光学学报, 2019, 39 (10): 1028002, 网络出版: 2019-10-17
基于双通道GAN的高光谱图像分类算法 下载: 1990次
Hyperspectral Image Classification Algorithm Based on Two-Channel Generative Adversarial Network
遥感 高光谱图像 分类 空谱联合特征 生成式对抗网络 remote sensing hyperspectral image classification spatial-spectral features generative adversarial network
摘要
高光谱图像分类问题是高光谱遥感图像处理问题中的研究基础,它的主要目的是根据高光谱遥感图像中的光谱信息和空间信息将图像中的每个像元划分为不同的地物类别[
Abstract
The existing hyperspectral image generative adversarial network(GAN) classification algorithm cannot fully extract spectral and spatial-spectral features, which leads to the degradation of hyperspectral image classification accuracy. To resolve this issue, this study proposes a hyperspectral image classification algorithm based on a two-channel GAN. Improved one- and two-dimensional GAN classification frameworks are used to extract complete spectral and spatial-spectral features, respectively. Those features are nonlinearly fused to form a more comprehensive spatial-spectral features for classification. The experiments on two commonly used hyperspectral image datasets show that the proposed algorithm achieves the best classification accuracy; further, the results verify the effectiveness and advantages of the proposed algorithm.
毕晓君, 周泽宇. 基于双通道GAN的高光谱图像分类算法[J]. 光学学报, 2019, 39(10): 1028002. Xiaojun Bi, Zeyu Zhou. Hyperspectral Image Classification Algorithm Based on Two-Channel Generative Adversarial Network[J]. Acta Optica Sinica, 2019, 39(10): 1028002.