Vector tunnel algorithm for hyperspectral target detection

Abstract

In this study, targets and nontargets in a hyperspectral image are characterized in terms of their spectral features. Target detection problem is considered as a two-class classification problem. For this purpose, a vector tunnel algorithm (VTA) is proposed. The vector tunnel is characterized only by the target class information. Then, this method is compared with Euclidean Distance (ED), Spectral Angle Map (SAM) and Support Vector Machine (SVM) algorithms. To obtain the training data belonging to target class, the training regions are selected randomly. After determination of the parameters of the algorithms with the training set, detection procedures are accomplished at each pixel as target or background. Consequently, detection results are displayed as thematic maps.The algorithms are trained with the same training sets, and their comparative performances are tested under various cases. During these studies, various levels of thresholds are evaluated based on the efficiency of the algorithms by means of Receiver Operating Characteristic Curves (ROC) as well as visually.

Improving hyperspectral sub-pixel target detection in multiple target signatures using a revised replacement signal model

“…Generally, an endmember is defined as the pure signature of an object. In practical experiences obtaining such knowledge of the endmember signatures may not be possible (Demirci et al, 2014). Moreover, there are four main categories of target detection approaches, including spectral angle mapper (also known as SAM) (Richards & Jia, 2006), matched filter (also known as MF) , adaptive cosine estimator (also known as ACE) (Kraut et al, 2005), and constrained energy minimization (also known as CEM) (Settle, 1996).…”