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Multilevel Local Pattern Histogram for SAR Image Classification

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3 Author(s)
Dengxin Dai ; School of Electronic Information and the State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China ; Wen Yang ; Hong Sun

In this letter, we propose a theoretically and computationally simple feature for synthetic aperture radar (SAR) image classification, the multilevel local pattern histogram (MLPH). The MLPH describes the size distributions of bright, dark, and homogenous patterns appearing in a moving window at various contrasts; these patterns are the elementary properties of SAR image texture. The MLPH is a very powerful descriptor of SAR images because it captures both local and global structural information. Additionally, it is robust to speckle noise. Experiments on a TerraSAR-X data set demonstrate that MLPH significantly outperforms four other widely used features in SAR image classification.

Published in:

IEEE Geoscience and Remote Sensing Letters  (Volume:8 ,  Issue: 2 )