Generalized Statistical Complexity of SAR Imagery
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- 作者：Eliana S. de Almeida (1)
Antonio Carlos de Medeiros (1)
Osvaldo A. Rosso (12)
Alejandro C. Frery (1) - 关键词：information theory – ; speckle – ; feature extraction
- 刊名：Lecture Notes in Computer Science
- 出版年：2012
- 出版时间：2012
- 年：2012
- 卷：7441
- 期：1
- 页码：656-663
- 全文大小：5.8 MB
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- ISSN：1611-3349
文摘
A new generalized Statistical Complexity Measure (SCM) was proposed by Rosso et al in 2010. It is a functional that captures the notions of order/disorder and of distance to an equilibrium distribution. The former is computed by a measure of entropy, while the latter depends on the definition of a stochastic divergence. When the scene is illuminated by coherent radiation, image data is corrupted by speckle noise, as is the case of ultrasound-B, sonar, laser and Synthetic Aperture Radar (SAR) sensors. In the amplitude and intensity formats, this noise is multiplicative and non-Gaussian requiring, thus, specialized techniques for image processing and understanding. One of the most successful family of models for describing these images is the Multiplicative Model which leads, among other probability distributions, to the G0\mathcal G^0 law. This distribution has been validated in the literature as an expressive and tractable model, deserving the “universal” denomination for its ability to describe most types of targets. In order to compute the statistical complexity of a site in an image corrupted by speckle noise, we assume that the equilibrium distribution is that of fully developed speckle, namely the Gamma law in intensity format, which appears in areas with little or no texture. We use the Shannon entropy along with the Hellinger distance to measure the statistical complexity of intensity SAR images, and we show that it is an expressive feature capable of identifying many types of targets.