@inproceedings{cc68feaf68df42cd8833f0c864cc5b21,
title = "Patch-based SAR image classification: The potential of modeling the statistical distribution of patches with Gaussian mixtures",
abstract = "Due to their coherent nature, SAR (Synthetic Aperture Radar) images are very different from optical satellite images and more difficult to interpret, especially because of speckle noise. Given the increasing amount of available SAR data, efficient image processing techniques are needed to ease the analysis. Classifying this type of images, i.e., selecting an adequate label for each pixel, is a challenging task. This paper describes a supervised classification method based on local features derived from a Gaussian mixture model (GMM) of the distribution of patches. First classification results are encouraging and suggest an interesting potential of the GMM model for SAR imaging.",
keywords = "GMM, SAR images, classification, patches",
author = "Sonia Tabti and Deledalle, \{Charles Alban\} and Loic Denis and Florence Tupin",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 ; Conference date: 26-07-2015 Through 31-07-2015",
year = "2015",
month = nov,
day = "10",
doi = "10.1109/IGARSS.2015.7326286",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2374--2377",
booktitle = "2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings",
}