Abstract
Target detection is an active area in hyperspectral imagery (HSI) processing. Many algorithms have been proposed for the past decades. However, the conventional detectors mainly benefit from the spectral information without fully exploiting the spatial structures of HSI. Besides, they primarily use all bands information and ignore the inter-band redundancy. Moreover, they do not make full use of the difference between the background and target samples. To alleviate these problems, we proposed a novel joint sparse and low-rank multi-task learning (MTL) with extended multi-attribute profile (EMAP) algorithm (MTJSLR-EMAP). Briefly, the spatial features of HSI were first extracted by morphological attribute filters. Then the MTL was exploited to reduce band redundancy and retain the discriminative information simultaneously. Considering the distribution difference between the background and target samples, the target and background pixels were separately modeled with different regularization terms. In each task, a background pixel can be low-rank represented by the background samples while a target pixel can be sparsely represented by the target samples. Finally, the proposed algorithm was compared with six detectors including constrained energy minimization (CEM), adaptive coherence estimator (ACE), hierarchical CEM (hCEM), sparsity-based detector (STD), joint sparse representation and MTL detector (JSR-MTL), independent encoding JSR-MTL (IEJSR-MTL) on three datasets. Corresponding to each competitor, it has the average detection performance improvement of about 19.94%, 22.53%, 16.92%, 14.87%, 14.73%, 4.21% respectively. Extensive experimental results demonstrated that MTJSLR-EMAP outperforms several state-of-the-art algorithms.
Highlights
Hyperspectral imagery (HSI) conveys rich spectral information over a wide range of the electromagnetic spectrum [1,2,3]
Target detection is an active area in the hyperspectral community, which focuses on distinguishing specific target pixels from various background pixels with a priori knowledge of target [2,4]
The effectiveness of the proposed algorithm was validated on three hyperspectral imagery (HSI)
Summary
Hyperspectral imagery (HSI) conveys rich spectral information over a wide range of the electromagnetic spectrum [1,2,3]. Target detection is an active area in the hyperspectral community, which focuses on distinguishing specific target pixels from various background pixels with a priori knowledge of target [2,4]. Due to its both civil and military use [5,6], target detection has been extensively applied in many HSI applications. The key idea of SR-based methods is that a test pixel in HSI lies in a low-dimensional subspace and can be represented as a sparse linear combination of the training samples [10]. A spatially adaptive sparsity model was proposed in Reference [13], which exploited different contribution of each neighboring pixel
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