PATCH-BASED SAR IMAGE CLASSIFICATION: THE POTENTIAL OF MODELING THE STATISTICAL DISTRIBUTION OF PATCHES WITH GAUSSIAN MIXTURES
Language
en
Communication dans un congrès
This item was published in
IGARSS, 2015-07-27, Milan.
English 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 ...Read more >
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.Read less <
English Keywords
Index Terms— SAR images
patches
classification
GMM
Origin
Hal imported