What is it about?
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. 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. 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.
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Why is it important?
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.
Perspectives
Writing this article was a great pleasure, as it has co-authors with whom I am inspired. This article also leads to a new perspective of analysis of remote sensoring images, having a great potential for new discoveries.
Eduarda Tatiane Caetano Chagas
Universidade Federal de Minas Gerais
Read the Original
This page is a summary of: Analysis and Classification of SAR Textures Using Information Theory, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, January 2021, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/jstars.2020.3031918.
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