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In recent years, tensor representation (TR)-based hyperspectral anomaly detection (HAD) approaches have attracted more and more attention. However, two urgent issues still need to be addressed: 1) existing tensor decomposition approaches for HAD cannot make full use of the spectral–spatial correlation of background components in hyperspectral images (HSIs) and 2) most approaches based on TR overlook the piecewise-smooth of background components that exist simultaneously in the spectral and spatial domains. To this end, with the aid of an advanced multiscale entanglement renormalization ansatz (MERA) tensor network, this article proposes an algorithm based on MERA decomposition and enhanced total variation regularization (MERAETV) for HAD. Specifically, MERA decomposes the background tensor by contracting a top-level factor with the remaining semiorthogonal and orthogonal factors. Due to the intricate interplay between semiorthogonal (low-rank) and orthogonal factors, low-rank MERA approximation exhibits a robust representational capacity that effectively captures the spectral–spatial correlation of the background component. Meanwhile, an enhanced total variation (ETV) regularization is devised to capture the inherent piecewise-smooth of the background component in both spectral and spatial domains. Furthermore, our algorithm incorporates group sparsity constraint and Gaussian noise term to enhance the discrimination between anomalies and background. Finally, a highly efficient update scheme based on the alternating direction method of multipliers (ADMM) is designed. A large number of experiments on one synthetic and seven real HSIs demonstrate the superiority of our proposed approach.

The ubiquity of mixed pixels in hyperspectral images makes it difficult for traditional classification techniques to determine the spatial distribution of land-cover classes accurately. Subpixel mapping (SPM) technology is an effective method to solve this problem. Aiming at taking the multiple scales and the spatial features into account, an SPM method based on multiscale and multifeature (MSMF) is proposed, so as to effectively improve the accuracy of SPM. First, the maximum linearization index (MLI) method of the nonredundant complete straight-line (CSL) set is designed to identify the linear distribution feature of land-cover (LC) classes. Then, different methods are applied to different spatial features and unified together finally, where the template matching iterative exchange is used for the linear distribution classes, and the multiscale spatial dependence (MSD) iterative exchange method combined with area perimeter is used for the planar distribution classes. Experiments on three remote sensing images are carried out to evaluate the performance of MSMF. The results show that the proposed method can effectively improve the accuracy of SPM.

Recently, hyperspectral anomaly detection (HAD) methods based on tensor low-rank representation (TLRR) have received widespread attention. However, most of them tend to emphasize the utilization of multiple types of prior knowledge to characterize background components, while the prior information about anomaly components is limited. In addition, the constructed background dictionary is also susceptible to noise and outliers. To address these challenges, this article focuses on both the background and abnormal components, proposing a robust tensor low-rank sparse representation with saliency prior (RTLSR-SP) method for HAD. Specifically, for the background component described by the dictionary tensor and the corresponding coefficient tensor, tensor nuclear norm (TNN) constraint and sparsity constraint are imposed on the coefficient tensor simultaneously to capture the global and local spatial–spectral structure information of the hyperspectral image (HSI), respectively. For the anomalous component, we design a sparse saliency prior weight tensor to enhance the saliency of anomalous targets. Meanwhile, the tensor $\ell _{F,1}$ -norm is also integrated into the model to better separate abnormal targets from the background. Furthermore, combining tensor robust principal component analysis (TRPCA) and skinny tensor singular value decomposition (skinny t-SVD), a robust background dictionary is constructed. Finally, an efficient iterative algorithm based on the alternating direction method of multipliers (ADMM) is derived to optimize the RTLSR-SP model. Comprehensive experimental findings on one simulated dataset and six real hyperspectral datasets demonstrate the effectiveness and superiority of the proposed algorithm compared with eight state-of-the-art HAD algorithms.

Most of the existing stereo matching methods have been formulated into four regular parts: feature extraction (FE), cost calculation (CC), cost aggregation (CA), and disparity refinement (DF). They can obtain high precision results in most regions through modifying parts of the four methods, but still have problems in some ill-posed regions. This paper focuses on feature consistency and confidence (FCC), discovers the new attributes of the feature, and proposes a novel neural network structure for stereo matching by measuring the consistency and confidence of features. Base on this method, the paper fuses the cost volume and calculates the pixel confidence map for cost calculation and cost aggregation. The experimental results show the proposed method outperforms most of the state-of-the-art methods on both SceneFlow and Kitti benchmarks and lowers the estimation error of stereo matching down to 1.82% ranking at the 7th position in the Kitti 2015 scoreboard six months ago (http://www.cvlibs.net/datasets/kitti/).