Rural Built-Up Area Extraction from Remote Sensing Images Using Spectral Residual Methods with Embedded Deep Neural Network

Shaodan Li, Shiyu Fu and Dongbo Zheng
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Shaodan Li: School of Geographical Sciences, Hebei Normal University, Shijiazhuang 050024, China
Shiyu Fu: School of Geographical Sciences, Hebei Normal University, Shijiazhuang 050024, China
Dongbo Zheng: School of Geographical Sciences, Hebei Normal University, Shijiazhuang 050024, China

Sustainability, 2022, vol. 14, issue 3, 1-16

Abstract: A rural built-up area is one of the most important features of rural regions. Rapid and accurate extraction of rural built-up areas has great significance to rural planning and urbanization. In this paper, the spectral residual method is embedded into a deep neural network to accurately describe the rural built-up areas from large-scale satellite images. Our proposed method is composed of two processes: coarse localization and fine extraction. Firstly, an improved Faster R-CNN (Regions with Convolutional Neural Network) detector is trained to obtain the coarse localization of the candidate built-up areas, and then the spectral residual method is used to describe the accurate boundary of each built-up area based on the bounding boxes. In the experimental part, we firstly explored the relationship between the sizes of built-up areas and the kernels in the spectral residual method. Then, the comparing experiments demonstrate that our proposed method has better performance in the extraction of rural built-up areas.

Keywords: rural built-up area extraction; remote sensing; spectral residual; deep neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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