Hyperspectral Anomaly Detection with Differential Attribute Profiles and Genetic Algorithms

Abstract

Anomaly detection is hampered by band redundancy and the restricted reconstruction ability of spectral–spatial information in hyperspectral remote sensing. A novel hyperspectral anomaly detection method integrating differential attribute profiles and genetic algorithms (DAPGA) is proposed to sufficiently extract the spectral–spatial features and automatically optimize the selection of the optimal features. First, a band selection method with cross-subspace combination is employed to decrease the spectral dimension and choose representative bands with rich information and weak correlation. Then, the differentials of attribute profiles are calculated by four attribute types and various filter parameters for multi-scale and multi-type spectral–spatial feature decomposition. Finally, the ideal discriminative characteristics are reserved and incorporated with genetic algorithms to cluster each differential attribute profile by dissimilarity assessment. Experiments run on a variety of genuine hyperspectral datasets including airport, beach, urban, and park scenes show that the effectiveness of the proposed algorithm has great improvement with existing state-of-the-art algorithms.

Spectral–Spatial Feature Fusion for Hyperspectral Anomaly Detection

“…A hyperspectral image (HSI) contains hundreds of narrow spectral channels for each pixel, which delivers rich spectral and spatial information [ 1 , 2 , 3 , 4 , 5 ]. Owing to this virtue, HSI has been popularly employed in a considerable number of fields, such as object detection [ 6 , 7 , 8 , 9 ], image classification [ 10 , 11 , 12 , 13 ], and change detection [ 14 , 15 , 16 ]. For all the application fields, hyperspectral anomaly detection, which aims to recognize the outliers whose spectra are significantly different from an ambient scene, has drawn much more attention over the last few years [ 17 ].…”