Fast Hyperspectral Image Anomaly Detection Based on Orthogonal Projection
The operating efficiency of an anomaly detection algorithm based on low-rank sparse decomposition decreases significantly when it is used to process a large amount of hyperspectral image data.Thus,a fast hyperspectral image anomaly detection algorithm based on orthogonal projection is proposed in this study.First,a hyperspectral image is projected onto background orthogonal subspace to improve the distinction between the background and anomalous objects,so as to separate them easily.Next,a new hyperspectral image representation model is proposed,and an algorithm for automatic target generation is introduced to construct a dictionary matrix.A high-dimensional image data matrix is mapped into a low-dimensional matrix using this model with the orthogonality of the dictionary matrix,so as to reduce the dimensionality of the data and the computational complexity of the algorithm.Proposed algorithm is tested on three real image datasets and it achieves detection accuracies of 0.9964,0.9984,and 0.9999,respectively.In addition,the average operation time taken by proposed algorithm on the three datasets is more than 90%shorter than the shortest operation time taken by comparative algorithms.Experimental results on three datasets demonstrate that the computational efficiency of proposed algorithm is higher than those of other algorithms that ensure detection performance.
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