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Open AccessArticle

Context-Enabled Extraction of Large-Scale Urban Functional Zones from Very-High-Resolution Images: A Multiscale Segmentation Approach

Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China
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Remote Sens. 2019, 11(16), 1902; https://doi.org/10.3390/rs11161902
Received: 15 July 2019 / Revised: 6 August 2019 / Accepted: 10 August 2019 / Published: 14 August 2019
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Abstract

Urban functional-zone (UFZ) analysis has been widely used in many applications, including urban environment evaluation, and urban planning and management. How to extract UFZs’ spatial units which delineates UFZs’ boundaries is fundamental to urban applications, but it is still unresolved. In this study, an automatic, context-enabled multiscale image segmentation method is proposed for extracting spatial units of UFZs from very-high-resolution satellite images. First, a window independent context feature is calculated to measure context information in the form of geographic nearest-neighbor distance from a pixel to different image classes. Second, a scale-adaptive approach is proposed to determine appropriate scales for each UFZ in terms of its context information and generate the initial UFZs. Finally, the graph cuts algorithm is improved to optimize the initial UFZs. Two datasets including WorldView-2 image in Beijing and GaoFen-2 image in Nanchang are used to evaluate the proposed method. The results indicate that the proposed method can generate better results from very-high-resolution satellite images than widely used approaches like image tiles and road blocks in representing UFZs. In addition, the proposed method outperforms existing methods in both segmentation quality and running time. Therefore, the proposed method appears to be promising and practical for segmenting large-scale UFZs. View Full-Text
Keywords: very-high-resolution image; urban functional zone; multiresolution segmentation; GEOBIA; urban land use very-high-resolution image; urban functional zone; multiresolution segmentation; GEOBIA; urban land use
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Du, S.; Du, S.; Liu, B.; Zhang, X. Context-Enabled Extraction of Large-Scale Urban Functional Zones from Very-High-Resolution Images: A Multiscale Segmentation Approach. Remote Sens. 2019, 11, 1902.

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