@article{MTMT:33887219,
	title = {A review of image fusion: Methods, applications and performance metrics},
	url = {https://m2.mtmt.hu/api/publication/33887219},
	author = {Singh, Simrandeep and Singh, Harbinder and Bueno, Gloria and Deniz, Oscar and Singh, Sartajvir and Monga, Himanshu and Hrisheekesha, P. N. and Pedraza, Anibal},
	doi = {10.1016/j.dsp.2023.104020},
	journal-iso = {DIGIT SIGNAL PROCESS},
	journal = {DIGITAL SIGNAL PROCESSING},
	volume = {137},
	unique-id = {33887219},
	issn = {1051-2004},
	abstract = {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.},
	keywords = {Segmentation; Information fusion; image decomposition; Quality metrics; Fusion criteria},
	year = {2023},
	eissn = {1095-4333}
}

@article{MTMT:34273683,
	title = {RSLC-Deeplab: A Ground Object Classification Method for High-Resolution Remote Sensing Images},
	url = {https://m2.mtmt.hu/api/publication/34273683},
	author = {Yu, Zhimin and Wan, Fang and Lei, Guangbo and Xiong, Ying and Xu, Li and Ye, Zhiwei and Liu, Wei and Zhou, Wen and Xu, Chengzhi},
	doi = {10.3390/electronics12173653},
	journal = {ELECTRONICS (SWITZ)},
	volume = {12},
	unique-id = {34273683},
	abstract = {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.},
	keywords = {Semantic segmentation; attention mechanism; feature fusion; high-resolution remote sensing images},
	year = {2023},
	eissn = {2079-9292}
}

@misc{MTMT:32829323,
	title = {Farm Parcel Extraction in High Resolution Remote Sensing Image Based on Hierarchical Spectrum and Shape Features},
	url = {https://m2.mtmt.hu/api/publication/32829323},
	author = {Bangyu, Li},
	unique-id = {32829323},
	year = {2022},
	pages = {&}
}

@article{MTMT:32954865,
	title = {Individual Tree Crown Delineation From UAS Imagery Based on Region Growing by Over-Segments With a Competitive Mechanism},
	url = {https://m2.mtmt.hu/api/publication/32954865},
	author = {Gu, Jianyu and Congalton, Russell G.},
	doi = {10.1109/TGRS.2021.3074289},
	journal-iso = {IEEE T GEOSCI REMOTE},
	journal = {IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING},
	volume = {60},
	unique-id = {32954865},
	issn = {0196-2892},
	abstract = {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.},
	keywords = {COMPETITION; remote sensing; VEGETATION; Image segmentation; Forestry; spatial resolution; region growing; Three-dimensional displays; unmanned aerial system (UAS); over-segmentation; Image edge detection; individual tree crown delineation (ITCD)},
	year = {2022},
	eissn = {1558-0644},
	pages = {1-11}
}

@article{MTMT:32893768,
	title = {A novel artificial intelligence-based approach for mapping groundwater nitrate pollution in the Andimeshk-Dezful plain, Iran},
	url = {https://m2.mtmt.hu/api/publication/32893768},
	author = {Javidan, Raana and Javidan, Narges},
	doi = {10.1080/10106049.2022.2035830},
	journal-iso = {GEOCAR INT},
	journal = {GEOCARTO INTERNATIONAL},
	volume = {&},
	unique-id = {32893768},
	issn = {1010-6049},
	year = {2022},
	eissn = {1752-0762},
	pages = {1-25}
}

@article{MTMT:33086093,
	title = {Road Condition Detection and Emergency Rescue Recognition Using On-Board UAV in the Wildness},
	url = {https://m2.mtmt.hu/api/publication/33086093},
	author = {Liu, Chang and Szirányi, Tamás},
	doi = {10.3390/rs14174355},
	journal-iso = {REMOTE SENS-BASEL},
	journal = {REMOTE SENSING},
	volume = {14},
	unique-id = {33086093},
	abstract = {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.},
	year = {2022},
	eissn = {2072-4292},
	orcid-numbers = {Szirányi, Tamás/0000-0003-2989-0214}
}

@article{MTMT:32995068,
	title = {PRECISE SEGMENTATION OF REMOTE SENSING CAGE IMAGES BASED ON SEGNET AND VOTING MECHANISM},
	url = {https://m2.mtmt.hu/api/publication/32995068},
	author = {Yu, Chuang and Liu, Yunpeng and Xia, Xin and Hu, Zhuhua and Fu, Shengpeng},
	doi = {10.13031/aea.14878},
	journal-iso = {APPL ENG AGRIC},
	journal = {APPLIED ENGINEERING IN AGRICULTURE},
	volume = {38},
	unique-id = {32995068},
	issn = {0883-8542},
	abstract = {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.},
	keywords = {mariculture; Remote image segmentation; SegNet; Sliding window overlap cropping; Voting mechanism},
	year = {2022},
	eissn = {1943-7838},
	pages = {573-581}
}

@article{MTMT:32239318,
	title = {Monitoring the recovery after 2016 hurricane matthew in haiti via markovian multitemporal region-based modeling},
	url = {https://m2.mtmt.hu/api/publication/32239318},
	author = {De Giorgi, A. and Solarna, D. and Moser, G. and Tapete, D. and Cigna, F. and Boni, G. and Rudari, R. and Serpico, S.B. and Pisani, A.R. and Montuori, A. and Zoffoli, S.},
	doi = {10.3390/rs13173509},
	journal-iso = {REMOTE SENS-BASEL},
	journal = {REMOTE SENSING},
	volume = {13},
	unique-id = {32239318},
	year = {2021},
	eissn = {2072-4292}
}

@article{MTMT:32273748,
	title = {Advancements in satellite image classification : methodologies, techniques, approaches and applications},
	url = {https://m2.mtmt.hu/api/publication/32273748},
	author = {Kamga, G. A. Fotso and Bitjoka, L. and Akram, T. and Mbom, A. Mengue and Naqvi, S. Rameez and Bouroubi, Y.},
	doi = {10.1080/01431161.2021.1954261},
	journal-iso = {INT J REMOTE SENS},
	journal = {INTERNATIONAL JOURNAL OF REMOTE SENSING},
	volume = {42},
	unique-id = {32273748},
	issn = {0143-1161},
	abstract = {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.},
	year = {2021},
	eissn = {1366-5901},
	pages = {7662-7722}
}

@article{MTMT:31751746,
	title = {Delineation of built-up land change from SAR stack by analysing the coefficient of variation},
	url = {https://m2.mtmt.hu/api/publication/31751746},
	author = {Jiang, Mi and Hooper, Andy and Tian, Xin and Xu, Jia and Chen, Sai-Nan and Ma, Zhang-Feng and Cheng, Xiao},
	doi = {10.1016/j.isprsjprs.2020.08.023},
	journal-iso = {ISPRS J PHOTOGRAMM},
	journal = {ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING},
	volume = {169},
	unique-id = {31751746},
	issn = {0924-2716},
	year = {2020},
	eissn = {1872-8235},
	pages = {93-108},
	orcid-numbers = {Jiang, Mi/0000-0003-2459-4619; Ma, Zhang-Feng/0000-0003-0044-7710}
}

@article{MTMT:31751830,
	title = {Video scene parsing: An overview of deep learning methods and datasets},
	url = {https://m2.mtmt.hu/api/publication/31751830},
	author = {Yan, Xiyu and Gong, Huihui and Jiang, Yong and Xia, Shu-Tao and Zheng, Feng and You, Xinge and Shao, Ling},
	doi = {10.1016/j.cviu.2020.103077},
	journal-iso = {COMPUT VIS IMAGE UND},
	journal = {COMPUTER VISION AND IMAGE UNDERSTANDING},
	volume = {201},
	unique-id = {31751830},
	issn = {1077-3142},
	year = {2020},
	eissn = {1090-235X}
}

@article{MTMT:30906237,
	title = {A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images},
	url = {https://m2.mtmt.hu/api/publication/30906237},
	author = {Shamsolmoali, Pourya and Zareapoor, Masoumeh and Wang, Ruili and Zhou, Huiyu and Yang, Jie},
	doi = {10.1109/JSTARS.2019.2925841},
	journal-iso = {IEEE J-STARS},
	journal = {IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING},
	volume = {12},
	unique-id = {30906237},
	issn = {1939-1404},
	abstract = {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.},
	keywords = {Remote sensing images; Deep neural network (DNN); dense network (DenseNet); sea-land segmentation; U-Net},
	year = {2019},
	eissn = {2151-1535},
	pages = {3219-3232}
}

@article{MTMT:31229955,
	title = {Review of image low-level feature extraction methods for content-based image retrieval},
	url = {https://m2.mtmt.hu/api/publication/31229955},
	author = {Wang, Shenlong and Han, Kaixin and Jin, Jiafeng},
	doi = {10.1108/SR-04-2019-0092},
	journal-iso = {SENSOR REV},
	journal = {SENSOR REVIEW},
	volume = {39},
	unique-id = {31229955},
	issn = {0260-2288},
	year = {2019},
	eissn = {1758-6828},
	pages = {783-809}
}

@article{MTMT:27698527,
	title = {Extreme anomaly event detection in biosphere using linear regression and a spatiotemporal MRF model},
	url = {https://m2.mtmt.hu/api/publication/27698527},
	author = {Guanche, García Y and Shadaydeh, M and Mahecha, M and Denzler, J},
	doi = {10.1007/s11069-018-3415-8},
	journal-iso = {NAT HAZARDS},
	journal = {NATURAL HAZARDS},
	volume = {xx},
	unique-id = {27698527},
	issn = {0921-030X},
	year = {2018},
	eissn = {1573-0840},
	pages = {x-xx}
}

@CONFERENCE{MTMT:27689918,
	title = {Change detection in remote sensing images using conditional adversarial networks},
	url = {https://m2.mtmt.hu/api/publication/27689918},
	author = {Lebedev, MA and Vizilter, YuV and Vygolov, OV and Knyaz, VA and Rubis, AYu},
	booktitle = {ISPRS TC II Mid-term Symposium "Towards Photogrammetry 2020"},
	doi = {10.5194/isprs-archives-XLII-2-565-2018},
	publisher = {ISPRS},
	unique-id = {27689918},
	year = {2018},
	pages = {565-571}
}

@article{MTMT:32239356,
	title = {Fractal Characterization and Classification Characteristics of the Artificial Joint Wear Particles},
	url = {https://m2.mtmt.hu/api/publication/32239356},
	author = {Lu, X. and Wang, Q. and Cui, W.},
	doi = {10.16156/j.1004-7220.2018.05.005},
	journal-iso = {Yiyong Shengwu Lixue/Journal of Medical Biomechanics},
	journal = {Yiyong Shengwu Lixue/Journal of Medical Biomechanics},
	volume = {33},
	unique-id = {32239356},
	issn = {1004-7220},
	year = {2018},
	pages = {410-416}
}

@article{MTMT:3363340,
	title = {Satellite and Aerial Image Processing for Smart Farming and Biodiversity Conservation},
	url = {https://m2.mtmt.hu/api/publication/3363340},
	author = {Manno-Kovács, Andrea and Majdik, András and Szirányi, Tamás},
	journal-iso = {ERCIM NEWS},
	journal = {ERCIM NEWS},
	unique-id = {3363340},
	issn = {0926-4981},
	year = {2018},
	eissn = {1564-0094},
	pages = {33-34},
	orcid-numbers = {Manno-Kovács, Andrea/0000-0002-9392-379X; Majdik, András/0000-0003-1807-2865; Szirányi, Tamás/0000-0003-2989-0214}
}

@{MTMT:32239327,
	title = {Contributions of machine learning to remote sensing data analysis},
	url = {https://m2.mtmt.hu/api/publication/32239327},
	author = {Scheunders, P. and Tuia, D. and Moser, G.},
	booktitle = {Comprehensive Remote Sensing Vol. 1-9},
	doi = {10.1016/B978-0-12-409548-9.10343-4},
	volume = {1-9},
	unique-id = {32239327},
	year = {2018},
	pages = {199-243}
}

@article{MTMT:3258163,
	title = {Wetland mapping by fusion of airborne laser scanning and multi-temporal multispectral satellite imagery},
	url = {https://m2.mtmt.hu/api/publication/3258163},
	author = {Shadaydeh, Maha and Zlinszky, András and Manno-Kovács, Andrea and Szirányi, Tamás},
	doi = {10.1080/01431161.2017.1375614},
	journal-iso = {INT J REMOTE SENS},
	journal = {INTERNATIONAL JOURNAL OF REMOTE SENSING},
	volume = {38},
	unique-id = {3258163},
	issn = {0143-1161},
	year = {2017},
	eissn = {1366-5901},
	pages = {7422-7440},
	orcid-numbers = {Zlinszky, András/0000-0002-9717-0043; Manno-Kovács, Andrea/0000-0002-9392-379X; Szirányi, Tamás/0000-0003-2989-0214}
}

@article{MTMT:26638538,
	title = {Improved mixture model for markov random field and its application in magnetic resonance image segmentation},
	url = {https://m2.mtmt.hu/api/publication/26638538},
	author = {Wang, X and He, S and Tong, Z},
	doi = {10.1166/jmihi.2017.2060},
	journal-iso = {J MED IMAG HEALTH IN},
	journal = {JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS},
	volume = {7},
	unique-id = {26638538},
	issn = {2156-7018},
	year = {2017},
	eissn = {2156-7026},
	pages = {323-329}
}

@article{MTMT:26379237,
	title = {Unsupervised Image Segmentation with Pairwise Markov Chains Based on Nonparametric Estimation of Copula Using Orthogonal Polynomials},
	url = {https://m2.mtmt.hu/api/publication/26379237},
	author = {Atiampo, Armand Kodjo and Loum, Georges Laussane},
	doi = {10.1142/S0219467816500200},
	journal-iso = {INT J IMAGE GRAPH},
	journal = {INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS},
	volume = {16},
	unique-id = {26379237},
	issn = {0219-4678},
	year = {2016},
	eissn = {1793-6756}
}

@article{MTMT:25774807,
	title = {A hypergraph-based context-sensitive representation technique for VHR remote-sensing image change detection},
	url = {https://m2.mtmt.hu/api/publication/25774807},
	author = {Jian, Ping and Chen, Keming and Zhang, Chenwei},
	doi = {10.1080/2150704X.2016.1163744},
	journal-iso = {INT J REMOTE SENS},
	journal = {INTERNATIONAL JOURNAL OF REMOTE SENSING},
	volume = {37},
	unique-id = {25774807},
	issn = {0143-1161},
	year = {2016},
	eissn = {1366-5901},
	pages = {1814-1825}
}

@article{MTMT:26449399,
	title = {Modulated Intensity Gradient and Texture Gradient Based Image Segmentation},
	url = {https://m2.mtmt.hu/api/publication/26449399},
	author = {Monika, Patel and Megha, Soni},
	journal-iso = {International Journal of Emerging Technologies in Engineering Research (IJETER)},
	journal = {International Journal of Emerging Technologies in Engineering Research (IJETER)},
	volume = {4},
	unique-id = {26449399},
	issn = {2454-6410},
	year = {2016},
	pages = {88-94}
}

@article{MTMT:26379238,
	title = {Remote Sensing Image Segmentation using Local Sparse Structure Constrainted Latent Low Rank Representation},
	url = {https://m2.mtmt.hu/api/publication/26379238},
	author = {Tian, Shu and Zhang, Ye and Yan, Yiming and Su, Nan and Zhang, Junping},
	doi = {10.1117/12.2237726},
	editor = {Silny, JF and Ientilucci, EJ},
	journal-iso = {PROCEEDINGS OF SPIE},
	journal = {PROCEEDINGS OF SPIE - THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING},
	volume = {9976},
	unique-id = {26379238},
	issn = {0277-786X},
	year = {2016},
	eissn = {1996-756X}
}

@article{MTMT:26449242,
	title = {Optimal Scales Based Segmentation of High Spatial Resolution Remote Sensing Data},
	url = {https://m2.mtmt.hu/api/publication/26449242},
	author = {YANG, Haiping and MING, Dongping},
	doi = {10.3724/SP.J.1047.2016.00632},
	journal-iso = {DIQIU XINXI KEXUE XUEBAO / JOURNAL OF GEO-INFORMATION SCIENCE},
	journal = {DIQIU XINXI KEXUE XUEBAO / JOURNAL OF GEO-INFORMATION SCIENCE},
	volume = {18},
	unique-id = {26449242},
	issn = {1560-8999},
	year = {2016},
	pages = {632-638}
}

@article{MTMT:2726136,
	title = {Multilayer Markov Random Field Models for Change Detection in Optical Remote Sensing Images},
	url = {https://m2.mtmt.hu/api/publication/2726136},
	author = {Benedek, Csaba and Shadaydeh, Maha and Kato, Zoltan and Szirányi, Tamás and Zerubia, Josiane},
	doi = {10.1016/j.isprsjprs.2015.02.006},
	journal-iso = {ISPRS J PHOTOGRAMM},
	journal = {ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING},
	volume = {107},
	unique-id = {2726136},
	issn = {0924-2716},
	abstract = {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.},
	year = {2015},
	eissn = {1872-8235},
	pages = {22-37},
	orcid-numbers = {Benedek, Csaba/0000-0003-3203-0741; Szirányi, Tamás/0000-0003-2989-0214}
}

@article{MTMT:25417416,
	title = {Brain Tumor Detection using Modified Fuzzy Segmentation},
	url = {https://m2.mtmt.hu/api/publication/25417416},
	author = {Deendyal, S and Malhotra, S},
	journal-iso = {INT J INNOV RES SCI ENG TECHNOL},
	journal = {INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN SCIENCE ENGINEERING AND TECHNOLOGY},
	volume = {2},
	unique-id = {25417416},
	issn = {2347-6710},
	year = {2015},
	eissn = {2319-8753},
	pages = {46-51}
}

@article{MTMT:26260112,
	title = {An object-oriented classification for hyperspectral remote sensing images based on improved genetic algorithm and support vector regression},
	url = {https://m2.mtmt.hu/api/publication/26260112},
	author = {Gao, H and Li, C},
	doi = {10.1166/jctn.2015.4410},
	journal-iso = {J COMPUT THEOR NANOS},
	journal = {JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE},
	volume = {12},
	unique-id = {26260112},
	issn = {1546-1955},
	year = {2015},
	eissn = {1546-1963},
	pages = {4624-4631}
}

@article{MTMT:24982759,
	title = {Multimodal Classification of Remote Sensing Images: A Review and Future Directions},
	url = {https://m2.mtmt.hu/api/publication/24982759},
	author = {Gomez-Chova, L and Tuia, D and Moser, G and Camps-Valls, G},
	doi = {10.1109/JPROC.2015.2449668},
	journal-iso = {P IEEE},
	journal = {PROCEEDINGS OF THE IEEE},
	volume = {103},
	unique-id = {24982759},
	issn = {0018-9219},
	year = {2015},
	eissn = {1558-2256},
	pages = {1560-1584}
}

@article{MTMT:25311625,
	title = {Segmentation Fusion for Building Detection Using Domain-Specific Information},
	url = {https://m2.mtmt.hu/api/publication/25311625},
	author = {Karadag, Ozge Oztimur and Senaras, Caglar and Vural, Fatos T Yarman},
	doi = {10.1109/JSTARS.2015.2403617},
	journal-iso = {IEEE J-STARS},
	journal = {IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING},
	volume = {8},
	unique-id = {25311625},
	issn = {1939-1404},
	year = {2015},
	eissn = {2151-1535},
	pages = {3305-3315}
}

@article{MTMT:24799933,
	title = {Supervised Segmentation of Remote Sensing Image Using Reference Descriptor},
	url = {https://m2.mtmt.hu/api/publication/24799933},
	author = {Mei, Tiancan and An, Le and Li, Qun},
	doi = {10.1109/LGRS.2014.2368552},
	journal-iso = {IEEE GEOSCI REMOTE S},
	journal = {IEEE GEOSCIENCE AND REMOTE SENSING LETTERS},
	volume = {12},
	unique-id = {24799933},
	issn = {1545-598X},
	year = {2015},
	eissn = {1558-0571},
	pages = {938-942}
}

@inproceedings{MTMT:25036070,
	title = {State change detection on PV panels based on image processing},
	url = {https://m2.mtmt.hu/api/publication/25036070},
	author = {Ozbey, N and Karakose, M},
	booktitle = {2015 23rd Signal Processing and Communications Applications Conference, SIU 2015},
	doi = {10.1109/SIU.2015.7130064},
	unique-id = {25036070},
	year = {2015},
	pages = {1248-1251}
}

@inproceedings{MTMT:25417403,
	title = {Change detection for optical and radar images using a Bayesian nonparametric model coupled with a Markov random field},
	url = {https://m2.mtmt.hu/api/publication/25417403},
	author = {Prendes, J and Chabert, M and Pascal, F and Giros, A},
	booktitle = {40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015},
	doi = {10.1109/ICASSP.2015.7178223},
	publisher = {Institute of Electrical and Electronics Engineers},
	unique-id = {25417403},
	year = {2015},
	pages = {1513-1517}
}

@article{MTMT:25035422,
	title = {Multispectral remote sensing image segmentation using rival penalized controlled competitive learning and fuzzy entropy},
	url = {https://m2.mtmt.hu/api/publication/25035422},
	author = {Xie X Luo, H and Wang, C and Liu, S and Xu, X and Tong, X},
	doi = {10.1007/s00500-015-1601-0},
	journal-iso = {SOFT COMPUT},
	journal = {SOFT COMPUTING},
	volume = {-},
	unique-id = {25035422},
	issn = {1432-7643},
	year = {2015},
	eissn = {1433-7479},
	pages = {1-14}
}

@inproceedings{MTMT:2726494,
	title = {An improved local similarity measure estimation for change detection in remote sensing images},
	url = {https://m2.mtmt.hu/api/publication/2726494},
	author = {Shadaydeh, Maha and Szirányi, Tamás},
	booktitle = {2014 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES)},
	doi = {10.1109/ICARES.2014.7024381},
	unique-id = {2726494},
	year = {2014},
	pages = {234-238},
	orcid-numbers = {Szirányi, Tamás/0000-0003-2989-0214}
}