We present a general half-quadratic based hyperspectral unmixing (HU) framework to solve the robust or sparse unmixing problem. A series of potential methods can be designed and developed to solve HU problem through this framework. By introducing correntropy metric, a correntropy based spatial-spectral robust sparsity regularized (CSsRS-NMF) unmixing method is derived through the proposed framework to achieve two-dimensional robustness and adaptive weighted sparsity constraint for abundances simultaneously.
Recently, many state-of-the-art methods have been proposed for infrared (IR) dim and small target detection, but the performance of IR small target detection still faces with challenges in complicated environments. In this paper, we propose a novel IR small target detection method named local entropy characterization prior with multi-mode weighted tensor nuclear norm (LEC-MTNN) that combines local entropy characterization prior (LEC) and multi-mode weighted tensor nuclear norm (MTNN). First, we transform the original infrared image sequence into a nonoverlapping spatial-temporal patch-tensor to fully utilize the spatial and temporal information in image sequences. Second, a nonconvex surrogate of tensor rank called MTNN is proposed to approximate background tensor rank, which organically combines the sum of the Laplace function of all the singular values and multi-mode tensor extension of the construct tensor without destroying the inherent structural information in the spatial-temporal tensor. Third, we introduce a new sparse prior map named LEC via an image entropy characterization operator and structure tensor theory, and more effective target prior can be extracted. As a sparse weight, it is beneficial to further preserve the targets and suppress the background components simultaneously. To solve the proposed model, an efficient optimization scheme utilizing the alternating direction multiplier method (ADMM) is designed to retrieve the small targets from IR sequence. Comprehensive experiments on four IR sequences of complex scenes demonstrate that LEC-MTNN has the superior target detectability (TD) and background suppressibility (BS) performance compared with other five state-of-the-art detection methods.
This paper develops an independent component analysis (ICA) with subspace projection for hyperspectral anomaly detection. The proposed model represents the sphered data set X as an ICA model specified by a two-component orthogonal sum decomposition, X IC N = +j with j independent components, j IC , generated by ICA and a noise component N. To better extract anomalies from the j IC component space, the concept of sparsity cardinality (SC) is integrated into ICA to derive a ICASC anomaly detector (ICASC-AD). For determine appropriate values of j, the virtual dimensionality (VD) and a minimax-singular value decomposition (MX-SVD) are used for this purpose. The experimental results demonstrate that ICASC are very competitive against the LRaSR-based models in hyperspectral anomaly detection.
KEYWORDS: Target detection, Infrared radiation, Infrared imaging, Infrared detectors, Detection and tracking algorithms, Image segmentation, Infrared search and track, Signal to noise ratio
Infrared small target detection under intricate background and heavy noise is one of the crucial tasks in the field of infrared search and tracking (IRST) system. The images with small targets are usually of quite low signal-to-noise ratios, which makes the targets very difficult to be detected. To solve this problem, an effective infrared small target detection algorithm is presented in this paper. Firstly, a nested structure of the original pixel-wise image is constructed and the local structural discontinuity of each pixel is measured by a vector so-called local contrast vector (LCV). Each element of LCV describes the minimal difference between the central region and its neighboring regions, and the scale variety of regions results in the variety of elements. Then, a multi-dimensional image is generated with respect to LCV. After that, a confidence map for small target detection is reconstructed by signed normalization, that is, each pixel in the confidence map is generated by signed inner product of LCV. Finally, we segment the targets from the confidence map by utilizing an adaptive threshold. Extensive experimental evaluation results on a real test dataset demonstrate that our algorithm is superior to the state-of-the-art algorithms in detection performance.
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