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  - CONF
AU  - Shadaydeh, Maha
AU  - Szirányi, Tamás
ED  - Csetverikov, D
TI  - A lokális hasonlósági mérték továbbfejlesztése távérzékelt képek változásainak becsléséhez
T2  - Képfeldolgozók és Alakfelismerők Társaságának 10. országos konferenciája KÉPAF 2015
PB  - Neumann János Számítógép-tudományi Társaság
C1  - Budapest
PY  - 2015
SP  - 240
EP  - 249
PG  - 10
UR  - https://m2.mtmt.hu/api/publication/3011569
ID  - 3011569
LA  - Hungarian
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Shadaydeh, Maha
AU  - Szirányi, Tamás
ED  - Bruzzone, Lorenzo
TI  - An improved mutual information similarity measure for registration of multi-modal remote sensing images
T2  - Image and Signal Processing for Remote Sensing XXI
PB  - SPIE
CY  - Bellingham (WA)
SN  - 9781628418538
T3  - Proceedings of SPIE, ISSN 0277-786X ; 9643.
PY  - 2015
PG  - 7
DO  - 10.1117/12.2194319
UR  - https://m2.mtmt.hu/api/publication/2946297
ID  - 2946297
N1  - A4 The Society of Photo-Optical Instrumentation Engineers (SPIE)
WoS:hiba:000367469500014 2019-03-03 11:20 befoglaló kiadók nem egyeznek
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.
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Shadaydeh, Maha
AU  - Szirányi, Tamás
ED  - IEEE, null
TI  - An improved local similarity measure estimation for change detection in remote sensing images
T2  - 2014 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES)
PB  - IEEE
CY  - Yogyakarta
SN  - 9781479961887
PY  - 2014
SP  - 234
EP  - 238
PG  - 5
DO  - 10.1109/ICARES.2014.7024381
UR  - https://m2.mtmt.hu/api/publication/2726494
ID  - 2726494
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Szirányi, Tamás
AU  - Shadaydeh, Maha
TI  - Segmentation of remote sensing images using similarity-measure-based fusion-MRF model
JF  - IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
J2  - IEEE GEOSCI REMOTE S
VL  - 11
PY  - 2014
IS  - 9
SP  - 1544
EP  - 1548
PG  - 5
SN  - 1545-598X
DO  - 10.1109/LGRS.2014.2300873
UR  - https://m2.mtmt.hu/api/publication/2467262
ID  - 2467262
N1  - WoS:hiba:000333105400019 2019-03-03 09:51 cikkazonosító nem egyezik
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Szirányi, Tamás
AU  - Shadaydeh, Maha
ED  - Czúni, László
ED  - Schöffmann, K
ED  - Szirányi, Tamás
TI  - Improved segmentation of a series of remote sensing images by using a fusion MRF model
T2  - 11th International Workshop on Content-Based Multimedia Indexing. CBMI 2013
PB  - IEEE
CY  - Seattle (WA)
SN  - 9781479909551
PY  - 2013
SP  - 137
EP  - 142
PG  - 6
DO  - 10.1109/CBMI.2013.6576571
UR  - https://m2.mtmt.hu/api/publication/2319152
ID  - 2319152
N1  - 
AB  - 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.
LA  - English
DB  - MTMT
ER  -