Over the last two decades, anomaly detection (AD) has been known to play a critical role in hyperspectral image analysis, which provides a new way to distinguish the targets from the background without prior knowledge. Recently, the representation-based methods were proposed and soon became a significant type of methods on hyperspectral AD. In this paper, a novel AD algorithm based on convolutional neural network (CNN) and low-rank representation (LRR) is proposed. First, a CNN model is built and trained on hyperspectral image (HSI) datasets to accurately obtain the resulting abundance maps. Compared with the raw dataset, abundance maps contain more distinctive features to identify anomalies from the background. Second, a dictionary is constructed by the density-based spatial clustering of applications with noise (DBSCAN) algorithm to stably represent the background component. Third, a matrix decomposition method based on LRR is adopted. In this way, a coefficient matrix corresponding to the constructed dictionary is obtained, which is low rank. At the same time, a residual matrix can be obtained as well, which is sparse. Finally, anomalies can be extracted from the residual matrix. The experimental results show that the proposed method achieves a superior performance compared to some of the state-of-the-art methods in the field of hyperspectral AD.

The aim of infrared and visible image fusion is to obtain an integrated image that contains obvious object information and high spatial resolution background information. The integrated image is more conductive for a human or a machine to understand and mine the information contained in the image. To attain this purpose, a fusion algorithm based on multi-level Gaussian curvature filtering (MLGCF) image decomposition is proposed. First, a MLGCF is presented and employed to decompose the input source images into three different layers: small-scale, large-scale, and base layers. Then, three fusion strategies—max-value, integrated, and energy-based—are applied to combine the three types of layers, which are based on the different properties of the three types of layers. Finally, the fusion image is reconstructed by summing the three types of fused layers. Six groups of experiments demonstrate that the proposed algorithm performs effectively in most cases by subjective and objective evaluations and even exceeds many high-level fusion algorithms.

Most existing hyperspectral anomaly detection algorithms primarily rely on spatial information to identify anomalous targets. However, they often overlook the spatial–spectral gradient information inherent in hyperspectral images, which can lead to decreased detection accuracy. To address this limitation, we propose a novel hyperspectral anomaly detection algorithm that incorporates both local sub-block contrast and spatial–spectral gradient features. In this approach, a grid block window is utilized to capture local spatial information. To effectively detect low-contrast targets, we introduce a novel local sub-block ratio-multiply contrast method that enhances anomalous regions while suppressing the background. Additionally, to mitigate the challenges posed by complex backgrounds, a feature extraction technique based on spatial–spectral gradients is proposed. To account for the spectral reflectance differences between anomalous targets and the background, we further introduce a local sub-block ratio-difference contrast method to compute preliminary detection scores. The final anomaly detection results are obtained by merging these two detection scores. The key advantage of the proposed method lies in its ability to exploit local gradient characteristics within hyperspectral images, thereby resolving the issue of edge features being misidentified as anomalies. This method also effectively reduces the impact of noise on detection accuracy. Experimental validation based on four real-world datasets demonstrates that the proposed method outperforms seven state-of-the-art techniques, showing superior performance in both qualitative and quantitative evaluations.

Hyperspectral anomaly detection is a challenging task due to the inaccurate evaluation of background statistics and the contamination of anomaly pixels. In this paper, we propose an effective hyperspectral anomaly detection algorithm based on local joint subspace process and support vector machine (SVM). The method mainly consists of three steps. At first, in the local joint subspace process, we combine Mahalanobis distance detection and spectral angular distance detection of subspace projection by local sliding change dual windows. With this local joint subspace process, the spatial-spectral information is jointly utilized and the local subspace score is also obtained. Then, to further improve the detection probability, we propose the support vector score by classifying the dimensionally reduced hyperspectral image data through a trained SVM model. Finally, we obtain the final detection result from the linear combination of above scores. Compared with alternative methods, the proposed method has shown a superior performance on both synthetic and real-world datasets.