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Proceedings Paper

Rural settlements extraction based on deep learning from high spatial resolution remote sensing imagery
Author(s): Qi Li; Liang Hong; Huiling Sun
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Paper Abstract

The accurately and efficiently extracting rural settlements from high resolution remote sensing image is of important significance for rural government management. Due to the complex environment in rural region, the traditional supervised classification methods already could not satisfy the application requirements for automatically extracting rural settlements, and they can only obtain the results of low precision and incomplete extraction. In recent years, with the rapid development of deep learning in computer vision, the deep learning method has been widely used to target extraction based on high resolution remote sensing imagery. So, this paper proposed a rural settlements extraction method based on the deep learning using high-resolution remote sensing image. The Tensorflow deep learning framework was built up to train the Faster regional recommendation convolutional neural network model(Faster R-CNN). Image feature maps were extracted by the Convolutional Neural Network(CNN) firstly. The region proposal network (RPN) was built to extract the regions that might contain rural settlements. And the region was identified and classified by detection network. The method was tested and verified in the homemade datasets. This paper selected a typical area for testing. The experimental results show that the proposed method can extract the rural settlements areas with higher accuracy compared with traditional rural extraction ways.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300A (14 February 2020); doi: 10.1117/12.2536991
Show Author Affiliations
Qi Li, Yunnan Normal Univ. (China)
Liang Hong, Yunnan Normal Univ. (China)
Huiling Sun, Yunnan Normal Univ. (China)


Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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