@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}
}

@CONFERENCE{MTMT:3011569,
	title = {A lokális hasonlósági mérték továbbfejlesztése távérzékelt képek változásainak becsléséhez},
	url = {https://m2.mtmt.hu/api/publication/3011569},
	author = {Shadaydeh, Maha and Szirányi, Tamás},
	booktitle = {Képfeldolgozók és Alakfelismerők Társaságának 10. országos konferenciája KÉPAF 2015},
	unique-id = {3011569},
	year = {2015},
	pages = {240-249},
	orcid-numbers = {Szirányi, Tamás/0000-0003-2989-0214}
}

@inproceedings{MTMT:2946297,
	title = {An improved mutual information similarity measure for registration of multi-modal remote sensing images},
	url = {https://m2.mtmt.hu/api/publication/2946297},
	author = {Shadaydeh, Maha and Szirányi, Tamás},
	booktitle = {Image and Signal Processing for Remote Sensing XXI},
	doi = {10.1117/12.2194319},
	unique-id = {2946297},
	year = {2015},
	orcid-numbers = {Szirányi, Tamás/0000-0003-2989-0214}
}

@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}
}

@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}
}

@article{MTMT:2467262,
	title = {Segmentation of remote sensing images using similarity-measure-based fusion-MRF model},
	url = {https://m2.mtmt.hu/api/publication/2467262},
	author = {Szirányi, Tamás and Shadaydeh, Maha},
	doi = {10.1109/LGRS.2014.2300873},
	journal-iso = {IEEE GEOSCI REMOTE S},
	journal = {IEEE GEOSCIENCE AND REMOTE SENSING LETTERS},
	volume = {11},
	unique-id = {2467262},
	issn = {1545-598X},
	year = {2014},
	eissn = {1558-0571},
	pages = {1544-1548},
	orcid-numbers = {Szirányi, Tamás/0000-0003-2989-0214}
}

@inproceedings{MTMT:2319152,
	title = {Improved segmentation of a series of remote sensing images by using a fusion MRF model},
	url = {https://m2.mtmt.hu/api/publication/2319152},
	author = {Szirányi, Tamás and Shadaydeh, Maha},
	booktitle = {11th International Workshop on Content-Based Multimedia Indexing. CBMI 2013},
	doi = {10.1109/CBMI.2013.6576571},
	unique-id = {2319152},
	abstract = {Classifying segments and detection of changes in terrestrial 
		areas are important remote-sensing tasks. Some country areas are 
		scanned frequently (e.g. year-by-year) to spot relevant changes, 
		and several repositories contain multi-temporal image samples 
		for the same area in very different quality and details. 
		We propose a Multi-Layer Markovian adaptive fusion on $Luv$ 
		color images and similarity measure for the segmentation and 
		detection of changes in a series of remote sensing images. We 
		aim the problem of detecting details in rarely scanned remote 
		sensing areas, where trajectory analysis or direct comparison is 
		not applicable.
		Our method applies unsupervised or partly supervised clustering 
		based on a cross-image featuring, followed by multilayer MRF 
		segmentation in the mixed dimensionality. On the base of the 
		fused segmentation, the clusters of the single layers are 
		trained by clusters of the mixed results. The improvement of 
		this (partly) unsupervised method has been validated on remotely 
		sensed image series.},
	year = {2013},
	pages = {137-142},
	orcid-numbers = {Szirányi, Tamás/0000-0003-2989-0214}
}