Abstract
Traditional detectors for hyperspectral imagery (HSI) target detection (TD) output the result after processing the HSI only once. However, using the prior target information only once is not sufficient, as it causes the inaccuracy of target extraction or the unclean separation of the background. In this paper, the target pixels are located by a hierarchical background separation method, which explores the relationship between the target and the background for making better use of the prior target information more than one time. In each layer, there is an angle distance (AD) between each pixel spectrum in HSI and the given prior target spectrum. The AD between the prior target spectrum and candidate target ones is smaller than that of the background pixels. The AD metric is utilized to adjust the values of pixels in each layer to gradually increase the separability of the background and the target. For making better discrimination, the AD is calculated through the whitened data rather than the original data. Besides, an elegant and ingenious smoothing processing operation is employed to mitigate the influence of spectral variability, which is beneficial for the detection accuracy. The experimental results of three real hyperspectral images show that the proposed method outperforms other classical and recently proposed HSI target detection algorithms.
Highlights
A hyperspectral remote sensing system uses sensors to collect the energy reflected by ground materials in a wide electromagnetic band range, producing hyperspectral imagery (HSI) with abundant spectral information [1,2]
It describes the relationship between the probability of detection (Pd) and the false alarm rate (FAR)
We proposed a Target detection (TD) method based on the angle distance (AD) metric with a hierarchical structure
Summary
A hyperspectral remote sensing system uses sensors to collect the energy reflected by ground materials in a wide electromagnetic band range, producing hyperspectral imagery (HSI) with abundant spectral information [1,2]. Some scholars have studied this problem and have proposed various methods, such as the extracting method-based endmember extraction (EE) [34,35], adaptive weighted learning method using a self-completed background dictionary (AWLM_SCBD) [36], and target signature optimization based on sparse representation [33] These methods improve the accuracy of detection results by finding more accurate target spectra. It is very rough in directly measuring the spectral AD between the tested pixel and the prior target as a basis for judging whether it is the target. The proposed method adopts a hierarchical architecture and combines the AD metric in whitened space between the spectra of tested pixels and the prior target to gradually separate the targets and backgrounds. FiFgiugruere2.2(.a()aT) Thehescshcehmemataitcicddiaigargarmamofofththeespspecetcrtarlaal nanglgelemmapapperer(S(ASAMM).)(.b(b) )IlIllulustsrtartaitoinonofofththeeeffefefcetct ofotfhteheadaadpatpivtievewwhihteitneinnigngprporcoecsessosnodnadtaa.taμ.0 μa0ndanμd reμp rerseepnrtesthenettathrgeettaargnedt tahnedbtahcekgbraocukngdroiunntdhein orthigeinoarligdiantalsdpatcae,srpeascpee,ctrievsepleyc. tμiv0ealyn.d μμ 01 raenpdresμe 1ntrtehperetsaernget ttahnedtathrgeebtaacnkdgrtohuendbaicnktghreouwnhditeinetdhe dawtahistpeanceed, rdeastpaecstpivaecely,.rTehspeerectiisvaelliya.siTnhgeirne tihseaoliraisgiinngalidnatthaespoaricgeinonalthdeatpaesrpspaeccetiovne othfeanpgelrespdeiscttaivneceo, f anadngtlheedailsitaasnincge,pahnedntohme eanlioansinisgapllheveniaotmedeninown hisitaelnleevdiadtaetdaisnpwachei.tened data space
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
