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In hyperspectral image processing technologies, anomaly detection is a valuable and practical way of searching small unknown targets based on spectral characteristics. For the lack of prior knowledge of targets, background modeling on hyperspectral images is the key process that affects the outcome of anomaly detection operator. In this paper, a novel method of anomaly detection based on quadratic...
Anomaly detection is one of the most important applications for hyperspectral images. In this paper, a new ensemble learning algorithm for anomaly detection in hyperspectral imagery is proposed, which integrates feature grouping and anomalous signal subspace estimation. Main contribution of the proposed algorithm consists in two aspects. First, feature grouping in original hyperspectral images are...
Anomaly detection is one of the most important applications for hyperspectral images. Conventional algorithm such as Reed-Xiaoli (RX) detector fails to be applied to hyperspectral images, which have high spectral dimensionality and complicated correlation between spectral bands. Therefore, effective feature extraction methods and selection rules are necessary. In this paper, comparative analyses of...
In this letter, a selective kernel principal component analysis (KPCA) algorithm based on high-order statistics is proposed for anomaly detection in hyperspectral imagery. First, KPCA is performed on the original hyperspectral data to fully mine the high-order correlation between spectral bands. Then, the average local singularity (LS) is defined based on the high-order statistics in the local sliding...
In this paper, a selective kernel principal component analysis algorithm is proposed for anomaly detection in hyperspectral imagery. The proposed algorithm tries to solve the problem brought by high dimensionality of hyperspectral images in anomaly detection. This algorithm firstly performs kernel principal component analysis (KPCA) on the original data to fully mine high-order correlation between...
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