Building Extraction from High–Resolution Remote Sensing Images by Adaptive Morphological Attribute Profile under Object Boundary Constraint
A novel adaptive morphological attribute profile under object boundary constraint (AMAP–OBC) method is proposed in this study for automatic building extraction from high-resolution remote sensing (HRRS) images. By investigating the associated attributes in morphological attribute profiles (MAPs), th...
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MDPI AG
2019-08-01
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Series: | Sensors |
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Online Access: | https://www.mdpi.com/1424-8220/19/17/3737 |
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author | Chao Wang Yi Shen Hui Liu Kaiguang Zhao Hongyan Xing Xing Qiu |
author_facet | Chao Wang Yi Shen Hui Liu Kaiguang Zhao Hongyan Xing Xing Qiu |
author_sort | Chao Wang |
collection | DOAJ |
description | A novel adaptive morphological attribute profile under object boundary constraint (AMAP–OBC) method is proposed in this study for automatic building extraction from high-resolution remote sensing (HRRS) images. By investigating the associated attributes in morphological attribute profiles (MAPs), the proposed method establishes corresponding relationships between AMAP–OBC and building characteristics in HRRS images. In the preprocessing step, the candidate object set is extracted by a group of rules for screening of non-building objects. Second, based on the proposed adaptive scale parameter extraction and object boundary constraint strategies, AMAP–OBC is conducted to obtain the initial building set. Finally, a further identification strategy with adaptive threshold combination is proposed to obtain the final building extraction results. Through experiments of multiple groups of HRRS images from different sensors, the proposed method shows outstanding performance in terms of automatic building extraction from diverse geographic objects in urban scenes. |
first_indexed | 2024-04-11T11:54:21Z |
format | Article |
id | doaj.art-8b117839c1e341da97a95584f546020d |
institution | Directory Open Access Journal |
issn | 1424-8220 |
language | English |
last_indexed | 2024-04-11T11:54:21Z |
publishDate | 2019-08-01 |
publisher | MDPI AG |
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series | Sensors |
spelling | doaj.art-8b117839c1e341da97a95584f546020d2022-12-22T04:25:14ZengMDPI AGSensors1424-82202019-08-011917373710.3390/s19173737s19173737Building Extraction from High–Resolution Remote Sensing Images by Adaptive Morphological Attribute Profile under Object Boundary ConstraintChao Wang0Yi Shen1Hui Liu2Kaiguang Zhao3Hongyan Xing4Xing Qiu5Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollege of Computer and Information Engineering, Hohai University, Nanjing 211100, ChinaCollege of Food, Agricultural, and Environmental Sciences, The Ohio State University, Wooster, OH 44691, USACollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaA novel adaptive morphological attribute profile under object boundary constraint (AMAP–OBC) method is proposed in this study for automatic building extraction from high-resolution remote sensing (HRRS) images. By investigating the associated attributes in morphological attribute profiles (MAPs), the proposed method establishes corresponding relationships between AMAP–OBC and building characteristics in HRRS images. In the preprocessing step, the candidate object set is extracted by a group of rules for screening of non-building objects. Second, based on the proposed adaptive scale parameter extraction and object boundary constraint strategies, AMAP–OBC is conducted to obtain the initial building set. Finally, a further identification strategy with adaptive threshold combination is proposed to obtain the final building extraction results. Through experiments of multiple groups of HRRS images from different sensors, the proposed method shows outstanding performance in terms of automatic building extraction from diverse geographic objects in urban scenes.https://www.mdpi.com/1424-8220/19/17/3737automatic building extractionhigh–resolutionremote sensingMAPsAMAP–OBC |
spellingShingle | Chao Wang Yi Shen Hui Liu Kaiguang Zhao Hongyan Xing Xing Qiu Building Extraction from High–Resolution Remote Sensing Images by Adaptive Morphological Attribute Profile under Object Boundary Constraint Sensors automatic building extraction high–resolution remote sensing MAPs AMAP–OBC |
title | Building Extraction from High–Resolution Remote Sensing Images by Adaptive Morphological Attribute Profile under Object Boundary Constraint |
title_full | Building Extraction from High–Resolution Remote Sensing Images by Adaptive Morphological Attribute Profile under Object Boundary Constraint |
title_fullStr | Building Extraction from High–Resolution Remote Sensing Images by Adaptive Morphological Attribute Profile under Object Boundary Constraint |
title_full_unstemmed | Building Extraction from High–Resolution Remote Sensing Images by Adaptive Morphological Attribute Profile under Object Boundary Constraint |
title_short | Building Extraction from High–Resolution Remote Sensing Images by Adaptive Morphological Attribute Profile under Object Boundary Constraint |
title_sort | building extraction from high resolution remote sensing images by adaptive morphological attribute profile under object boundary constraint |
topic | automatic building extraction high–resolution remote sensing MAPs AMAP–OBC |
url | https://www.mdpi.com/1424-8220/19/17/3737 |
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