The convolutional neural network (CNN) has achieved great success in the field of scene classification. Nevertheless, strong spatial information in CNN and irregular repetitive patterns in synthetic aperture radar (SAR) images make the feature descriptors less discriminative for scene classification. Aiming at providing more discriminative feature representations for SAR scene classification, a generalized compact channel-boosted high-order orderless pooling network (GCCH) is proposed. The GCCH network includes four parts, namely the standard convolution layer, second-order generalized layer, squeeze and excitation block, and the compact high-order generalized orderless pooling layer. Here, all of the layers are trained by back-propagation, and the parameters enable end-to-end optimization. First of all, the second-order orderless feature representation is acquired by the parameterized locality constrained affine subspace coding (LASC) in the second-order generalized layer, which cascades the first and second-order orderless feature descriptors of the output of the standard convolution layer. Subsequently, the squeeze and excitation block is employed to learn the channel information of parameterized LASC statistic representation by explicitly modelling interdependencies between channels. Lastly, the compact high-order orderless feature descriptors can be learned by the kernelled outer product automatically, which enables low-dimensional but highly discriminative feature descriptors. For validation and comparison, we conducted extensive experiments into the SAR scene classification dataset from TerraSAR-X images. Experimental results illustrate that the GCCH network achieves more competitive performance than the state-of-art network in the SAR image scene classification task.
Sparse Robust Filters for scene classification of Synthetic Aperture Radar (SAR) images
