Nowadays, the low-rank representation (LRR) and deep learning-based methods have received much attention in anomaly detection for hyperspectral images (HSIs). However, most of these methods mainly focus on the powerful reconstruction capability of the neural networks while ignoring the potential probability distribution of both anomalies and background pixels. To solve the problem, we propose a sparse component extraction-based probability distribution representation detector (SC-PDRD) framework, which integrates the characteristic of the sparse component obtained by the LRR model with the powerful probability representation ability of the variational autoencoder (VAE) network. The LRR model effectively separates the anomaly component from the background, which also serves as the prior anomaly distribution for each pixel. Moreover, the VAE architecture tries to recover the potential anomaly distribution using the sparse detection map in the feature space. In addition, we employ the Chebyshev neighborhood to leverage spatial information. The modified Wasserstein distance measures the distance between the test pixel and its neighborhood. The final detection map is attained by combining the prior anomalous degree of the anomalies with the output of the VAE network. Experimental results on three real HSIs demonstrate the effectiveness and superiority of SC-PDRD.
Anomaly detection plays a significant role in hyperspectral imagery. Traditional methods mainly focus on the spectral discrimination between the background object and the test object by means of utilizing the Mahalanobis distance such as the benchmark Reed-Xiaoli (RX) detector. In this paper, we propose a novel hyperspectral anomaly detection method based on low rank representation. Since the observed hyperspectral data can be decomposed into a background part with low-rank property and a sparse anomaly part, we exploit the local outlier factor (LOF) to construct the potential background dictionary. The dictionary attempts to cover as many categories as possible for the potential background objects and can effectively excludes the anomaly objects by calculating the local density and outlier degree. In order to take advantage of the huge hyperspectral dataset cube, we integrate the spectral and spatial information with the outlier degree as a constraint component to optimize the low rank representation model, which takes the implicit structure of the whole hyperspectral image into consideration. Experiments conducted on both synthetic and real hyperspectral datasets indicate the proposed method achieves a better performance compared to other state-of -the-art methods.
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