Exploring the Intrinsic Probability Distribution for Hyperspectral Anomaly Detection

TL;DR

This paper introduces a novel hyperspectral anomaly detection method that models the intrinsic probability distributions of data and anomalies, leveraging local spatial statistics and Wasserstein distance for improved detection accuracy.

Contribution

The paper proposes a new probability distribution representation detector (PDRD) that explicitly models the distributions of background and anomalies in hyperspectral data.

Findings

Outperforms state-of-the-art methods in accuracy

Demonstrates high efficiency on real datasets

Effectively captures intrinsic data distributions

Abstract

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.