Hyperspectral Anomaly Detection via Graph Dictionary-Based Low Rank Decomposition with Texture Feature Extraction

The accuracy of anomaly detection in hyperspectral images (HSIs) faces great challenges due to the high dimensionality, redundancy of data, and correlation of spectral bands. In this paper, to further improve the detection accuracy, we propose a novel anomaly detection method based on texture feature extraction and a graph dictionary-based low rank decomposition (LRD). First, instead of using traditional clustering methods for the dictionary, the proposed method employs the graph theory and designs a graph Laplacian matrix-based dictionary for LRD. The robust information of the background matrix in the LRD model is retained, and both the low rank matrix and the sparse matrix are well separated while preserving the correlation of background pixels. To further improve the detection performance, we explore and extract texture features from HSIs and integrate with the low-rank model to obtain the sparse components by decomposition. The detection results from feature maps are generated in order to suppress background components similar to anomalies in the sparse matrix and increase the strength of real anomalies. Experiments were run on one synthetic dataset and three real datasets to evaluate the performance. The results show that the performance of the proposed method yields competitive results in terms of average area under the curve (AUC) for receiver operating characteristic (ROC), i.e., 0.9845, 0.9962, 0.9699, and 0.9900 for different datasets, respectively. Compared with seven other state-of-the-art algorithms, our method yielded the highest average AUC for ROC in all datasets

Hyperspectral Anomaly Detection via Dictionary Construction-Based Low-Rank Representation and Adaptive Weighting

Anomaly detection (AD), which aims to distinguish targets with significant spectral differences from the background, has become an important topic in hyperspectral imagery (HSI) processing. In this paper, a novel anomaly detection algorithm via dictionary construction-based low-rank representation (LRR) and adaptive weighting is proposed. This algorithm has three main advantages. First, based on the consistency with AD problem, the LRR is employed to mine the lowest-rank representation of hyperspectral data by imposing a low-rank constraint on the representation coefficients. Sparse component contains most of the anomaly information and can be used for anomaly detection. Second, to better separate the sparse anomalies from the background component, a background dictionary construction strategy based on the usage frequency of the dictionary atoms for HSI reconstruction is proposed. The constructed dictionary excludes possible anomalies and contains all background categories, thus spanning a more reasonable background space. Finally, to further enhance the response difference between the background pixels and anomalies, the response output obtained by LRR is multiplied by an adaptive weighting matrix. Therefore, the anomaly pixels are more easily distinguished from the background. Experiments on synthetic and real-world hyperspectral datasets demonstrate the superiority of our proposed method over other AD detectors

Light-responsive and ultrapermeable two-dimensional metal-organic framework membrane for efficient ionic energy harvesting

Abstract Nanofluidic membranes offer exceptional promise for osmotic energy conversion, but the challenge of balancing ionic selectivity and permeability persists. Here, we present a bionic nanofluidic system based on two-dimensional (2D) copper tetra-(4-carboxyphenyl) porphyrin framework (Cu-TCPP). The inherent nanoporous structure and horizontal interlayer channels endow the Cu-TCPP membrane with ultrahigh ion permeability and allow for a power density of 16.64 W m−2, surpassing state of-the-art nanochannel membranes. Moreover, leveraging the photo-thermal property of Cu-TCPP, light-controlled ion active transport is realized even under natural sunlight. By combining solar energy with salinity gradient, the driving force for ion transport is reinforced, leading to further improvements in energy conversion performance. Notably, light could even eliminate the need for salinity gradient, achieving a power density of 0.82 W m−2 in a symmetric solution system. Our work introduces a new perspective on developing advanced membranes for solar/ionic energy conversion and extends the concept of salinity energy to a notion of ionic energy

Amelioration of Neurosensory Structure and Function in Animal and Cellular Models of a Congenital Blindness.

Most genetically distinct inherited retinal degenerations are primary photoreceptor degenerations. We selected a severe early onset form of Leber congenital amaurosis (LCA), caused by mutations in the gene LCA5, in order to test the efficacy of gene augmentation therapy for a ciliopathy. The LCA5-encoded protein, Lebercilin, is essential for the trafficking of proteins and vesicles to the photoreceptor outer segment. Using the AAV serotype AAV7m8 to deliver a human LCA5 cDNA into an Lca5 null mouse model of LCA5, we show partial rescue of retinal structure and visual function. Specifically, we observed restoration of rod-and-cone-driven electroretinograms in about 25% of injected eyes, restoration of pupillary light responses in the majority of treated eyes, an ∼20-fold decrease in target luminance necessary for visually guided behavior, and improved retinal architecture following gene transfer. Using LCA5 patient-derived iPSC-RPEs, we show that delivery of the LCA5 cDNA restores lebercilin protein and rescues cilia quantity. The results presented in this study support a path forward aiming to develop safety and efficacy trials for gene augmentation therapy in human subjects with LCA5 mutations. They also provide the framework for measuring the effects of intervention in ciliopathies and other severe, early-onset blinding conditions