期刊论文

Chen, JH

英文

Remote Sensing

2072-4292

2023

15

9

2351-

10.3390/rs15092351

Conceptualization, J.F. and J.C.; Data curation, M.Z. and J.Z.; Formal analysis, M.Z., J.C., J.Z. and Z.S.; Funding acquisition, J.F.; Methodology, J.F.; Software, M.Z., J.Z. and Z.S.; Supervision, J.F. and M.J.; Visualization, M.Z.; Writing–original draft, J.F. and J.C.; Writing–review and editing, J.F. and M.J. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (Grant No. 42171413); a grant from the State Key Laboratory of Resources and Environmental Information System; the Shandong Provincial Natural Science Foundation (Grant No. ZR2020MD015 and ZR2020MD018); the National Key Research and Development Program of China (Grant No. 2017YFB0503500); and the Young Teacher Development Support Program of Shandong University of Technology (Grant No. 4072-115016).

本校为通讯机构

Building change detection is an important task in the remote sensing field, and the powerful feature extraction ability of the deep neural network model shows strong advantages in this task. However, the datasets used for this study are mostly three-band high-resolution remote sensing images from a single data source, and few spectral features limit the development of building change detection from multisource remote sensing images. To investigate the influence of spectral and texture features on the effect of building change detection based on deep learning, a multisource building change dete...