Hyperspectral anomaly detection (HAD) is a technique to find observations without prior knowledge, which is of particular interest as a branch of remote sensing object detection. However, the application of HAD is limited by various challenges, such as high-dimensional data, high intraclass variability, redundant information, and limited samples. To overcome these restrictions, we report an unsupervised strategy to implement HAD by dimensionality reduction (DR) and prior-based collaborative representation with adaptive global salient weight. The proposed framework includes three main steps. First, we select the most discriminating bands as the input hyperspectral images for subsequent processing in a DR manner. Then, we apply piecewise-smooth prior and local salient prior to collaborative representation to produce the initial detection map. Finally, to generate the final detection map, a global adaptive salient map is applied to the initial anomaly map to further highlight anomalies. Most importantly, the experimental results show that the proposed method outperforms alternative detectors on several datasets over different scenes. In particular, on the Gulfport dataset, the area under the curve value obtained by the proposed method is 0.9932, which is higher than the second-best method, convolutional neural network detector, by 0.0071.
Anomalous objects detection for hyperspectral imagery is a significant branch in the area of remote sensing. Although enormous advancements have been developed, issues of redundancy of spectral information and correlation between pixels should be further explored and improved. To address these problems, we proposed a method that is on the basis of integrating collaborative representation with multipurification processing and local salient weight. Multipurification processing consists of spectral bands purification (SBP) and background purification (BGP). First, to alleviate the interference of redundant spectral information, we remove unnecessary spectral bands by adopting SBP based on considering the global spectral intensity of each band. Then, we remove the outliers in the local dual window by BGP to avoid the effect of heterogeneous pixels. Simultaneously, we obtain the local salient weight by calculating the similarity and difference of pixels in the dual window. Next, we obtain the initial detection result by a collaborative representation, which has been testified to be very effective. Finally, combined with the local salient weight map, the initial detection map is improved to the final detection map. To demonstrate the superiority of the proposed method, we conducted the comprehensive experiment on three public benchmark datasets that contain 15 hyperspectral images.
Traditional anomaly detection algorithms for hyperspectral imagery does not consider spatial information of imagery, which decreases detection efficiency of anomaly detection. The traditional RXD algorithm uses Gauss model to evaluate the distribution of background, but ignores spatial correlation of the imagery. Aiming at improving detection efficiency, this paper proposed an anomaly detection algorithm which utilize both spatial and spectral information of hyperspectral imagery based on graph Laplacian. In this paper, an anomaly detection algorithm for hyperspectral imagery based on graph Laplacian (Graph Laplacian Anomaly Detection with Mahalanobis distance, LADM) is presented. The spatial information is considered in the model by graph Laplacian matrix. First, LADM considers not only spectral information but also the spatial information by mapping image to a graph. Secondly, a symmetrical normalization Laplacian matrix is constructed for the graph with Mahalanobis distance. The operation eliminates interference among the nodes, which improves the accuracy of Laplacian matrix and improves the detection result. Thirdly, LADM detectors is constructed with graph Laplacian detection model. Lastly, anomaly detection model based on graph is given based on graph Laplacian and spectral vector of the pixels. A threshold value is given to judge whether the currently detection pixel is anomaly or not. Experiments for synthetic data and real hyperspectral image is proposed in this paper. The proposed algorithm is compared with three classical anomaly detection algorithms. ROC curves and AUC values are given for both synthetic data and real data in the paper. Experiments results show that LADM algorithm can improve the accuracy of anomaly detection for hyperspectral imagery, and reduced the false alarm rate.
Aiming at the reflected highlight in remote sensing, we propose a new method of removing reflected highlight in polarimetric images. This method is based on reflection physical model, only requiring four polarization images and reflection angle; the original intensities of target which is under water or other crystals can be calculated by Stokes parameters accurately. Comparing to conventional polarization methods, experimental results show that proposed method can remove the glitters more effectively and reserve the original characteristic of target as much as possible. This new method does not need to restrict the specific observation angle and time so that it has more versatility. This new method improves reflected highlight image quality and it can be considered very suitable in water and polarization remote sensing.
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