考虑局部密度的共享近邻高光谱图像波段选择方法  

Method of Shared Nearest Neighbors in Hyperspectral Image Band Selection Considering Local Density

作　　者：王宇轩 孙晓兵[1] 提汝芳 黄红莲 刘晓 Wang Yuxuan;Sun Xiaobing;Ti Rufang;Huang Honglian;Liu Xiao(Key Laboratory of General Optical Calibration and Characterization Technology,Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei 230031,Anhui,China;University of Science and Technology of China,Hefei 230026,Anhui,China)

机构地区：[1]中国科学院合肥物质科学研究院通用光学定标与表征技术重点实验室,安徽合肥230031 [2]中国科学技术大学,安徽合肥230026

出　　处：《光学学报》2024年第24期248-258,共11页Acta Optica Sinica

基　　金：航天科技创新应用研究项目(E23Y0H555S1);航空科技创新应用研究项目(62502510201);高分辨率对地观测系统重大专项(30-Y20A010-9007-17/18);高精度温室气体综合探测卫星云和气溶胶成像仪处理算法(E13Y0J31601);中国资源卫星数据与应用中心项目(HFWZ2020080302)。

摘　　要：高光谱图像提供了数百个连续的光谱测量,从这些信息繁多的通道中筛选特征鲜明且相互独立的波段子集成为一个重要问题。近年来,尽管有许多波段选择方法,但大多数方法只关注波段的信息熵或已被挑选波段间的冗余性。为了综合考虑波段间的冗余性和信息熵,提出了一种考虑波段共享近邻的高光谱波段选择方法。该方法包括子空间划分和权重排名两个部分。通过波段的共享近邻,将空间预分割点优化到适当的位置,以最大化不同子空间之间的差异。在波段选择阶段,综合考虑了局部密度、信息熵和信噪比,以选择出最优的波段子集。在3个公开数据集上进行大量对比实验,证明了该方法在精度和效率方面的显著提升。Objective Hyperspectral image provides hundreds of continuous spectral measurements,and selecting a subset of bands with distinct and independent features from these numerous channels is a crucial problem.In recent years,although scholars have proposed many methods for band selection,most of these methods only focus on the information content of the bands or the redundancy between the selected bands.To comprehensively consider the redundancy and information entropy between bands,we propose a hyperspectral band selection method that considers bandsharing neighbors.This method consists of two parts:subspace partitioning and weight ranking.By sharing neighbors between bands,we optimize the spatial presegmentation points to appropriate positions,to maximize the differences between different subspaces.In the band selection stage,we comprehensively consider factors such as local density,information entropy,and signaltonoise ratio to select the optimal band subset.Through extensive comparative experiments on three public datasets,we demonstrate a significant improvement in accuracy and efficiency with this method.Methods This paper presents a shared nearest neighbor band selection method based on local density,enabling rapid band selection for hyperspectral images while maintaining accuracy.Specifically,the proposed method comprises two steps:subspace partitioning and comprehensive weighted ranking.During subspace partitioning,the method first prepartitions the hyperspectral image bands into subspaces by evenly dividing them.It then dynamically adjusts the interval points between subspaces by considering the correlation of shared nearest neighbors among the central bands of each subspace.After completing the subspace partitioning,the method comprehensively considers local density,information entropy,and signaltonoise ratio to select the optimal subset of bands.Compared to other band selection methods,the approach proposed in this paper has two main advantages.First,subspace partitioning does not require multiple iterati

关 键 词：高光谱成像 波段选择 共享近邻 局部密度 

分 类 号：P2[天文地球—测绘科学与技术]