1. PolSAR image classification based on multi-scale stacked sparse autoencoder.
- Author
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Zhang, Lu, Jiao, Licheng, Ma, Wenping, Duan, Yiping, and Zhang, Dan
- Subjects
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SYNTHETIC apertures , *POLARIMETRY , *SYNTHETIC aperture radar , *DEEP learning , *FEATURE extraction , *CLASSIFICATION - Abstract
Recently, many deep learning methods are applied with the spatial information to learn features for polarimetric synthetic aperture radar (PolSAR) image classification. However, without considering the multi-scale information, the classification performance of these methods are limited. Hence, this paper proposes a multi-scale feature extraction method based on stacked sparse autoencoder (SSAE), named the multi-scale SSAE (MS-SSAE), to improve the classification performance. This method extracts the deep multi-scale features by a two-stage framework. In the first stage, the SSAE uses training data at different scales to extract the multi-scale features. Then, a 1-D average pooling strategy is proposed to reduce the feature dimensionality at the second stage. Therefore, the MS-SSAE can capture discriminative multi-scale features. The experimental results certify that the MS-SSAE can not only improve the classification accuracy, but also remain the details in the image. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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