  In recent years, neural network-based anomaly detection methods have
attracted considerable attention in the hyperspectral remote sensing domain due
to the 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 in original data 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 corresponding distributions of 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 four real data sets to evaluate
the performance of our proposed method. Experimental results demonstrate the
accuracy and efficiency of our proposed method compared to the state-of-the-art
detection methods.
