TY - JOUR AU - Singh, Simrandeep AU - Singh, Harbinder AU - Bueno, Gloria AU - Deniz, Oscar AU - Singh, Sartajvir AU - Monga, Himanshu AU - Hrisheekesha, P. N. AU - Pedraza, Anibal TI - A review of image fusion: Methods, applications and performance metrics JF - DIGITAL SIGNAL PROCESSING J2 - DIGIT SIGNAL PROCESS VL - 137 PY - 2023 PG - 31 SN - 1051-2004 DO - 10.1016/j.dsp.2023.104020 UR - https://m2.mtmt.hu/api/publication/33887219 ID - 33887219 AB - The same sensor or a number of image sensors are used to take a series of photographs in order to gather as much data as possible about the scene. Several imaging techniques are used to retrieve entire information from the source under observation. Image fusion (IF) is used to create a new image that incorporates comprehensive information from many photographs. The various images may be captured from different viewpoints, different imaging sensors i.e., visible (VIS) and IR camera, different modalities i.e., computed tomography (CT) and magnetic resonance image (MRI), hyper spectral images i.e., panchromatic and multi-spectral satellite images, multi-exposure images and multi-focus images. Owing to the growing mandates and development of image enhancement schemes, numerous fusion methods were recently formulated. Consequentially, we are doing a survey study to document the methodological development in IF techniques. The outline of picture merging technologies is described in this article. Ultimately, latest state-of-the-art fusion techniques are also demonstrated. Readers will gain insights on current discoveries and their implications for the future through a review of diverse image fusion in various areas and fusion quality metrics.(c) 2023 Elsevier Inc. All rights reserved. LA - English DB - MTMT ER - TY - JOUR AU - Yu, Zhimin AU - Wan, Fang AU - Lei, Guangbo AU - Xiong, Ying AU - Xu, Li AU - Ye, Zhiwei AU - Liu, Wei AU - Zhou, Wen AU - Xu, Chengzhi TI - RSLC-Deeplab: A Ground Object Classification Method for High-Resolution Remote Sensing Images JF - ELECTRONICS (SWITZ) VL - 12 PY - 2023 IS - 17 PG - 16 SN - 2079-9292 DO - 10.3390/electronics12173653 UR - https://m2.mtmt.hu/api/publication/34273683 ID - 34273683 AB - With the continuous advancement of remote sensing technology, the semantic segmentation of different ground objects in remote sensing images has become an active research topic. For complex and diverse remote sensing imagery, deep learning methods have the ability to automatically discern features from image data and capture intricate spatial dependencies, thus outperforming traditional image segmentation methods. To address the problems of low segmentation accuracy in remote sensing image semantic segmentation, this paper proposes a new remote sensing image semantic segmentation network, RSLC-Deeplab, based on DeeplabV3+. Firstly, ResNet-50 is used as the backbone feature extraction network, which can extract deep semantic information more effectively and improve the segmentation accuracy. Secondly, the coordinate attention (CA) mechanism is introduced into the model to improve the feature representation generated by the network by embedding position information into the channel attention mechanism, effectively capturing the relationship between position information and channels. Finally, a multi-level feature fusion (MFF) module based on asymmetric convolution is proposed, which captures and refines low-level spatial features using asymmetric convolution and then fuses them with high-level abstract features to mitigate the influence of background noise and restore the lost detailed information in deep features. The experimental results on the WHDLD dataset show that the mean intersection over union (mIoU) of RSLC-Deeplab reached 72.63%, the pixel accuracy (PA) reached 83.49%, and the mean pixel accuracy (mPA) reached 83.72%. Compared to the original DeeplabV3+, the proposed method achieved a 4.13% improvement in mIoU and outperformed the PSP-NET, U-NET, MACU-NET, and DeeplabV3+ networks. LA - English DB - MTMT ER - TY - GEN AU - Bangyu, Li TI - Farm Parcel Extraction in High Resolution Remote Sensing Image Based on Hierarchical Spectrum and Shape Features PY - 2022 SP - & UR - https://m2.mtmt.hu/api/publication/32829323 ID - 32829323 LA - English DB - MTMT ER - TY - JOUR AU - Gu, Jianyu AU - Congalton, Russell G. TI - Individual Tree Crown Delineation From UAS Imagery Based on Region Growing by Over-Segments With a Competitive Mechanism JF - IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING J2 - IEEE T GEOSCI REMOTE VL - 60 PY - 2022 SP - 1 EP - 11 PG - 11 SN - 0196-2892 DO - 10.1109/TGRS.2021.3074289 UR - https://m2.mtmt.hu/api/publication/32954865 ID - 32954865 AB - Unmanned aerial systems (UAS) have become a flexible and low-cost platform to supplement aerial or satellite remote sensing for precision forestry. The data derived from UAS are widely used to measure variables at a single tree level where individual tree crowns become fundamental. Most research has adapted some region growing method for individual tree crown delineation (ITCD). However, pixels are often used as growing units without considering the spatial and contextual information, which can be adversely affected by noise (e.g., background or branches) in the imagery. Instead, over-segments can compensate for these pixels' shortcomings while also partially detecting the edge of a tree crown. These over-segments then become the growing units used in this study. In addition, this research incorporated competition among the over-segments to alleviate the deficits of sequential ordering. The algorithm was evaluated in three study sites with distinctive forest patterns utilizing natural color imagery. Results demonstrated that using over-segments as growing units improved the ITCD accuracy by 1.8%-2.3%, whereas incorporating the competitive mechanism further increased the accuracy by 4.3%-9.3%. The spatial arrangement of trees also affected the segmentation accuracy. The sources of uncertainties, such as the manually interpreted treetops and feature selection for region growing, were also analyzed. The algorithm developed in this research can be easily extended to other data sources to achieve promising accuracy. LA - English DB - MTMT ER - TY - JOUR AU - Javidan, Raana AU - Javidan, Narges TI - A novel artificial intelligence-based approach for mapping groundwater nitrate pollution in the Andimeshk-Dezful plain, Iran JF - GEOCARTO INTERNATIONAL J2 - GEOCAR INT VL - & PY - 2022 SP - 1 EP - 25 PG - 25 SN - 1010-6049 DO - 10.1080/10106049.2022.2035830 UR - https://m2.mtmt.hu/api/publication/32893768 ID - 32893768 LA - English DB - MTMT ER - TY - JOUR AU - Liu, Chang AU - Szirányi, Tamás TI - Road Condition Detection and Emergency Rescue Recognition Using On-Board UAV in the Wildness JF - REMOTE SENSING J2 - REMOTE SENS-BASEL VL - 14 PY - 2022 IS - 17 PG - 27 SN - 2072-4292 DO - 10.3390/rs14174355 UR - https://m2.mtmt.hu/api/publication/33086093 ID - 33086093 N1 - Export Date: 27 September 2022 Correspondence Address: Szirányi, T.; Department of Networked Systems and Services, Műegyetem rkp. 3, Hungary; email: sziranyi.tamas@sztaki.hu AB - Unmanned aerial vehicle (UAV) vision technology is becoming increasingly important, especially in wilderness rescue. For humans in the wilderness with poor network conditions and bad weather, this paper proposes a technique for road extraction and road condition detection from video captured by UAV multispectral cameras in real-time or pre-downloaded multispectral images from satellites, which in turn provides humans with optimal route planning. Additionally, depending on the flight altitude of the UAV, humans can interact with the UAV through dynamic gesture recognition to identify emergency situations and potential dangers for emergency rescue or re-routing. The purpose of this work is to detect the road condition and identify emergency situations in order to provide necessary and timely assistance to humans in the wild. By obtaining a normalized difference vegetation index (NDVI), the UAV can effectively distinguish between bare soil roads and gravel roads, refining the results of our previous route planning data. In the low-altitude human–machine interaction part, based on media-pipe hand landmarks, we combined machine learning methods to build a dataset of four basic hand gestures for sign for help dynamic gesture recognition. We tested the dataset on different classifiers, and the best results show that the model can achieve 99.99% accuracy on the testing set. In this proof-of-concept paper, the above experimental results confirm that our proposed scheme can achieve our expected tasks of UAV rescue and route planning. LA - English DB - MTMT ER - TY - JOUR AU - Yu, Chuang AU - Liu, Yunpeng AU - Xia, Xin AU - Hu, Zhuhua AU - Fu, Shengpeng TI - PRECISE SEGMENTATION OF REMOTE SENSING CAGE IMAGES BASED ON SEGNET AND VOTING MECHANISM JF - APPLIED ENGINEERING IN AGRICULTURE J2 - APPL ENG AGRIC VL - 38 PY - 2022 IS - 3 SP - 573 EP - 581 PG - 9 SN - 0883-8542 DO - 10.13031/aea.14878 UR - https://m2.mtmt.hu/api/publication/32995068 ID - 32995068 AB - In mariculture, improper cage layout and excessive density of mariculture will lead to deterioration of water quality and the growth of harmful bacteria. However, relying solely on manual measurement will consume a considerable amount of manpower and material resources. Therefore, we propose a precise segmentation scheme for remote sensing cage images based on SegNet and voting mechanism. First, a Remote Sensing Cage Segmentation (RSCS) dataset is constructed. Second, the number of collected samples is too small and the sample sizes are too large. Random cropping, data augmentation, and three-channel separation operations are used to construct the training sets. Nine training sets consisting of three image sizes and three single channels are generated. Finally, the proposed sliding window overlap cropping method and two rounds of voting are used on the test samples to improve the segmentation accuracy. The experimental results show that using sliding window overlap cropping, three-channel voting, and three-size voting can improve mIoU (mean Intersection over Union) by up to 0.9%, 1.9%, and 0.6%, respectively. By using the proposed final scheme, the mIoU of test samples can reach 0.89. LA - English DB - MTMT ER - TY - JOUR AU - De Giorgi, A. AU - Solarna, D. AU - Moser, G. AU - Tapete, D. AU - Cigna, F. AU - Boni, G. AU - Rudari, R. AU - Serpico, S.B. AU - Pisani, A.R. AU - Montuori, A. AU - Zoffoli, S. TI - Monitoring the recovery after 2016 hurricane matthew in haiti via markovian multitemporal region-based modeling JF - REMOTE SENSING J2 - REMOTE SENS-BASEL VL - 13 PY - 2021 IS - 17 SN - 2072-4292 DO - 10.3390/rs13173509 UR - https://m2.mtmt.hu/api/publication/32239318 ID - 32239318 N1 - Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Via all’Opera Pia 11a, Genoa, I-16145, Italy Italian Space Agency (ASI), Via del Politecnico, Rome, I-00133, Italy Department of Civil, Chemical, and Environmental Engineering (DICCA), University of Genoa, Via Montallegro 1, Genoa, I-16145, Italy CIMA Foundation, Via Magliotto 1, Savona, I-17100, Italy Export Date: 24 September 2021 Correspondence Address: Moser, G.; Department of Electrical, Via all’Opera Pia 11a, Italy; email: gabriele.moser@unige.it LA - English DB - MTMT ER - TY - JOUR AU - Kamga, G. A. Fotso AU - Bitjoka, L. AU - Akram, T. AU - Mbom, A. Mengue AU - Naqvi, S. Rameez AU - Bouroubi, Y. TI - Advancements in satellite image classification : methodologies, techniques, approaches and applications JF - INTERNATIONAL JOURNAL OF REMOTE SENSING J2 - INT J REMOTE SENS VL - 42 PY - 2021 IS - 20 SP - 7662 EP - 7722 PG - 61 SN - 0143-1161 DO - 10.1080/01431161.2021.1954261 UR - https://m2.mtmt.hu/api/publication/32273748 ID - 32273748 AB - Segmentation and classification are two imperative, yet challenging tasks in image analysis for remote-sensing applications. In the former, an image is divided into spatially continuous, disjoint, and homogeneous regions, called clusters, in terms of their various properties: shape, intensity, texture, colour, contrast, etc. Classification, on the other hand, is applied later in the process, to recognize or categorize individual objects or targets. Each task plays an important role in the refinement and enhancement of the various utilizations of remote sensing images. Driven by recent progress in earth observation sensor technology, satellite image classification systems for earth-observation applications have seen significant growth and progress. This growth has led to a notable increase in the number of published materials in these areas. We present an overview of the horizons that the modern remote sensing domain promises in terms of the efficient classification processing of satellite imagery. We begin by defining remote sensing, specifically in the context of its potential application areas, and highlight the importance of pre-processing and feature extraction steps' in accurate classification. Various works have proposed novel segmentation, feature extraction/selection, and classification methods; these have been collected and duly reported in this work. The deep learning classification method has been given special attention due to its relatively limited dependence on training data, its wide spectrum of applications, and its ability to autonomously classify images with higher accuracy. We conclude by presenting a critical evaluation of the important contributions in this domain. LA - English DB - MTMT ER - TY - JOUR AU - Jiang, Mi AU - Hooper, Andy AU - Tian, Xin AU - Xu, Jia AU - Chen, Sai-Nan AU - Ma, Zhang-Feng AU - Cheng, Xiao TI - Delineation of built-up land change from SAR stack by analysing the coefficient of variation JF - ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING J2 - ISPRS J PHOTOGRAMM VL - 169 PY - 2020 SP - 93 EP - 108 PG - 16 SN - 0924-2716 DO - 10.1016/j.isprsjprs.2020.08.023 UR - https://m2.mtmt.hu/api/publication/31751746 ID - 31751746 N1 - School of Geospatial Engineering and Science, Sun Yat-Sen University, Guangzhou, 510275, China Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China Centre for the Observation and Modelling of Earthquakes Volcanoes and Tectonics (COMET), School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, United Kingdom Department of Surveying and Mapping Engineering, School of Transportation, Southeast University, Nanjing, Jiangsu 210096, China School of Earth Science and Engineering, Hohai University, Nanjing, Jiangsu 211100, China Cited By :1 Export Date: 24 September 2021 CODEN: IRSEE Correspondence Address: Tian, X.; Department of Surveying and Mapping Engineering, China; email: tianxin@seu.edu.cn LA - English DB - MTMT ER - TY - JOUR AU - Yan, Xiyu AU - Gong, Huihui AU - Jiang, Yong AU - Xia, Shu-Tao AU - Zheng, Feng AU - You, Xinge AU - Shao, Ling TI - Video scene parsing: An overview of deep learning methods and datasets JF - COMPUTER VISION AND IMAGE UNDERSTANDING J2 - COMPUT VIS IMAGE UND VL - 201 PY - 2020 SN - 1077-3142 DO - 10.1016/j.cviu.2020.103077 UR - https://m2.mtmt.hu/api/publication/31751830 ID - 31751830 N1 - Tsinghua Shenzhen International Graduate School, Tsinghua University (THU), Shenzhen, China Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, 518055, China PCL Research Center of Networks and Communications, Peng Cheng Laboratory, Shenzhen, China School of Electronic Information and Communications, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates Cited By :1 Export Date: 24 September 2021 CODEN: CVIUF Correspondence Address: Zheng, F.; Department of Computer Science and Engineering, China; email: zhengf@sustech.edu.cn LA - English DB - MTMT ER - TY - JOUR AU - Shamsolmoali, Pourya AU - Zareapoor, Masoumeh AU - Wang, Ruili AU - Zhou, Huiyu AU - Yang, Jie TI - A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images JF - IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING J2 - IEEE J-STARS VL - 12 PY - 2019 IS - 9 SP - 3219 EP - 3232 PG - 14 SN - 1939-1404 DO - 10.1109/JSTARS.2019.2925841 UR - https://m2.mtmt.hu/api/publication/30906237 ID - 30906237 AB - Sea-land segmentation is an important process for many key applications in remote sensing. Proper operative sea-land segmentation for remote sensing images remains a challenging issue due to complex and diverse transition between sea and land. Although several convolutional neural networks (CNNs) have been developed for sea-land segmentation, the performance of these CNNs is far from the expected target. This paper presents a novel deep neural network structure for pixel-wise sea-land segmentation, a residual Dense U-Net (RDU-Net), in complex and high-density remote sensing images. RDU-Net is a combination of both downsampling and upsampling paths to achieve satisfactory results. In each downsampling and upsampling path, in addition to the convolution layers, several densely connected residual network blocks are proposed to systematically aggregate multiscale contextual information. Each dense network block contains multilevel convolution layers, short-range connections, and an identity mapping connection, which facilitates features reuse in the network and makes full use of the hierarchical features from the original images. These proposed blocks have a certain number of connections that are designed with shorter distance backpropagation between the layers and can significantly improve segmentation results while minimizing computational costs. We have performed extensive experiments on two real datasets, Google-Earth and ISPRS, and compared the proposed RDU-Net against several variations of dense networks. The experimental results show that RDU-Net outperforms the other state-of-the-art approaches on the sea-land segmentation tasks. LA - English DB - MTMT ER - TY - JOUR AU - Wang, Shenlong AU - Han, Kaixin AU - Jin, Jiafeng TI - Review of image low-level feature extraction methods for content-based image retrieval JF - SENSOR REVIEW J2 - SENSOR REV VL - 39 PY - 2019 IS - 6 SP - 783 EP - 809 PG - 27 SN - 0260-2288 DO - 10.1108/SR-04-2019-0092 UR - https://m2.mtmt.hu/api/publication/31229955 ID - 31229955 LA - English DB - MTMT ER - TY - JOUR AU - Guanche, García Y AU - Shadaydeh, M AU - Mahecha, M AU - Denzler, J TI - Extreme anomaly event detection in biosphere using linear regression and a spatiotemporal MRF model JF - NATURAL HAZARDS J2 - NAT HAZARDS VL - xx PY - 2018 SP - x EP - xx SN - 0921-030X DO - 10.1007/s11069-018-3415-8 UR - https://m2.mtmt.hu/api/publication/27698527 ID - 27698527 LA - English DB - MTMT ER - TY - CONF AU - Lebedev, MA AU - Vizilter, YuV AU - Vygolov, OV AU - Knyaz, VA AU - Rubis, AYu ED - Remondino, F. ED - Toschi, I. ED - Fuse, T. TI - Change detection in remote sensing images using conditional adversarial networks T2 - ISPRS TC II Mid-term Symposium "Towards Photogrammetry 2020" PB - International Society for Photogrammetry and Remote Sensing (ISPRS) C1 - Göttingen T3 - ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1777 ; XLII-2. PB - International Society for Photogrammetry and Remote Sensing (ISPRS) PY - 2018 SP - 565 EP - 571 PG - 7 DO - 10.5194/isprs-archives-XLII-2-565-2018 UR - https://m2.mtmt.hu/api/publication/27689918 ID - 27689918 N1 - A4 LA - English DB - MTMT ER - TY - JOUR AU - Lu, X. AU - Wang, Q. AU - Cui, W. TI - Fractal Characterization and Classification Characteristics of the Artificial Joint Wear Particles JF - Yiyong Shengwu Lixue/Journal of Medical Biomechanics J2 - Yiyong Shengwu Lixue/Journal of Medical Biomechanics VL - 33 PY - 2018 IS - 5 SP - 410 EP - 416 PG - 7 SN - 1004-7220 DO - 10.16156/j.1004-7220.2018.05.005 UR - https://m2.mtmt.hu/api/publication/32239356 ID - 32239356 N1 - School of Mechanical and Electrical Engineering, Xuzhou University of Technology, Xuzhou, Jiangsu 221111, China School of Material Science and Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China Cited By :1 Export Date: 24 September 2021 Correspondence Address: Wang, Q.; School of Material Science and Engineering, China; email: wql889@cumt.edu.cn LA - Chinese DB - MTMT ER - TY - JOUR AU - Manno-Kovács, Andrea AU - Majdik, András AU - Szirányi, Tamás TI - Satellite and Aerial Image Processing for Smart Farming and Biodiversity Conservation JF - ERCIM NEWS J2 - ERCIM NEWS PY - 2018 IS - 113 SP - 33 EP - 34 PG - 2 SN - 0926-4981 UR - https://m2.mtmt.hu/api/publication/3363340 ID - 3363340 LA - English DB - MTMT ER - TY - CHAP AU - Scheunders, P. AU - Tuia, D. AU - Moser, G. ED - Shunlin, Liang TI - Contributions of machine learning to remote sensing data analysis T2 - Comprehensive Remote Sensing Vol. 1-9 VL - 1-9 PB - Elsevier CY - New York, New York SN - 9780128032213 PY - 2018 SP - 199 EP - 243 PG - 45 DO - 10.1016/B978-0-12-409548-9.10343-4 UR - https://m2.mtmt.hu/api/publication/32239327 ID - 32239327 N1 - Cited By :5 Export Date: 24 September 2021 LA - English DB - MTMT ER - TY - JOUR AU - Shadaydeh, Maha AU - Zlinszky, András AU - Manno-Kovács, Andrea AU - Szirányi, Tamás TI - Wetland mapping by fusion of airborne laser scanning and multi-temporal multispectral satellite imagery JF - INTERNATIONAL JOURNAL OF REMOTE SENSING J2 - INT J REMOTE SENS VL - 38 PY - 2017 IS - 23 SP - 7422 EP - 7440 PG - 19 SN - 0143-1161 DO - 10.1080/01431161.2017.1375614 UR - https://m2.mtmt.hu/api/publication/3258163 ID - 3258163 LA - English DB - MTMT ER - TY - JOUR AU - Wang, X AU - He, S AU - Tong, Z TI - Improved mixture model for markov random field and its application in magnetic resonance image segmentation JF - JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS J2 - J MED IMAG HEALTH IN VL - 7 PY - 2017 IS - 2 SP - 323 EP - 329 PG - 7 SN - 2156-7018 DO - 10.1166/jmihi.2017.2060 UR - https://m2.mtmt.hu/api/publication/26638538 ID - 26638538 LA - English DB - MTMT ER - TY - JOUR AU - Atiampo, Armand Kodjo AU - Loum, Georges Laussane TI - Unsupervised Image Segmentation with Pairwise Markov Chains Based on Nonparametric Estimation of Copula Using Orthogonal Polynomials JF - INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS J2 - INT J IMAGE GRAPH VL - 16 PY - 2016 IS - 4 PG - 15 SN - 0219-4678 DO - 10.1142/S0219467816500200 UR - https://m2.mtmt.hu/api/publication/26379237 ID - 26379237 LA - English DB - MTMT ER - TY - JOUR AU - Jian, Ping AU - Chen, Keming AU - Zhang, Chenwei TI - A hypergraph-based context-sensitive representation technique for VHR remote-sensing image change detection JF - INTERNATIONAL JOURNAL OF REMOTE SENSING J2 - INT J REMOTE SENS VL - 37 PY - 2016 IS - 8 SP - 1814 EP - 1825 PG - 12 SN - 0143-1161 DO - 10.1080/2150704X.2016.1163744 UR - https://m2.mtmt.hu/api/publication/25774807 ID - 25774807 LA - English DB - MTMT ER - TY - JOUR AU - Monika, Patel AU - Megha, Soni TI - Modulated Intensity Gradient and Texture Gradient Based Image Segmentation JF - International Journal of Emerging Technologies in Engineering Research (IJETER) J2 - International Journal of Emerging Technologies in Engineering Research (IJETER) VL - 4 PY - 2016 IS - 8 SP - 88 EP - 94 PG - 7 SN - 2454-6410 UR - https://m2.mtmt.hu/api/publication/26449399 ID - 26449399 LA - English DB - MTMT ER - TY - JOUR AU - Tian, Shu AU - Zhang, Ye AU - Yan, Yiming AU - Su, Nan AU - Zhang, Junping ED - Silny, JF ED - Ientilucci, EJ TI - Remote Sensing Image Segmentation using Local Sparse Structure Constrainted Latent Low Rank Representation JF - PROCEEDINGS OF SPIE - THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING J2 - PROCEEDINGS OF SPIE VL - 9976 PY - 2016 IS - San Diego PG - 6 SN - 0277-786X DO - 10.1117/12.2237726 UR - https://m2.mtmt.hu/api/publication/26379238 ID - 26379238 LA - English DB - MTMT ER - TY - JOUR AU - YANG, Haiping AU - MING, Dongping TI - Optimal Scales Based Segmentation of High Spatial Resolution Remote Sensing Data JF - DIQIU XINXI KEXUE XUEBAO / JOURNAL OF GEO-INFORMATION SCIENCE J2 - DIQIU XINXI KEXUE XUEBAO / JOURNAL OF GEO-INFORMATION SCIENCE VL - 18 PY - 2016 IS - 5 SP - 632 EP - 638 PG - 7 SN - 1560-8999 DO - 10.3724/SP.J.1047.2016.00632 UR - https://m2.mtmt.hu/api/publication/26449242 ID - 26449242 N1 - 2016 május 13 LA - English DB - MTMT ER - TY - JOUR AU - Benedek, Csaba AU - Shadaydeh, Maha AU - Kato, Zoltan AU - Szirányi, Tamás AU - Zerubia, Josiane TI - Multilayer Markov Random Field Models for Change Detection in Optical Remote Sensing Images JF - ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING J2 - ISPRS J PHOTOGRAMM VL - 107 PY - 2015 SP - 22 EP - 37 PG - 16 SN - 0924-2716 DO - 10.1016/j.isprsjprs.2015.02.006 UR - https://m2.mtmt.hu/api/publication/2726136 ID - 2726136 AB - In this paper, we give a comparative study on three Multilayer Markov Random Field (MRF) based solutions proposed for change detection in optical remote sensing images, called Multicue MRF, Conditional Mixed Markov model, and Fusion MRF. Our purposes are twofold. On one hand, we highlight the significance of the focused model family and we set them against various state-of-the-art approaches through a thematic analysis and quantitative tests. We discuss the advantages and drawbacks of class comparison vs. direct approaches, usage of training data, various targeted application fields and different ways of Ground Truth generation, meantime informing the Reader in which roles the Multilayer MRFs can be efficiently applied. On the other hand we also emphasize the differences between the three focused models at various levels, considering the model structures, feature extraction, layer interpretation, change concept definition, parameter tuning and performance. We provide qualitative and quantitative comparison results using principally a publicly available change detection database which contains aerial image pairs and Ground Truth change masks. We conclude that the discussed models are competitive against alternative state-of-the-art solutions, if one uses them as pre-processing filters in multitemporal optical image analysis. In addition, they cover together a large range of applications, considering the different usage options of the three approaches. 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