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A Lightweight False Alarm Suppression Method in Heterogeneous Change Detection

Year: 2024

Type: article

Abstract: Overlooking the issue of false alarm suppression in heterogeneous change detection leads to inferior detection performance. This paper proposes a method to handle false alarms in heterogeneous change detection. A lightweight network of two channels is bulit based on the combination of convolutional neural network (CNN) and graph convolutional network (GCN). CNNs learn feature difference maps of multitemporal images, and attention modules adaptively fuse CNN-based and graph-based features for different scales. GCNs with a new kernel filter adaptively distinguish between nodes with the same and those with different labels, generating change maps. Experimental evaluation on two datasets validates the efficacy of the proposed method in addressing false alarms.

Source: Journal of Systems Engineering and Electronics

DOI: 10.23919/jsee.2024.000086

Authors: Cong Xu, Zishu He, Haicheng Liu

Institutions: Heilongjiang Institute of Technology, University of Electronic Science and Technology of China

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funder: Natural Science Foundation of Heilongjiang Province

Award: G6550714715