always sensitive to unavoidable noise and interferences, especially for the HRCRSI imagery. Furthermore, structural information of the target (e.g., buildings herein) is not fully utilized in these general methods, which is undoubtedly a useful clue to reducing the effects of noise and interference. Therefore, undesired results are inevitable for building extraction methods conducted with a passive or semiactive manner. In this paper, in order to alleviate this problem to a certain extent, we carried out the building extraction task in a completely active manner: 1) under the guidance of the visual perception theory, cues of building edges and regions are actively collected by considering building priors related to main direction and color; and 2) based on knowledge about building shape widely accepted in the literature, cues of the obtained building edges and regions are actively integrated for final building extraction. Experimental results on three benchmark datasets, including aerial and high-resolution optical satellite images, illustrate that the proposed active method can achieve the expected building extraction results. LA - English DB - MTMT ER - TY - JOUR AU - Manno-Kovács, Andrea TI - Direction Selective Contour Detection for Salient Objects JF - IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY J2 - IEEE T CIRC SYST VID VL - 29 PY - 2019 IS - 2 SP - 375 EP - 389 PG - 15 SN - 1051-8215 DO - 10.1109/TCSVT.2018.2804438 UR - https://m2.mtmt.hu/api/publication/3419087 ID - 3419087 LA - English DB - MTMT ER - TY - JOUR AU - Manno-Kovács, Andrea AU - Giusti, E AU - Berizzi, F AU - Kovács, Levente Attila TI - Image Based Robust Target Classification for Passive ISAR JF - IEEE SENSORS JOURNAL J2 - IEEE SENS J VL - 19 PY - 2019 IS - 1 SP - 268 EP - 276 PG - 9 SN - 1530-437X DO - 10.1109/JSEN.2018.2876911 UR - https://m2.mtmt.hu/api/publication/30343501 ID - 30343501 LA - English DB - MTMT ER - TY - JOUR AU - Ting, C. AU - Wenbin, W. AU - Jianjun, H. AU - Yuexia, Q. AU - Feng, U. AU - Qiang, W. TI - Urban human settlements monitoring model and its application based on multi- source spatial data fusion JF - SHENGTAI XUEBAO / ACTA ECOLOGICA SINICA J2 - ACTA ECOL SINICA VL - 39 PY - 2019 IS - 4 SP - 1300 EP - 1308 PG - 9 SN - 1000-0933 DO - 10.5846/stxb201809111948 UR - https://m2.mtmt.hu/api/publication/32009443 ID - 32009443 N1 - Cited By :1 Export Date: 11 May 2021 Cited By :1 Export Date: 24 September 2021 Correspondence Address: Qiang, W.; Turnty First Century Aerospace TechnologyChina; email: wenqiang@21at.com.cn LA - English DB - MTMT ER - TY - CHAP AU - Wang, Shuang AU - Zhou, Ligang AU - He, Pei AU - Quan, Dou AU - Zhao, Qing AU - Liang, Xuefeng AU - Hou, Biao ED - Takuya, Sakamoto ED - Junichi, Susaki TI - An Improved Fully Convolutional Network for Learning Rich Building Features T2 - 2019 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2019 PB - IEEE CY - Yokohama SN - 9781538691540 PY - 2019 SP - 6444 EP - 6447 PG - 4 DO - 10.1109/IGARSS.2019.8898460 UR - https://m2.mtmt.hu/api/publication/31576979 ID - 31576979 AB - Many efficient approaches are proposed to detect building in remote sensing images. In this paper, in order to learning rich building features better, we propose a full convolutional network with dense connection. There contributions are made: 1) To strengthen feature propagation, an improved dense network is introduced to the full convolution network. 2) We have designed top-down short connections to facilitate the fusion of high and low feature information. 3) In addition, we add the weighted cross entropy edge loss function to make the network pay more attention to building edge in detail. Experiments show that the proposed method achieves excellent performance on the remote sensing image data taken by the QuickBird satellite. LA - English DB - MTMT ER - TY - JOUR AU - Yousefiyan, Farzaneh AU - Ebadi, Hamid AU - Sedaghat, Amin TI - Integrated Local Features to Detect Building Locations in High-Resolution Satellite Imagery JF - PHOTONIRVACHAK / JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING J2 - PHOTONIRVACHAK-J IND VL - 47 PY - 2019 IS - 8 SP - 1375 EP - 1389 PG - 15 SN - 0255-660X DO - 10.1007/s12524-019-01001-w UR - https://m2.mtmt.hu/api/publication/30799345 ID - 30799345 LA - English DB - MTMT ER - TY - JOUR AU - DU, Shouji AU - ZOU, Zhengrong AU - ZHANG, Yunsheng AU - HE, Xue AU - WANG, Jingxue TI - A Building Extraction Method via Graph Cuts Algorithm by Fusion of LiDAR Point Cloud and Orthoimage JF - ACTA GEODAETICA ET CARTOGRAPHICA SINICA J2 - ACTA GEODAET CARTOGR SINICA VL - 47 PY - 2018 IS - 4 SP - 519 EP - 527 PG - 9 SN - 1001-1595 DO - 10.11947/j.AGCS.2018.20160534 UR - https://m2.mtmt.hu/api/publication/27480805 ID - 27480805 N1 - School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China School of Geomatics, Liaoning Technical University, Fuxin, 123000, China Cited By :2 Export Date: 24 September 2021 CODEN: CEXUE Correspondence Address: Zhang, Y.; School of Geosciences and Info-Physics, China; email: zhangys@csu.edu.cn LA - Chinese DB - MTMT ER - TY - JOUR AU - Hu, Wu-Chih AU - Chen, Chao-Ho AU - Chen, Tsong-Yi AU - Peng, Min-Yang AU - Su, Yi-Jen TI - Real-time video stabilization for fast-moving vehicle cameras JF - MULTIMEDIA TOOLS AND APPLICATIONS: AN INTERNATIONAL JOURNAL J2 - MULTIMED TOOLS APPL VL - 77 PY - 2018 IS - 1 SP - 1237 EP - 1260 PG - 24 SN - 1380-7501 DO - 10.1007/s11042-016-4291-4 UR - https://m2.mtmt.hu/api/publication/27270945 ID - 27270945 LA - English DB - MTMT ER - TY - CHAP AU - Manno-Kovács, Andrea AU - Giusti, E AU - Berizzi, F AU - Kovács, Levente Attila ED - IEEE, null TI - Automatic Target Classification in Passive ISAR Range-Crossrange Images T2 - 2018 IEEE Radar Conference (Radarconf’18) PB - IEEE CY - Oklahoma City (OK) SN - 9781538641668 PY - 2018 SP - 206 EP - 211 PG - 6 DO - 10.1109/RADAR.2018.8378558 UR - https://m2.mtmt.hu/api/publication/3368420 ID - 3368420 N1 - A4 ISSN:1097-5764 AB - This paper presents a method for automatic analysis of passive radar 2D ISAR images to evaluate the possibilities and capabilities of image feature based target extraction and classification. The goal is to extend signal processing based detection and recognition methods with image information. The presented method is fast, easily embeddable and extendable, works near real-time, and we show its viability for classification using real passive 2D ISAR images. LA - English DB - MTMT ER - TY - JOUR AU - Shu, Z. AU - Hu, X. AU - Sun, J. TI - Center-Point-Guided Proposal Generation for Detection of Small and Dense Buildings in Aerial Imagery JF - IEEE GEOSCIENCE AND REMOTE SENSING LETTERS J2 - IEEE GEOSCI REMOTE S VL - 15 PY - 2018 IS - 7 SP - 1100 EP - 1104 PG - 5 SN - 1545-598X DO - 10.1109/LGRS.2018.2822760 UR - https://m2.mtmt.hu/api/publication/32009447 ID - 32009447 N1 - School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China Cited By :9 Export Date: 11 May 2021 School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China Cited By :11 Export Date: 24 September 2021 Correspondence Address: Hu, X.; School of Remote Sensing and Information Engineering, China; email: huxy@whu.edu.cn LA - English DB - MTMT ER - TY - JOUR AU - Xu, S AU - Pan, X AU - Li, E AU - Wu, B AU - Bu, S AU - Dong, W AU - Xiang, S AU - Zhang, X TI - Automatic Building Rooftop Extraction From Aerial Images via Hierarchical RGB-D Priors. JF - IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING J2 - IEEE T GEOSCI REMOTE VL - 56 PY - 2018 IS - 12 SP - 7369 EP - 7387 PG - 19 SN - 0196-2892 DO - 10.1109/TGRS.2018.2850972 UR - https://m2.mtmt.hu/api/publication/27686231 ID - 27686231 LA - English DB - MTMT ER - TY - JOUR AU - Yousefiyan, Farzane AU - Ebadi, Hamin AU - Sedaghat, Amin TI - Shadow extraction of building using fusion of edge and point feature orientation from high resolution satellite imagery JF - Journal of Geospatial Information Technology J2 - jgit VL - 6 PY - 2018 IS - 3 SP - 123 EP - 145 PG - 23 SN - 2008-9635 DO - 10.29252/jgit.6.3.123 UR - https://m2.mtmt.hu/api/publication/31965052 ID - 31965052 LA - English DB - MTMT ER - TY - JOUR AU - Benedek, Csaba TI - An Embedded Marked Point Process Framework for Three-Level Object Population Analysis JF - IEEE TRANSACTIONS ON IMAGE PROCESSING J2 - IEEE T IMAGE PROCESS VL - 26 PY - 2017 IS - 9 SP - 4430 EP - 4445 PG - 16 SN - 1057-7149 DO - 10.1109/TIP.2017.2716181 UR - https://m2.mtmt.hu/api/publication/3239014 ID - 3239014 LA - English DB - MTMT ER - TY - JOUR AU - Du, S AU - Zhang, Y AU - Zou, Z AU - Xu, S AU - He, X AU - Chen, S TI - Automatic building extraction from LiDAR data fusion of point and grid-based features JF - ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING J2 - ISPRS J PHOTOGRAMM VL - 130 PY - 2017 SP - 294 EP - 307 PG - 14 SN - 0924-2716 DO - 10.1016/j.isprsjprs.2017.06.005 UR - https://m2.mtmt.hu/api/publication/26671635 ID - 26671635 LA - English DB - MTMT ER - TY - JOUR AU - Kakooei, M. AU - Baleghi, Y. TI - Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment JF - INTERNATIONAL JOURNAL OF REMOTE SENSING J2 - INT J REMOTE SENS VL - 38 PY - 2017 IS - 8-10 SP - 2511 EP - 2534 PG - 24 SN - 0143-1161 DO - 10.1080/01431161.2017.1294780 UR - https://m2.mtmt.hu/api/publication/32009454 ID - 32009454 N1 - Cited By :36 Export Date: 11 May 2021 Cited By :41 Export Date: 24 September 2021 CODEN: IJSED Correspondence Address: Baleghi, Y.; Electrical & Computer Engineering Department, Iran; email: y.baleghi@nit.ac.ir LA - English DB - MTMT ER - TY - JOUR AU - Konukcu, B.E. AU - Karaman, H. AU - Şahin, M. TI - Determination of building age for Istanbul buildings to be used for the earthquake damage analysis according to structural codes by using aerial and satellite images in GIS JF - NATURAL HAZARDS J2 - NAT HAZARDS VL - 85 PY - 2017 IS - 3 SP - 1811 EP - 1834 PG - 24 SN - 0921-030X DO - 10.1007/s11069-016-2666-5 UR - https://m2.mtmt.hu/api/publication/32009459 ID - 32009459 N1 - Department of Geomatics Engineering, Faculty of Civil Engineering, Istanbul Technical University, Istanbul, 34469, Turkey Rectorate, MEF University, Istanbul, 34396, Turkey Cited By :1 Export Date: 11 May 2021 Department of Geomatics Engineering, Faculty of Civil Engineering, Istanbul Technical University, Istanbul, 34469, Turkey Rectorate, MEF University, Istanbul, 34396, Turkey Cited By :3 Export Date: 24 September 2021 Correspondence Address: Karaman, H.; Department of Geomatics Engineering, Turkey; email: karamanhi@itu.edu.tr LA - English DB - MTMT ER - TY - JOUR AU - Li, E AU - Xu, S AU - Meng, W AU - Zhang, X TI - Building Extraction from Remotely Sensed Images by Integrating Saliency Cue JF - IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING J2 - IEEE J-STARS VL - 10 PY - 2017 IS - 3 SP - 906 EP - 919 PG - 14 SN - 1939-1404 DO - 10.1109/JSTARS.2016.2603184 UR - https://m2.mtmt.hu/api/publication/26260071 ID - 26260071 N1 - N1 Funding details: NSFC, National Natural Science Foundation of China N1 Funding details: 61331018, NSFC, National Natural Science Foundation of China LA - English DB - MTMT ER - TY - CONF AU - Manno-Kovács, Andrea AU - Kovács, Levente Attila ED - CVF, null TI - Lightweight Monocular Obstacle Avoidance by Salient Feature Fusion T2 - ICCV 2017: International Conference on Computer Vision PB - Computer Vision Foundation C1 - Venice PY - 2017 SP - 734 EP - 741 PG - 8 DO - 10.1109/ICCVW.2017.92 UR - https://m2.mtmt.hu/api/publication/3287277 ID - 3287277 N1 - WoS:hiba:000425239600084 2019-03-03 11:11 típus nem egyezik LA - English DB - MTMT ER - TY - JOUR AU - Ngo, T-T AU - Mazet, V AU - Collet, C AU - De Fraipont, P TI - Shape-Based Building Detection in Visible Band Images Using Shadow Information JF - IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING J2 - IEEE J-STARS VL - 10 PY - 2017 IS - 3 SP - 920 EP - 932 PG - 13 SN - 1939-1404 DO - 10.1109/JSTARS.2016.2598856 UR - https://m2.mtmt.hu/api/publication/26260070 ID - 26260070 LA - English DB - MTMT ER - TY - JOUR AU - Prince, D AU - Sidike, P AU - Essa, A AU - Asari, V TI - Multifeature fusion for automatic building change detection in wide-area imagery JF - JOURNAL OF APPLIED REMOTE SENSING J2 - J APPL REMOTE SENS VL - 11 PY - 2017 IS - 2 PG - 21 SN - 1931-3195 DO - 10.1117/1.JRS.11.026040 UR - https://m2.mtmt.hu/api/publication/27134908 ID - 27134908 N1 - University of Dayton, Department of Electrical and Computer Engineering, Dayton, OH, United States Saint Louis University, Center for Sustainability, St. Louis, MO, United States Cited By :3 Export Date: 24 September 2021 CODEN: JARSC Correspondence Address: Essa, A.; University of Dayton, United States; email: essaa1@udayton.edu LA - English DB - MTMT ER - TY - JOUR AU - Shen, L. AU - Yang, F. TI - Satellite image-based ancient dwelling fingerprint detection algorithm JF - PATTERN RECOGNITION AND IMAGE ANALYSIS J2 - PATT RECOG IMAGE ANAL VL - 27 PY - 2017 IS - 3 SP - 610 EP - 617 PG - 8 SN - 1054-6618 DO - 10.1134/S1054661817030282 UR - https://m2.mtmt.hu/api/publication/32009453 ID - 32009453 N1 - Cited By :1 Export Date: 11 May 2021 Cited By :1 Export Date: 24 September 2021 Correspondence Address: Shen, L.; School of Information Engineering, China; email: slx965@163.com LA - English DB - MTMT ER - TY - JOUR AU - Sheppard, Clay AU - Rahnemoonfar, Maryam TI - Real-time Scene Understanding for UAV Imagery based on Deep Convolutional Neural Networks JF - IEEE INTERNATIONAL SYMPOSIUM ON GEOSCIENCE AND REMOTE SENSING IGARSS J2 - IEEE INT SYMP GEOSCI REMOTE SENS IGARSS VL - 2017 PY - 2017 SP - 2243 EP - 2246 PG - 4 SN - 2153-6996 DO - 10.1109/IGARSS.2017.8127435 UR - https://m2.mtmt.hu/api/publication/27271308 ID - 27271308 LA - English DB - MTMT ER - TY - JOUR AU - Zheng, Yuanfan AU - Weng, Qihao AU - Zheng, Yaoxing TI - A Hybrid Approach for Three-Dimensional Building Reconstruction in Indianapolis from LiDAR Data JF - REMOTE SENSING J2 - REMOTE SENS-BASEL VL - 9 PY - 2017 IS - 4 SP - 310 SN - 2072-4292 DO - 10.3390/rs9040310 UR - https://m2.mtmt.hu/api/publication/26500623 ID - 26500623 N1 - Center for Urban and Environmental Change, Indiana State University, Terre Haute, IN 47802-1902, United States College of Tourism, Fujian Normal University, Fujian, 350000, China Cited By :25 Export Date: 24 September 2021 Correspondence Address: Weng, Q.; Center for Urban and Environmental Change, United States; email: qweng@indstate.edu LA - English DB - MTMT ER - TY - JOUR AU - Liasis, Gregoris AU - Stavrou, Stavros TI - Satellite images analysis for shadow detection and building height estimation JF - ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING J2 - ISPRS J PHOTOGRAMM VL - 119 PY - 2016 SP - 437 EP - 450 PG - 14 SN - 0924-2716 DO - 10.1016/j.isprsjprs.2016.07.006 UR - https://m2.mtmt.hu/api/publication/26231637 ID - 26231637 LA - English DB - MTMT ER - TY - JOUR AU - Peeters, Aviva TI - A GIS-based method for modeling urban-climate parameters using automated recognition of shadows cast by buildings JF - COMPUTERS ENVIRONMENT AND URBAN SYSTEMS J2 - COMPUT ENVIRON URBAN VL - 59 PY - 2016 IS - 9 SP - 107 EP - 115 PG - 9 SN - 0198-9715 DO - 10.1016/j.compenvurbsys.2016.05.006 UR - https://m2.mtmt.hu/api/publication/25962911 ID - 25962911 N1 - Cited By :9 Export Date: 24 September 2021 CODEN: CEUSD Correspondence Address: Peeters, A.; Desert Architecture and Urban planning Unit, Sede Boqer Campus, Israel; email: apeeters@bgu.ac.il LA - English DB - MTMT ER -