Hyperspectral anomaly detection:a performance comparison of existing techniques  

作　　者：Noman Raza Shah Abdur Rahman M.Maud Farrukh Aziz Bhatti Muhammad Khizer Ali Khurram Khurshid Moazam Maqsood Muhammad Amin 

机构地区：[1]iVISION Lab,Electrical Engineering Department,Institute of Space Technology,Islamabad,Pakistan [2]AI Enabling Technologies Lab,Center of Excellence in Artificial Intelligence,Bahria University,Islamabad,Pakistan [3]Department of Electrical and Computer Engineering,Pak-Austria Fachhochschule:Institute of Applied Sciences and Technology,Haripur,Pakistan [4]Avionics Engineering Department,Institute of Space Technology,Islamabad,Pakistan

出　　处：《International Journal of Digital Earth》2022年第1期2078-2125,共48页国际数字地球学报（英文）

基　　金：supported by Pakistan Space and Upper Atmosphere Research Commission[grant number NSP-654-20].

摘　　要：Anomaly detection in Hyperspectral Imagery(HSI)has received considerable attention because of its potential application in several areas.Numerous anomaly detection algorithms for HSI have been proposed in the literature;however,due to the use of different datasets in previous studies,an extensive performance comparison of these algorithms is missing.In this paper,an overview of the current state of research in hyperspectral anomaly detection is presented by broadly dividing all the previously proposed algorithms into eight different categories.In addition,this paper presents the most comprehensive comparative analysis to-date in hyperspectral anomaly detection by evaluating 22 algorithms on 17 different publicly available datasets.Results indicate that attribute and edge-preserving filtering-based detection(AED),local summation anomaly detection based on collaborative representation and inverse distance weight(LSAD-CR-IDW)and local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector(LSUNRSORAD)perform better as indicated by the mean and median values of area under the receiver operating characteristic(ROC)curves.Finally,this paper studies the effect of various dimensionality reduction techniques on anomaly detection.Results indicate that reducing the number of components to around 20 improves the performance;however,any further decrease deteriorates the performance.

关 键 词：Anomaly detection algorithms hyperspectral imagery deep learning dimensionality reduction 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]