Hyperspectral image classification with a dense multi-scale residual network

Abstract

Hyperspectral images (HSIs) contain spatial features and rich spectral features that provide them with great advantages in target classification and make it easy to improve image classification accuracy. Convolutional neural networks (CNNs) have shown good performance in HSI classification. However, blindly increasing the depth of the CNNs may lead to overfitting. A HSI classification method based on a dense multi-scale residual network is proposed to address these two problems. The proposed framework obtains the spectral-spatial characteristics of HSIs through an improved multi-scale residual network. Then, three cascaded multi-scale residual modules form a deep network. The dense connection module is used to stack feature maps from all previous layers to form the concatenate feature map rather than fusing pixels of these feature maps and further achieve the purpose of improving classification accuracy and reducing the time consumption of the network. A series of experiments show that the proposed method achieves good experimental results on three widely used hyperspectral datasets and a new hyperspectral dataset (Farmland distribution dataset, FDD).