Synthetic Aperture Radar Scene Classification Using Multiview Cross Correlation Attention Network

Abstract

The second-order pooling manner, exploring higher feature statistics than the first-order pooling, has achieved impressive performance in scene classification. However, the object not only presents similarity but also exhibits diversified singularity on the synthetic aperture radar (SAR) image. These make the second-order pooling approaches to explore the single-view second-order feature statistics less adaptable for SAR scene classification. To solve this issue, an end-to-end training framework based on the multiview cross correlation attention network (MCAN) is proposed. The spatial and channelwise self-attention modules are first employed to model the interdependences between the spatial and channel dimensions of their convolutional features. Subsequently, the global spatial and channelwise covariance pooling layers are drawn into the MCAN, and then, they learn the spatial and channel cross correlations within the feature statistics, respectively. Finally, an iterative matrix square-root normalization layer, which owns the capability to fast computing approximate square root of the covariance matrix, is introduced for making the feature representation more discriminative. Experiments on the SAR data set from the TerraSAR-X images for scene classification demonstrate that the MCAN performs better than other related works.