PolSAR image classification based on multi-scale stacked sparse autoencoder

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...

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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 351; S. 167 - 179
Hauptverfasser: Zhang, Lu, Jiao, Licheng, Ma, Wenping, Duan, Yiping, Zhang, Dan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 25.07.2019
Schlagworte:
Deep learning
Feature learning
Multi-scale
Polsar classification
Stacked sparse autoencoder
Deep learning
Feature learning
Multi-scale
Polsar classification
Stacked sparse autoencoder
ISSN:0925-2312, 1872-8286
Online-Zugang:Volltext
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Zusammenfassung: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2019.03.024