A Hyperspectral Image Classification Method Based on Pyramid Feature Extraction With Deformable–Dilated Convolution
Jinghui Yang, Anqi Li, Jinxi Qian, Qin Jia, Liguo Wang
Abstract
In recent years, deep learning methods, especially convolutional neural networks (CNNs), have been gradually applied to the field of hyperspectral image (HIS) classification. Because the receptive fields of standard convolution are regular, fixed, and limited, CNNs usually only tend to focus on local formations, which cannot fully reflect the complex information in HSIs. To address the above issue, a novel HSI classification method based on pyramid feature extraction with deformable–dilated convolution (PD2C) is proposed. First, a pyramid feature extraction (PFE) model based on a multiscale double-branch module with deformable–dilated convolution (MDBD2) and a deformable downsampling module is proposed to extract local features. Second, transformer is used to extract global features. On this basis, complex information is well utilized for classification. Experiments on three public datasets show that the proposed PD2C method achieves optimal classification results compared with other state-of-the-art classification methods.