Multitask Learning-Based Reliability Analysis for Hyperspectral Target Detection

Abstract

Hyperspectral images contain abundant spectral information, which provide great potential for target detection. However, it also introduces a critical spectral variability problem for hyperspectral target detection, which makes the hyperspectral target detection much difficult than the classical spectral match issue. Many traditional detection methods have been proposed to deal with the spectral variability. However, these algorithms are still highly susceptible to the target spectral variability. The single input restriction and the inherent spectral characteristics mining problem are the main issues with these methods. The multitask learning (MTL) technique may have the potential to solve the above hyperspectral target detection issues since it can extract the inherent similarity and difference within multiple priori target spectra to learn a robust target spectral signature. This paper proposed a MTL-based reliability analysis method for hyperspectral target detection (MultiRely). This approach: 1) utilizes multiple priori target spectra to better represent the target spectral characteristics and construct multiple related detection tasks; 2) takes full advantage of the multitask learning technique to explore the spectral similarity and difference between multiple priori target spectra; 3) and applies the reliability analysis to obtain a reliable target spectrum in order to alleviate the target spectral variability. Experiments on two real hyperspectral datasets and one synthetic hyperspectral dataset illustrated the effectiveness of the proposed algorithm compared to the state-of-the-art detectors.