Analysis and Classification of SAR Textures Using Information Theory

The use of Bandt-Pompe probability distributions and descriptors of information theory has been presenting satisfactory results with low computational cost in the time series analysis literature [1]-[3]. However, these tools have limitations when applied to data without time dependency. Given this context, we present a newly proposed technique for texture analysis and classification based on the Bandt-Pompe symbolization for SAR data. It consists of linearizing a 2-D patch of the image using the Hilbert-Peano curve, build an ordinal pattern transition graph that considers the data amplitude encoded into the weight of the edges, obtain a probability distribution function derived from this graph, and compute information theory descriptors (permutation entropy and statistical complexity) from this distribution and use them as features to feed a classifier. The ordinal pattern graph we propose considers that the edges' weight is related to the absolute difference of observations, which encodes the information about the data amplitude. This modification considers the unfavorable signal-to-noise ratio of SAR images and leads to the characterization of several types of textures. Experiments with data from Munich urban areas, Guatemala forest regions, and Cape Canaveral ocean samples show the effectiveness of our technique in homogeneous areas, achieving satisfactory separability levels. The two descriptors chosen in this work are easy and quick to calculate and are used as input for a k-nearest neighbor classifier. Experiments show that this technique presents results similar to state-of-the-art techniques that employ a much larger number of features and, consequently, impose a higher computational cost.

Automated summary

The paper introduces a new technique for texture analysis and classification of SAR data based on Bandt-Pompe symbolization. It linearizes 2-D patches of the image and builds ordinal pattern transition graph to characterize textures, showing effectiveness in homogeneous areas with satisfactory separability levels. The technique uses simple and quick-to-calculate descriptors as features for a k-nearest neighbor classifier, presenting results similar to state-of-the-art techniques with higher computational costs.