Hyperspectral Image Classification Based on Kernel-Guided Deformable Convolution and Double-Window Joint Bilateral Filter

Convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification. However, a shape-fixed convolution kernel cannot extract appropriate spatial-spectral features. Thus, we propose a novel two-stage classification method based on kernel-guided deformable convolution networks and double-window joint bilateral filter (KDCDWBF) for HSIs. First, according to the calculated similarity map, the shape of the kernel-guided deformable convolution (KDC) is more consistent with the real shape of land covers, so the KDC can extract more pure neighborhood spatial-spectral information. Then, using the piecewise smoothness property of the HSI, a double-window joint bilateral filter (DWJBF) is designed to complete the coarse-to-fine classification stage, which can solve the misclassification problem of single pixels and small regions. Experiments on two HSI datasets demonstrate that the proposed network can achieve better classification performance when compared with other state-of-the-art methods.

Automated summary

This research proposes a novel two-stage classification method for hyperspectral image classification based on convolutional neural networks, which can achieve more accurate classification results by extracting spatial-spectral features using deformable convolution and joint bilateral filtering.