Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest
The accurate mapping of impervious surfaces is of key significance for various urban applications. Usually, traditional methods extract the proportion image of impervious surfaces from remote sensing images; however, the proportion image cannot specify where the impervious surfaces spatially distrib...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-02-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | http://www.mdpi.com/2072-4292/10/2/242 |
id |
doaj-658db08b26c2435bbc2d8e4f5a49d500 |
---|---|
record_format |
Article |
spelling |
doaj-658db08b26c2435bbc2d8e4f5a49d5002020-11-25T00:31:21ZengMDPI AGRemote Sensing2072-42922018-02-0110224210.3390/rs10020242rs10020242Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-InterestYuehong Chen0Yong Ge1Ru An2Yu Chen3School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaSchool of Earth Sciences and Engineering, Hohai University, Nanjing 210098, ChinaSchool of Geography Science, Nanjing Normal University, Nanjing 210023, ChinaThe accurate mapping of impervious surfaces is of key significance for various urban applications. Usually, traditional methods extract the proportion image of impervious surfaces from remote sensing images; however, the proportion image cannot specify where the impervious surfaces spatially distribute within a pixel. Meanwhile, impervious surfaces often locate urban areas and have a strong correlation with the relatively new big (geo)data points of interest (POIs). This study, therefore, proposed a novel impervious surfaces mapping method (super-resolution mapping of impervious surfaces, SRMIS) by combining a super-resolution mapping technique and POIs to increase the spatial resolution of impervious surfaces in proportion images and determine the accurate spatial location of impervious surfaces within each pixel. SRMIS was evaluated using a 10-m Sentinel-2 image and a 30-m Landsat 8 Operational Land Imager (OLI) image of Nanjing city, China. The experimental results show that SRMIS generated satisfactory impervious surface maps with better-classified image quality and greater accuracy than a traditional hard classifier, the two existing super-resolution mapping (SRM) methods of the subpixel-swapping algorithm, or the method using both pixel-level and subpixel-level spatial dependence. The experimental results show that the overall accuracy increase of SRMIS was from 2.34% to 5.59% compared with the hard classification method and the two SRM methods in the first experiment, while the overall accuracy of SRMIS was 1.34–3.09% greater than that of the compared methods in the second experiment. Hence, this study provides a useful solution to combining SRM techniques and the relatively new big (geo)data (i.e., POIs) to extract impervious surface maps with a higher spatial resolution than that of the input remote sensing images, and thereby supports urban research.http://www.mdpi.com/2072-4292/10/2/242super-resolution mappingimpervious surfacesspatial dependencepoints of interesturban remote sensing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuehong Chen Yong Ge Ru An Yu Chen |
spellingShingle |
Yuehong Chen Yong Ge Ru An Yu Chen Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest Remote Sensing super-resolution mapping impervious surfaces spatial dependence points of interest urban remote sensing |
author_facet |
Yuehong Chen Yong Ge Ru An Yu Chen |
author_sort |
Yuehong Chen |
title |
Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest |
title_short |
Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest |
title_full |
Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest |
title_fullStr |
Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest |
title_full_unstemmed |
Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest |
title_sort |
super-resolution mapping of impervious surfaces from remotely sensed imagery with points-of-interest |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2018-02-01 |
description |
The accurate mapping of impervious surfaces is of key significance for various urban applications. Usually, traditional methods extract the proportion image of impervious surfaces from remote sensing images; however, the proportion image cannot specify where the impervious surfaces spatially distribute within a pixel. Meanwhile, impervious surfaces often locate urban areas and have a strong correlation with the relatively new big (geo)data points of interest (POIs). This study, therefore, proposed a novel impervious surfaces mapping method (super-resolution mapping of impervious surfaces, SRMIS) by combining a super-resolution mapping technique and POIs to increase the spatial resolution of impervious surfaces in proportion images and determine the accurate spatial location of impervious surfaces within each pixel. SRMIS was evaluated using a 10-m Sentinel-2 image and a 30-m Landsat 8 Operational Land Imager (OLI) image of Nanjing city, China. The experimental results show that SRMIS generated satisfactory impervious surface maps with better-classified image quality and greater accuracy than a traditional hard classifier, the two existing super-resolution mapping (SRM) methods of the subpixel-swapping algorithm, or the method using both pixel-level and subpixel-level spatial dependence. The experimental results show that the overall accuracy increase of SRMIS was from 2.34% to 5.59% compared with the hard classification method and the two SRM methods in the first experiment, while the overall accuracy of SRMIS was 1.34–3.09% greater than that of the compared methods in the second experiment. Hence, this study provides a useful solution to combining SRM techniques and the relatively new big (geo)data (i.e., POIs) to extract impervious surface maps with a higher spatial resolution than that of the input remote sensing images, and thereby supports urban research. |
topic |
super-resolution mapping impervious surfaces spatial dependence points of interest urban remote sensing |
url |
http://www.mdpi.com/2072-4292/10/2/242 |
work_keys_str_mv |
AT yuehongchen superresolutionmappingofimpervioussurfacesfromremotelysensedimagerywithpointsofinterest AT yongge superresolutionmappingofimpervioussurfacesfromremotelysensedimagerywithpointsofinterest AT ruan superresolutionmappingofimpervioussurfacesfromremotelysensedimagerywithpointsofinterest AT yuchen superresolutionmappingofimpervioussurfacesfromremotelysensedimagerywithpointsofinterest |
_version_ |
1725322486095020032 |