Hyperspectral Target Detection Using Neural Networks

Abstract

Artificial neural networks are designed for classic classification problem, which is different than our goal of target detection. The objective of this paper is to develop an algorithm, based on a one-layer neural network, and assess its performance and utility as a target detection algorithm to detect a subpixel target in a hyperspectral image. The weights are estimated by maximizing the likelihood function of the output variable and are solved numerically using the gradient descent method with a variable step size based on the Lipschitz's constant for the objective function. Experimental results using hyperspectral data are presented so as to assess the performance of the proposed algorithm. Results demonstrated that a single-layer neural network, implemented using the gradient descent method with a variable step size, can detect subpixel objects in hyperspectral imagery.