A Wavelet-Driven Subspace Basis Learning Network for High-Resolution Synthetic Aperture Radar Image Classification

Abstract

The feature learning strategy of convolutional neural networks learns the deep spatial features from high-resolution (HR) synthetic aperture radar (SAR) images while ignoring the speckle noise based on the SAR imaging mechanism. In the feature learning module, the noise reduction by feature-adaptive projection guided by a powerful embedded wavelet feature reconstruction mechanism can effectively learn the deep feature statistics. In this article, we present a wavelet-driven subspace basis learning network (WDSBLN), following an encoder–decoder architecture, for the HR SAR image classification. The powerful wavelet module, including wavelet decomposition and reconstruction, is employed for keeping the structures of learned features well under speckle noise. Specifically, a compact second-order feature enhancement mechanism is designed for improving the contour and edge information of low-frequency components in the feature decomposition stage, and a local feature attention module based on the point-wise convolutional layer is adopted to aggregate the contextual information of the local channel and reserves detail information in the high-frequency components. Then, the reconstructed feature map is employed as a guided standard in the subspace basis learning (SBL) module. The SBL module, including basis generation (generating the subspace basis vectors) and subspace projection (transforming deep feature maps into a signal subspace), maintains the local structure of HR SAR image patches and acquires the robust feature statistics. We conduct evaluations on three real HR SAR image classification datasets, achieving superior performances as compared to other related networks.

Keywords

• Feature decomposition
• feature learning
• image classification
• speckle noise
• synthetic aperture radar (SAR)