Buildings extraction based on high-resolution remote sensing imagery

doi:10.13474/j.cnki.11-2246.2023.0191

Abstract

Abstract: High-resolution remote sensing images not only have rich spectrum, spatial distribution, shape and texture features, but also contain clear scene semantic information. Taking Zongyang town, Zongyang county, Anhui province as the research area, and using high-resolution images as the basic data source, the deep learning and object-oriented method in eCognition software is used to automatically extract buildings in this paper. The results show that the method of combining deep learning with object-oriented has a better effect of building extraction, and the overall classification accuracy reaches 96.8%, which can be used for building extraction production based on high-resolution images.

Key words: deep learning, eCognition, multiresolution segmentation, object-based image analysis, convolutional neural networks

CLC Number:

P237

WANG Limei, WANG Yanzheng. Buildings extraction based on high-resolution remote sensing imagery[J]. Bulletin of Surveying and Mapping, 2023, 0(6): 180-183.

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