Attention-Based Convolutional Neural Network for Anomaly Detection in Multispectral Images of Semi-Natural Ecosystems

Abstract

The monitoring of semi-natural ecosystems has become increasingly critical due to the rising impact of ecological disturbances, including natural disasters and unauthorized human-made constructions. Anomaly detection (AD) in multispectral imagery serves as a fundamental tool in this context. Deep-learning (DL)-based techniques are particularly effective at capturing the intricate spectral and spatial patterns of anomalies. This letter proposes a new AD technique called attention-based convolutional neural network (ACNN), designed to enhance AD performance in multispectral images of high spatial resolution for the detection of human-made constructions. The model integrates attention mechanisms to prioritize informative features while suppressing irrelevant background information, thereby improving sensitivity to subtle and rare anomalies. Experimental results on multispectral datasets from semi-natural ecosystems show that the proposed approach outperforms existing DL techniques in terms of detection accuracy. These findings highlight the potential of attention-based models as a robust framework for environmental monitoring and AD in complex remote sensing scenarios.