Building Change Detection with Deep Learning by Fusing Spectral and Texture Features of Multisource Remote Sensing Images: A GF-1 and Sentinel 2B Data Case

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...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 15; no. 9; p. 2351
Main Authors Fan, Junfu, Zhang, Mengzhen, Chen, Jiahao, Zuo, Jiwei, Shi, Zongwen, Ji, Min
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 29.04.2023
Subjects
Accuracy
Artificial neural networks
Batch processing
building change detection
Change detection
Datasets
Deep learning
Design
Feature extraction
High resolution
Image resolution
Land use
Machine learning
multisource spectral data
multispectral
Neural networks
Remote sensing
Texture
China
Siam
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Summary: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 detection dataset (MS-HS BCD dataset) is produced in this paper using GF-1 high-resolution remote sensing images and Sentinel-2B multispectral remote sensing images. According to the different resolutions of each Sentinel-2B band, eight different multisource spectral data combinations are designed, and six advanced network models are selected for the experiments. After adding multisource spectral and texture feature data, the results show that the detection effects of the six networks improve to different degrees. Taking the MSF-Net network as an example, the F1-score and IOU improved by 0.67% and 1.09%, respectively, compared with high-resolution images, and by 7.57% and 6.21% compared with multispectral images.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15092351