Change Detection in Dual-Temporal Remote Sensing Data Based on a Lightweight Siamese Network with Effective Preprocessing

Change detection (CD) is a crucial application in the field of remote sensing. Most current CD methods are based on deep learning and revolve around multispectral data. However, a common issue arising from these methods is the large number of model parameters due to the high dimensionality of the da...

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Bibliographic Details
Main Authors: Huang, Yankun, Wei, Maosheng, Ge, Baoyu, Zhang, Yun, Ji, Zhenyuan
Format: Conference Proceeding
Language:English
Subjects:
change detection (CD)
Data models
Data preprocessing
Deep learning
Feature extraction
lightweight network
Network architecture
Remote sensing
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Summary:Change detection (CD) is a crucial application in the field of remote sensing. Most current CD methods are based on deep learning and revolve around multispectral data. However, a common issue arising from these methods is the large number of model parameters due to the high dimensionality of the data channels. In this paper, we propose a lightweight Siamese network structure that minimally incorporates conventional convolutional layers. Additionally, in terms of data preprocessing, we select the R, G, and B channels of multispectral data for dehazing processing, and employ the processed data as inputs to the network. Experimental results illustrate the outstanding effectiveness and efficiency of the proposed method.
ISSN:2153-7003
DOI:10.1109/IGARSS53475.2024.10642488