基于特征融合方法的高光谱图像分类综述

高光谱图像中包含丰富的光谱特征和空间特征,这对地表物质的分类至关重要。然而高光谱图像的空间分辨率相对较低,使得图像中存在大量的混合像素,这严重制约物质分类的精度。受到观测噪声、目标区域大小及端元易变性等因素的影响,使得高光谱图像的分类仍然面临诸多挑战。随着人工智能和信息处理技术的不断进步,高光谱图像分类已成为遥感领域的一个热点问题。首先对基于特征融合的高光谱图像分类文献进行系统综述,并对几种分类策略进行分析与比较,然后介绍高光谱图像分类的发展现状及面临的相应问题,最后提出一些可以提高分类性能的策略,从而为课题的技术研究提供指导和帮助。

Review of Hyperspectral Image Classification Based on Feature Fusion Method

The hyperspectral image contains rich spectral and spatial features,which are essential for the classification of surface materials.However,the spatial resolution of hyperspectral images is relatively low,resulting in a large number of mixed pixels in the image,which severely restricts the accuracy of substance classification.Affected by factors such as observation noise,target area size,and endmember variability,hyperspectral image classification still faces many challenges.With the continuous progress of artificial intelligence and information processing technology,hyperspectral image classification has become a hot issue in the field of remote sensing.First,the literature on hyperspectral image classification based on feature fusion is systematically reviewed,and several classification strategies are analyzed and compared.Then,the development status of hyperspectral image classification and the corresponding problems are introduced.Finally,some suggestions can improve the classification performance are proposed,which provide guidance and assistance for the technical research of the subject.