High-Resolution SAR Image Classification Using Subspace Wavelet Encoding Network

The feature learning methods based on convolutional neural networks (CNNs) have produced tremendous achievements in high-resolution (HR) synthetic aperture radar (SAR) image classification. However, the inherent speckle noise could weaken the effectiveness of the convolutional feature statistics. To effectively characterize the features of SAR land-covers under speckle noise, we propose a subspace wavelet encoding network (SWENet) trainable end-to-end and based on an encoder-decoder architecture for modeling the robust feature statistics in individual feature subspaces. We introduce a subspace encoder block at the end of the encoder stage and divide the entire feature space into a set of subspaces; the second-order statistics of all subspaces are concatenated. Then, the wavelet pooling block, suppressing the noise and keeping the structures of learned features well, decomposes the features into low-frequency (storing the basic object structures) and high-frequency components by Haar wavelet layer (HWL), and this block reconstructs the processed components using inverse IHWL during the upsampling stage. Especially, the wavelet pooling block is defined in each subspace for powerful feature learning. Experimental results on a TerraSAR-X image classification dataset suggest that our proposed SWENet yields a performance boost over its competitors.