Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors

TL;DR

This paper introduces a novel anomaly segmentation model for high-resolution remote sensing images that leverages pixel descriptors and deep one-class classification to effectively identify abnormal patterns despite data complexity.

Contribution

It proposes a new ASD model combining multi-scale feature extraction and virtual abnormal sample generation for improved anomaly detection in HSR imagery.

Findings

Outperforms recent state-of-the-art models on four HSR datasets.

Effectively detects irregular and complex anomaly patterns.

Demonstrates robustness in diverse Earth vision applications.

Abstract

Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an important role in various Earth vision applications. However, it is a challenging task due to the complex distribution and the irregular shapes of objects, and the lack of abnormal samples. To tackle these problems, an anomaly segmentation model based on pixel descriptors (ASD) is proposed for anomaly segmentation in HSR imagery. Specifically, deep one-class classification is introduced for anomaly segmentation in the feature space with discriminative pixel descriptors. The ASD model incorporates the data argument for generating virtual ab-normal samples, which can force the pixel descriptors to be compact for normal data and meanwhile to be diverse to avoid the model collapse problems when only positive samples participated in the training. In addition, the ASD introduced a multi-level and multi-scale feature extraction strategy for learning the low-level and semantic information to make the pixel descriptors feature-rich. The proposed ASD model was validated using four HSR datasets and compared with the recent state-of-the-art models, showing its potential value in Earth vision applications.