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Edge-preserving classification of high-resolution remote-sensing images by Markovian data fusion

Gabriele Moser, Sebastiano B. Serpico
2009 2009 IEEE International Geoscience and Remote Sensing Symposium  
In this paper, a novel supervised classification technique is proposed for multispectral HR images.  ...  the related MRF model through an edge-preserving approach and by endowing the resulting model with a novel parameter-optimization technique.  ...  developed in [1] in order to define an edge-preserving hybrid supervised/unsupervised MRF model for HR image classification.  ... 
doi:10.1109/igarss.2009.5417489 dblp:conf/igarss/MoserS09 fatcat:vyqre5jvgfgkzd4xhu6vambnoi

Wetland mapping by fusion of airborne laser scanning and multi-temporal multispectral satellite imagery

Maha Shadaydeh, András Zlinszky, Andrea Manno-Kovacs, Tamas Sziranyi
2017 International Journal of Remote Sensing  
This paper presents an automatic method for the mapping and monitoring of wetlands based on the fused processing of laser scans and multispectral satellite imagery, with validations and evaluations performed  ...  In this paper we propose the Multi-Layer Fusion Markov Random Field (ML-FMRF) model for classifying wetland areas, built into an automatic classification process that combines multitemporal multispectral  ...  Acknowledgments This work was partially funded by the Government of Hungary through a European Space Agency (ESA) Contract (DUSIREF) under the Plan for European Cooperating  ... 
doi:10.1080/01431161.2017.1375614 fatcat:fe2s2l4toffsboacw2pji3nx64

Building detection in very high resolution multispectral data with deep learning features

M. Vakalopoulou, K. Karantzalos, N. Komodakis, N. Paragios
2015 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)  
An MRF model is then responsible for obtaining the optimal labels regarding the detection of scene buildings.  ...  The core of the developed method is based on a supervised classification procedure employing a very large training dataset.  ...  For the classification, the same SVM model has been employed for all the test images. The resulting classification map delivers for each pixel a calculated score for each class.  ... 
doi:10.1109/igarss.2015.7326158 dblp:conf/igarss/VakalopoulouKKP15 fatcat:7grqlipjtbby5lvkv4sry23dwy

A Novel Classification Optimization Approach Integrating Class Adaptive MRF and Fuzzy Local Information for High Spatial Resolution Multispectral Imagery

Yuejin Zhou, Hua Zhang, Xiaoding Xu, Mingpeng Li, Lihui Zheng, Yakun Zhu
2018 Applied Sciences  
This paper develops a novel classification optimization approach integrating class adaptive Markov Random Field (MRF) and fuzzy local information (CAMRF-FLI) for high spatial resolution multispectral imagery  ...  Secondly, the class adaptive MRF-based data energy function is developed to integrate class spatial dependency information.  ...  Details of the MRF-based classification approach have been introduced previously [10] .  ... 
doi:10.3390/app8101792 fatcat:n2nvwitgw5g4dmkddrda5scgq4

Markovian approach using several Gibbs energy for remote sensing images segmentation

Sadia Alkama, Youssef Chahir, Daoud Berkani
2011 Analog Integrated Circuits and Signal Processing  
In this paper, we segment multispectral images MSG2, provided by meteorological satellite "Meteosat Second Generation 2", by using an approach based on support vector Markov model witch takes into account  ...  The high resolution multispectral imagery needs to be segmented into regions that can be easily interpreted and which correspond roughly to the "ground truth".  ...  The criterions developed for the color imagery can be applied to the multispectral imagery by extending the dimension.  ... 
doi:10.1007/s10470-011-9631-8 fatcat:u2or57llura7feude3wiqo7cpe

Markov Random Field-Based Segmentation Algorithm For Detection Of Land Cover Changes Using Uninhabited Aerial Vehicle Synthetic Aperture Radar Polarimetric Images

Mehrnoosh Omati, Mahmod Reza Sahebi
2017 Zenodo  
This paper proposes a new boundary-based segmentation algorithm as a fundamental step for land cover change detection.  ...  Then, object-based classification is performed to determine changed/no changed image objects.  ...  ACKNOWLEDGMENT The authors would like to thank American JPL/NASA, for providing the data set, and the remote sensing research center of K.N Toosi University of technology.  ... 
doi:10.5281/zenodo.1131534 fatcat:55iybkjoiveidm6mo23twhult4

Monitoring the Recovery after 2016 Hurricane Matthew in Haiti via Markovian Multitemporal Region-Based Modeling

Andrea De Giorgi, David Solarna, Gabriele Moser, Deodato Tapete, Francesca Cigna, Giorgio Boni, Roberto Rudari, Sebastiano Bruno Serpico, Anna Rita Pisani, Antonio Montuori, Simona Zoffoli
2021 Remote Sensing  
Moreover, the adoption of a region-based approach allows for the characterization of the geometrical structures in the images through multiple segmentation maps at different scales and times.  ...  This is accomplished via a novel change detection method that has been formulated, in a data fusion perspective, in terms of multitemporal supervised classification.  ...  models for image classification and segmentation [5, 9] .  ... 
doi:10.3390/rs13173509 fatcat:drmmajenifcz5ltgtebqtfhike


L. He, Z. Wu, Y. Zhang, Z. Hu
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
A remote sensing imagery is first segmented and the object-based hierarchical segmentation tree is built based on initial segmentation objects and merging criteria.  ...  In the experiment, this paper utilized a Worldview-3 image to evaluate the performance, and the results show the validity and the accuracy of the presented semantic segmentation approach.  ...  In semantic segmentation, the MRF model makes full use of spatial information constraints, which is suitable for spectral and texture processing of remote sensing imagery.  ... 
doi:10.5194/isprs-archives-xliii-b3-2020-75-2020 fatcat:tuw4tjpbpfbnrny2jrikbhzt4u


Z. Guan, J. Yu, T. Feng, A. Li
2012 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
For high spatial resolution Remote Sensing images, it is very important to investigate the transformational methods between background and target characteristics.  ...  Amongst the characteristics of imagery targets, texture is a visual feature that reflects the homogeneity of images and the inner attributes of different objects.  ...  Lopez-Espinoza et al. (2008) presented a method for image classification which was taken by SPOT-5 and TM, based on tree-structured Markov random field (TS-MRF) and a texture energy function (TEF).  ... 
doi:10.5194/isprsarchives-xxxix-b7-393-2012 fatcat:lgypxcqkpbejldv5y6pdp4boie

Page 97 of Journal of Research and Practice in Information Technology Vol. 26, Issue 3 [page]

1994 Journal of Research and Practice in Information Technology  
with MRF models.  ...  This combination would be given a very low probability of occurrence via the MRF model.  ... 

An object-based convolutional neural network (OCNN) for urban land use classification

Ce Zhang, Isabel Sargent, Xin Pan, Huapeng Li, Andy Gardiner, Jonathon Hare, Peter M. Atkinson
2018 Remote Sensing of Environment  
Two CNNs with 152 different model structures and window sizes were applied to analyse and label these two kinds 153 of objects, and a rule-based decision fusion was undertaken to integrate the models for  ...  In this paper, a novel 19 object-based convolutional neural network (OCNN) is proposed for urban land use 20 classification using VFSR images.  ...  Acknowledgements 790 This research was funded by PhD studentship "Deep Learning in massive area, multi-scale 791 resolution remotely sensed imagery" (NO. EAA7369), sponsored by Ordnance Survey and  ... 
doi:10.1016/j.rse.2018.06.034 fatcat:te2vvor5dzcc3mrm7l4stghk4a

Table of contents

2021 IEEE Geoscience and Remote Sensing Letters  
Finally, cumulative MRF, as a variant of MRF, is used to further refine the segmentation result through combining original HSI information.  ...  Clausi 162 A Multiscale Deep Learning Approach for High-Resolution Hyperspectral Image Classification ........................ ..........................................................................  ... 
doi:10.1109/lgrs.2020.3044100 fatcat:ko5xpzj27bcilbwaqtwayjqxcu

2014 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 7

2014 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., Bronstert, A., and Foerster, S  ...  A., +, JSTARS April 2014 1103-1115 Regional Grain Yield Response to Climate Change in China: A Statistic Modeling Approach.  ...  ., +, JSTARS April 2014 1142-1156 Regional Grain Yield Response to Climate Change in China: A Statistic Modeling Approach.  ... 
doi:10.1109/jstars.2015.2397347 fatcat:ib3tjwsjsnd6ri6kkklq5ov37a

Junction-aware extraction and regularization of urban road networks in high-resolution SAR images

M. Negri, P. Gamba, G. Lisini, F. Tupin
2006 IEEE Transactions on Geoscience and Remote Sensing  
A general processing framework for urban road network extraction in high-resolution synthetic aperture radar images is proposed.  ...  It is based on novel multiscale detection of street candidates, followed by optimization using a Markov random field description of the road network.  ...  This new definition adds flexibility to the approach and improves its ability to detect potential junctions that may split segments. 2) MRF Model Optimization: Following the modified MRF model discussed  ... 
doi:10.1109/tgrs.2006.877289 fatcat:fge2brwclrbxldydsgrivjzmkm

Multimodal Fusion of Mobility Demand Data and Remote Sensing Imagery for Urban Land-Use and Land-Cover Mapping

Martina Pastorino, Federico Gallo, Angela Di Febbraro, Gabriele Moser, Nicola Sacco, Sebastiano B. Serpico
2022 Remote Sensing  
After a discussion on the function of mobility demand data, a probabilistic fusion framework is developed to take advantage of remote sensing and transport data, and their joint use for urban land-use  ...  Two different methods are proposed within this framework, the first based on pixelwise probabilistic decision fusion and the second on the combination with a region-based multiscale Markov random field  ...  Acknowledgments: The authors would like to thank Chiara Tacconi and Maria Pia Tuscano for their help with the implementation.  ... 
doi:10.3390/rs14143370 fatcat:oe4hnryvg5gdflp6zm3gc3kpd4
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