No 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, absence of sufficient training data is the main weakness of selecting optimal bands with regards 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. Afterward, via running an optimization algorithm twice, with the aim of minimizing the false alarm rate (FAR) in local Adaptive Coherence Estimator (ACE) target detection algorithm, the number of optimal bands and optimal spectral bands are selected. In this study, the performance of three optimization methods, including Genetic Algorithm (GA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO), are compared. Experimental results on Hymap and Hyperion datasets show that, the proposed method obtains the minimum FAR compared to the rest of evaluated methods. Also, based on the results obtained, GWO outperforms GA and PSO optimization methods in the STD domain.

ABSTRACT:In recent years, developing target detection algorithms has received growing interest in hyperspectral images. In comparison to the classification field, few studies have been done on dimension reduction or band selection for target detection in hyperspectral images. This study presents a simple method to remove bad bands from the images in a supervised manner for sub-pixel target detection. The proposed method is based on comparing field and laboratory spectra of the target of interest for detecting bad bands. For evaluation, the target detection blind test dataset is used in this study. Experimental results show that the proposed method can improve efficiency of the two well-known target detection methods, ACE and CEM.

No abstract