PATCH-BASED SAR IMAGE CLASSIFICATION: THE POTENTIAL OF MODELING THE STATISTICAL DISTRIBUTION OF PATCHES WITH GAUSSIAN MIXTURES - Archive ouverte HAL Access content directly
Conference Papers Year : 2015

PATCH-BASED SAR IMAGE CLASSIFICATION: THE POTENTIAL OF MODELING THE STATISTICAL DISTRIBUTION OF PATCHES WITH GAUSSIAN MIXTURES

Loïc Denis
Florence Tupin

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.
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Dates and versions

hal-01158555 , version 1 (01-06-2015)

Identifiers

  • HAL Id : hal-01158555 , version 1

Cite

Sonia Tabti, Charles-Alban Deledalle, Loïc Denis, Florence Tupin. PATCH-BASED SAR IMAGE CLASSIFICATION: THE POTENTIAL OF MODELING THE STATISTICAL DISTRIBUTION OF PATCHES WITH GAUSSIAN MIXTURES. IGARSS, Jul 2015, Milan, Italy. ⟨hal-01158555⟩
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