约束能量最小化变分自编码的高光谱目标检测  

Hyperspectral target detection based on constrained energy minimization variational autoencoder

作　　者：周琨 徐洋 魏洁[1] 吴泽彬[1,2] 韦志辉 ZHOU Kun;XU Yang;WEI Jie;WU Zebin;WEI Zhihui(Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China;Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学计算机科学与工程学院,南京210094 [2]南京理工大学江苏省光谱成像与智能感知重点实验室,南京210094

出　　处：《遥感学报》2024年第1期78-87,共10页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:62071233,61971223,61976117);江苏省自然科学基金(编号:BK20211570,BK20180018,BK20191409);中央高校基金项目(编号:30917015104,30919011103,30919011402,30921011209)。

摘　　要：目标检测是高光谱领域中一个重要的研究方向,高光谱目标检测(hyperspectral target detection)是根据目标的光谱特征将像素判断为背景或者目标。在过去的几十年中已经提出了很多的检测算法,但是高光谱图像中背景样本的复杂性以及目标样本的有限性,使得检测算法面临着很大的挑战。本文提出了一种基于背景重构的高光谱目标检测算法,利用高光谱图像中背景样本占比较大的特点,训练背景样本自表示模型,然后重构出背景。同时利用约束能量最小化对残差图像进行检测,将重构出的背景用于自相关矩阵计算,避免目标样本参与计算影响目标样本的响应能量,提高了检测的精确度。在真实的高光谱图像数据上结果明显优于对比实验,验证了该方法的有效性和高效性。Target detection is an important research direction in the hyperspectral field.Hyperspectral target detection aims to distinguish pixels as background or target according to the spectral characteristics of the target.Several detection algorithms have been proposed in the past few decades.However,the complexity of background samples in hyperspectral images and the limited number of target samples lead to considerable challenges in detection algorithms.A hyperspectral target detection algorithm based on background reconstruction is proposed in this paper.Taking advantage of the large proportion of background samples in hyperspectral images,the self-representation model of the background samples is trained,and then the background is reconstructed.Simultaneously,the constrained energy minimization is used to detect the residual image,and the reconstructed background is used for the calculation of the correlation matrix.Therefore,the target sample is not involved in the calculation to affect the response energy of the target sample,and the detection accuracy is improved.Results on real hyperspectral image data are better than those of comparison experiments,which verify the effectiveness of this method.Obtaining numerous training sets of artificially labeled hyperspectral data is difficult.Therefore,using limited samples to train deep neural networks is the largest difficulty in applying deep learning to hyperspectral target detection.When calculating the average output energy of the background,the calculation of the correlation matrix of all samples is used.Therefore,the target pixel also participates in the calculation,causing a certain degree of damage to the target spectrum.The background is used as a training sample to train the entire network to solve the above problems,and the reconstructed background is utilized for constrained energy minimization detection to reduce the impact on the target spectrum during the detection process.This paper proposes a hyperspectral target detection based on constrained energy m

关 键 词：遥感 高光谱 目标检测 背景重构 约束能量最小化 自相关矩阵 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]