Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/201037
Title: Effective Mapping of Urban Areas Using ENVISAT ASAR, Sentinel-1A, and HJ-1-C Data
Authors: Shengxiu Zhou;Yunkai Deng;Robert Wang;Ning Li;Qi Si
Year: 2017
Publisher: IEEE
Abstract: This letter presents a methodology for urban area mapping with density-based spatial clustering of applications with noise (DBSCAN) using the Advanced Synthetic Aperture Radar (ASAR), Sentinel-1A, and HuanJing-1C data. Urban areas have a diversity of shapes, including circles, squares, strips, and other irregular shapes, and the DBSCAN clustering algorithm is suitable for identifying clusters of arbitrary shapes. Exploiting DBSCAN to extract urban areas is a key aspect of this method, and improvements via the incorporation of synthetic aperture radar data preprocessing and postprocessing also play important roles in optimizing the extractions. Different test site sizes were chosen to demonstrate the effectiveness and feasibility of the proposed method, and the validation results showed that the method is efficient and accurately extracts urban areas ranging from small towns to super metropolitan areas.
URI: http://localhost/handle/Hannan/201037
volume: 14
issue: 6
More Information: 891,
895
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7898393.pdf1.51 MBAdobe PDF
Title: Effective Mapping of Urban Areas Using ENVISAT ASAR, Sentinel-1A, and HJ-1-C Data
Authors: Shengxiu Zhou;Yunkai Deng;Robert Wang;Ning Li;Qi Si
Year: 2017
Publisher: IEEE
Abstract: This letter presents a methodology for urban area mapping with density-based spatial clustering of applications with noise (DBSCAN) using the Advanced Synthetic Aperture Radar (ASAR), Sentinel-1A, and HuanJing-1C data. Urban areas have a diversity of shapes, including circles, squares, strips, and other irregular shapes, and the DBSCAN clustering algorithm is suitable for identifying clusters of arbitrary shapes. Exploiting DBSCAN to extract urban areas is a key aspect of this method, and improvements via the incorporation of synthetic aperture radar data preprocessing and postprocessing also play important roles in optimizing the extractions. Different test site sizes were chosen to demonstrate the effectiveness and feasibility of the proposed method, and the validation results showed that the method is efficient and accurately extracts urban areas ranging from small towns to super metropolitan areas.
URI: http://localhost/handle/Hannan/201037
volume: 14
issue: 6
More Information: 891,
895
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7898393.pdf1.51 MBAdobe PDF
Title: Effective Mapping of Urban Areas Using ENVISAT ASAR, Sentinel-1A, and HJ-1-C Data
Authors: Shengxiu Zhou;Yunkai Deng;Robert Wang;Ning Li;Qi Si
Year: 2017
Publisher: IEEE
Abstract: This letter presents a methodology for urban area mapping with density-based spatial clustering of applications with noise (DBSCAN) using the Advanced Synthetic Aperture Radar (ASAR), Sentinel-1A, and HuanJing-1C data. Urban areas have a diversity of shapes, including circles, squares, strips, and other irregular shapes, and the DBSCAN clustering algorithm is suitable for identifying clusters of arbitrary shapes. Exploiting DBSCAN to extract urban areas is a key aspect of this method, and improvements via the incorporation of synthetic aperture radar data preprocessing and postprocessing also play important roles in optimizing the extractions. Different test site sizes were chosen to demonstrate the effectiveness and feasibility of the proposed method, and the validation results showed that the method is efficient and accurately extracts urban areas ranging from small towns to super metropolitan areas.
URI: http://localhost/handle/Hannan/201037
volume: 14
issue: 6
More Information: 891,
895
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7898393.pdf1.51 MBAdobe PDF