$C^{2}N^{2}$: Complex-Valued Contourlet Neural Network

Le résumé fourni par la source

Complex-valued convolutional neural networks (CV-CNN) have recently gained recognition in feature representation learning. It implements the repeated application of the operations in convolution, local average pooling, and the absolute value of the resulting vectors. However, it is only conducted in the complex spatial domain, and lacks effective representation of directionality, singularity and regularity in the complex spectral domain for anomaly detection of images. This is the key to feature learning representation of high-order singularity. To solve this problem, a complex-valued contourlet neural network (C$^{2}$N$^{2}$) is proposed in this paper. It is novel in this sense that, different from the CV-CNN in the spatial domain, the spectral stream of C$^{2}$N$^{2}$can enhance the multi-resolution sparse representation of nonsubsampled contourlet (NSCT) with multi-scales and multi-directions for images. Furthermore, the spectral feature integration module (SFIM) is proposed to capture the statistical properties of the NSCT coefficients. It is shown that the proposed network can improve the distinguishability of feature learning, and classification ability in theoretical analysis and experiments on three benchmark datasets (Flevoland, Xi'an, and Germany) compared with developed methods. PolSAR image classification is widely used in the fields of agriculture, forestry and military. It must be emphasized that there is potential in effective feature learning representation and the generalization capability of C$^{2}$N$^{2}$in deep learning, recognition and interpretation.

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