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      Wishart Deep Stacking Network for Fast POLSAR Image Classification.

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          Abstract

          Inspired by the popular deep learning architecture, deep stacking network (DSN), a specific deep model for polarimetric synthetic aperture radar (POLSAR) image classification is proposed in this paper, which is named Wishart DSN (W-DSN). First of all, a fast implementation of Wishart distance is achieved by a special linear transformation, which speeds up the classification of POLSAR image and makes it possible to use this polarimetric information in the following neural network (NN). Then, a single-hidden-layer NN based on the fast Wishart distance is defined for POLSAR image classification, which is named Wishart network (WN) and improves the classification accuracy. Finally, a multi-layer NN is formed by stacking WNs, which is in fact the proposed deep learning architecture W-DSN for POLSAR image classification and improves the classification accuracy further. In addition, the structure of WN can be expanded in a straightforward way by adding hidden units if necessary, as well as the structure of the W-DSN. As a preliminary exploration on formulating specific deep learning architecture for POLSAR image classification, the proposed methods may establish a simple but clever connection between POLSAR image interpretation and deep learning. The experiment results tested on real POLSAR image show that the fast implementation of Wishart distance is very efficient (a POLSAR image with 768 000 pixels can be classified in 0.53 s), and both the single-hidden-layer architecture WN and the deep learning architecture W-DSN for POLSAR image classification perform well and work efficiently.

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          Author and article information

          Journal
          IEEE Trans Image Process
          IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
          Institute of Electrical and Electronics Engineers (IEEE)
          1941-0042
          1057-7149
          Jul 2016
          : 25
          : 7
          Article
          10.1109/TIP.2016.2567069
          28113713

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