Dual-Channel Convolution Network With Image-Based Global Learning Framework for Hyperspectral Image Classification
Haoyang Yu, Hao Zhang, Yao Liu, Ke Zheng, Zhen Xu, Chenchao Xiao
Abstract
Recently, convolutional neural networks (CNNs) have been widely applied to hyperspectral image (HSI) classification due to their detailed representation of features. Nevertheless, the current CNN-based HSI classification methods mainly follow a patch-based learning framework. These methods are nonglobal learning methods, which not only limit the use of global information but also require a high computational cost. In this letter, an image-based global learning framework is introduced to HSI classification. Based on this framework, we propose a dual-channel convolutional network (DCCN) for HSI classification to maximize the exploitation of the global and multiscale information of HSI. The experimental results conducted on two real hyperspectral datasets indicate that our method is superior to other related methods in terms of both efficiency and accuracy for HSI classification.