MSCDUNet: A Deep Learning Framework for Built-Up Area Change Detection Integrating Multispectral, SAR, and VHR Data
Haoyang Li, Fangjie Zhu, Xiaoyu Zheng, Mengxi Liu, Guangzhao Chen
Abstract
Built-up area change detection (CD) plays an important role in city management, which always uses very high spatial resolution remote sensing (VHR) 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: MSBC and MSOSCD datasets. Second, a feature selection method based on Random Forest(RF) is introduced to choose effective features from multispectral and SAR images. Finally, a multi-level heterogeneous feature fusion module is embedded in MSCDUNet to combine multi-features for CD. Experiments are conducted on both the MSOSCD and the MSBC dataset. 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 paper will be available for download from the following link <b> <uri>https://github.com/Lihy256/MSCDUnet</uri></b>.