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10 November 2004 Information-theoretic textural features of SAR images: an assessment for land cover classification
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In this work, two feature, calculable from SAR images on a per-pixel basis, but relying on global image statistics, are described and discussed. The rationale is that spatial heterogeneity is regarded as uncertainty, that is unpredictability of a sample feature, e.g., the square root of local variance, from another pixel feature, like the local mean. Thus, such an uncertainty can be measured by resorting to Shannon's Information Theory in a mathematically rigorous and physically consistent manner. Starting from the multiplicative noise and texture models peculiar of SAR imagery, the conditional information of square root of estimated local variance to local mean has been found to be a powerful heterogeneity measurement, very little sensitive to the noise, and thus capable of capturing subtle variations of backscatter and texture whenever they are embedded in a heavy speckle. On the other side, the joint information of standard deviation to mean, although not strictly a heterogeneity feature, can be used as a textural feature for automated segmentation and classification, thanks to its noise-insensitiveness and to its capability of highlighting man-made structures. Experimental results carried out on C-band SIR-C and X-band X-SAR data of the city of Pavia, in Italy, demonstrate that the proposed features are useful for automated segmentation and classification tasks. Promising results are obtained in terms of discrimination of urban and suburban areas with different degrees of building density. Furthermore, the additional capabilities stemming from the joint use of X-band data, analogous to those available after the launch of the upcoming COSMO/SkyMed mission, are highlighted and discussed.
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Bruno Aiazzi, Luciano Alparone, Stefano Baronti, and Massimo Bianchini "Information-theoretic textural features of SAR images: an assessment for land cover classification", Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004);

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