NDLI: Selecting optimal bands for sub-pixel target detection in hyperspectral images based on implanting synthetic targets

Content Provider	IET Digital Library
Author	Hashjin, Shahram Sharifi Boloorani, Ali Darvishi Khazai, Safa Kakroodi, Ata Abdollahi
Abstract	Target detection at sub-pixel abundances is, in fact, one of the challenging issues of hyperspectral image processing. Selection of optimal bands to improve sub-pixel target detection (STD) performance is one of the common solutions, applied by many researchers. Nevertheless, the absence of sufficient training data is the main weakness of selecting optimal bands with regard to this approach. The present research introduces a new band selection method for STD in hyperspectral images, based on creating training data, in which the desired target spectrum is implanted randomly in a series of host pixels from the entire hyperspectral image. Afterwards, via running an optimisation algorithm twice, with the aim of minimising the false alarm rate (FAR) in local adaptive coherence estimator target detection algorithm, the number of optimal bands and optimal spectral bands are selected. In this study, the performance of three optimisation methods including the genetic algorithm (GA), Grey Wolf optimisation (GWO), and particle swarm optimisation (PSO) are compared. Experimental results on HyMap and Hyperion datasets show that the proposed method obtains the minimum FAR compared with the rest of the evaluated methods. Also, based on the results obtained, GWO outperforms GA and PSO optimisation methods in the STD domain.
Starting Page	323
Ending Page	331
Page Count	9
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 2, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5324
Journal	IET Image Processing
Publisher Date	2018-10-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Band Selection Method Computer Vision And Image Processing Technique Genetic Algorithm Geophysical Image Processing Grey Wolf Optimisation Hyperspectral Image Hyperspectral Image Processing Learning in AI Local Adaptive Coherence Estimator Target Detection Algorithm Object Detection Optical, Image And Video Signal Processing Optimal Band Optimal Spectral Band Optimisation Technique Particle Swarm Optimisation Spectral Analysis Statistical Analysis Statistics STD Domain Sub-pixel Abundances Sub-pixel Target Detection Performance Synthetic Targets Target Spectrum Training Data
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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