MSCDUNet: A Deep Learning Framework for Built-Up Area Change Detection Integrating Multispectral, SAR, and VHR Data

Abstract

Built-up area change detection (CD) plays an important role in city management, which always uses very high spatial resolution (VHR) remote sensing data to extract refined spatial information. Recently, many CD models based on deep learning with VHR data have been proposed. However, due to the complex background information and natural landscape changes, VHR with optical RGB features is hard to extract changes exactly. To this end, we tend to explore the abundant channel information of multispectral and SAR data as a supplement to the refined spatial features of VHR images. We propose a new deep learning framework called multisource CD UNet++ (MSCDUNet), integrating multispectral, SAR, and VHR data for built-up area CD. First, we label and reform two new built-up area CD datasets containing multispectral, SAR, and VHR data: multisource built-up change (MSBC) and multisource OSCD (MSOSCD) datasets. Second, a feature selection method based on random forest is introduced to choose effective features from multispectral and SAR images. Finally, a multilevel heterogeneous feature fusion module is embedded in MSCDUNet to combine multifeatures for CD. Experiments are conducted on both the MSOSCD and the MSBC datasets. Compared to other CD methods based on VHR images, our proposal achieves the highest accuracy on both datasets and proves the effectiveness of multispectral, SAR, and VHR data fusion for CD. The dataset in the article will be available for download from the following link.1

Keywords

• Benchmark dataset
• built-up
• change detection (CD)
• deep learning (DL)
• multispectral data fusion
• very high resolution (VHR)