Multiscale Adaptive Convolution for Hyperspectral Image Classification

Convolutional neural network (CNN) is widely used in hyperspectral image (HSI) classification owing to their advantages of spatial-spectral features capture capability and learning depth features as well as their structural flexibility. Nevertheless, the shape of the convolution kernel is fixed, a limitation that leads to shape fixation when modeling different features in CNN, especially in the edge regions between classes. A multiscale adaptive convolution (MSAC) model is proposed in this article to overcome this shortcoming. Combining superpixels with the traditional convolutional kernels to form adaptive kernels automatically adjusts the receptive field, suppresses edge noise, and enhances feature learning for different classes. On the basis of adaptive convolution, adaptive convolution units (AConvUs) are constructed. The hierarchical residual structure is constructed by superimposing multiple AConvUs to learn the spatial spectral features of different receptive fields of the HSI, reduce the gradient disappearance and enhance the robustness. The proposed multiscale convolution adjusts the shape of the convolution kernel according to the spatial distribution of different superpixels in HSI. Finally, the MSAC classification framework is constructed by the decision fusion of multiscale adaptive convolution at different superpixel scales, which helps to extract the complementary information of the HSI. The MSAC method’s experimental performance on several HSI datasets, including Indian Pines, University of Pavia, Salinas and Gaofen-5, to verify the validity and practically.