Abstract
In recent years, neural network-based anomaly detection methods have attracted considerable attention in the hyperspectral remote sensing domain due to their powerful reconstruction ability compared with traditional methods. However, actual probability distribution statistics hidden in the latent space are not discovered by exploiting the reconstruction error because the probability distribution of anomalies is not explicitly modeled. To address the issue, we propose a novel probability distribution representation detector (PDRD) that explores the intrinsic distribution of both the background and the anomalies for hyperspectral anomaly detection in this paper. First, we represent the hyperspectral data with multivariate Gaussian distributions from a probabilistic perspective. Then, we combine the local statistics with the obtained distributions to leverage the spatial information. Finally, the difference between the test pixel and the average expectation of the pixels in the Chebyshev neighborhood is measured by computing the modified Wasserstein distance to acquire the detection map. We conduct the experiments on three real data sets to evaluate the performance of our proposed method. The experimental results demonstrate the accuracy and efficiency of our proposed method compared to the state-of-the-art detection methods.
Highlights
Due to the hundreds of continuous and narrow bands with a wide range of wavelengths, hyperspectral imagery (HSI) can provide much information about spectral signature [1]
We propose a novel probability distribution representation detector (PDRD) based on variational autoencoder (VAE) structure for hyperspectral anomaly detection, which explicitly represents the HSI with multivariate Gaussian distributions and detects the anomalies by employing the modified Wasserstein distance
The PDRD has achieved better performance than other methods, and the results demonstrate the effectiveness of our proposed method
Summary
Due to the hundreds of continuous and narrow bands with a wide range of wavelengths, hyperspectral imagery (HSI) can provide much information about spectral signature [1]. Hyperspectral anomaly detection is considered a particular case of target detection [8]. It requires no prior knowledge of background or specific objects compared to target detection tasks [9]. As a consequence, it owns a promising application prospect. The leading hyperspectral anomaly detection theory considers that the objects in HSI comprise the background component and the anomaly component [10]. The anomalies we studied here are defined objects in small regions whose spectral signature significantly differs from the neighboring areas. Density Nonparametric Estimation With Data-Adaptive Bandwidths for the Detection of Anomalies in Multi-Hyperspectral Imagery. In Proceedings of the SPIE, Baltimore, MD, USA, 23–24 April 2012; pp. 625–636
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