Multiple Sub-Pixel Target Detection for Hyperspectral Imaging Systems

TL;DR

This paper introduces a novel detection method for hyperspectral imaging that effectively identifies multiple sub-pixel targets by modeling their spectral signatures and background, improving detection accuracy in low-resolution sensors.

Contribution

It proposes a generalized replacement model for multiple sub-pixel targets and develops detection architectures based on likelihood ratio tests with parameter estimation.

Findings

Effective detection of multiple sub-pixel targets demonstrated on synthetic data.

Improved detection performance over existing methods shown with real hyperspectral data.

The proposed approach accurately models mixed spectral signatures within pixels.

Abstract

Hyperspectral target detection is a task of primary importance in remote sensing since it allows identification, location, and discrimination of target features. To this end, the reflectance maps, which contain the spectral signatures and related abundances of the materials in the observed scene, are often used. However, due to the low spatial resolution of most hyperspectral sensors, targets occupy a fraction of the pixel and, hence, the spectra of different sub-pixel targets (including the background spectrum) are mixed together within the same pixel. To solve this issue, in this paper, we adopt a generalized replacement model accounting for multiple sub-pixel target spectra and formulate the detection problem at hand as a binary hypothesis test where under the alternative hypothesis the target is modeled in terms of a linear combination of endmembers whose coefficients also account for the presence of the background. Then, we devise detection architectures based upon the generalized likelihood ratio test where the unknown parameters are suitably estimated through procedures inspired by the maximum likelihood approach. The performances of the proposed decision schemes are evaluated by means of both synthetic as well as real data and compared with an analogous counterpart by showing the effectiveness of the proposed procedure.