Two Linear Unmixing Algorithms to Recognize Targets Using Supervised Classiﬁcation and Orthogonal Rotation in Airborne Hyperspectral Images
The goal of the paper is to detect pixels that contain targets of known spectra. The target can be present in a sub- or above pixel. Pixels without targets are classiﬁed as background pixels. Each pixel is treated via the content of its neighborhood. A pixel whose spectrum is diﬀerent from its neighborhood is classiﬁed as a “suspicious point”. In each suspicious point there is a mix of target(s) and background. The main objective in a supervised detection (also called “target detection”) is to search for a speciﬁc given spectral material (target) in hyperspectral imaging (HSI) where the spectral signature of the target is known a priori from laboratory measurements. In addition, the fractional abundance of the target is computed. To achieve this we present two linear unmixing algorithms that recognize targets with known (given) spectral signatures. The CLUN is based on automatic feature extraction from the target's spectrum. These features separate the target from the background. The ROTU algorithm is based on embedding the spectra space into a special space by random orthogonal transformation and on the statistical properties of the embedded result. Experimental results demonstrate that the targets' locations were extracted correctly and these algorithms are robust and eﬀicient.
above pixel,hyperspectral imaging,spectral signature,sub,supervised classiﬁcation,target recognition,unmixing