Object-Based Building Change Detection by Fusing Pixel-Level Change Detection Results Generated from Morphological Building Index
Change detection (CD) is an important tool in remote sensing. CD can be categorized into pixel-based change detection (PBCD) and object-based change detection (OBCD). PBCD is traditionally used because of its simple and straightforward algorithms. However, with increasing interest in very-high-resol...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/18/2952 |
| _version_ | 1797553952213958656 |
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| author | Aisha Javed Sejung Jung Won Hee Lee Youkyung Han |
| author_facet | Aisha Javed Sejung Jung Won Hee Lee Youkyung Han |
| author_sort | Aisha Javed |
| collection | DOAJ |
| description | Change detection (CD) is an important tool in remote sensing. CD can be categorized into pixel-based change detection (PBCD) and object-based change detection (OBCD). PBCD is traditionally used because of its simple and straightforward algorithms. However, with increasing interest in very-high-resolution (VHR) imagery and determining changes in small and complex objects such as buildings or roads, traditional methods showed limitations, for example, the large number of false alarms or noise in the results. Thus, researchers have focused on extending PBCD to OBCD. In this study, we proposed a method for detecting the newly built-up areas by extending PBCD results into an OBCD result through the Dempster–Shafer (D–S) theory. To this end, the morphological building index (MBI) was used to extract built-up areas in multitemporal VHR imagery. Then, three PBCD algorithms, change vector analysis, principal component analysis, and iteratively reweighted multivariate alteration detection, were applied to the MBI images. For the final CD result, the three binary change images were fused with the segmented image using the D–S theory. The results obtained from the proposed method were compared with those of PBCD, OBCD, and OBCD results generated by fusing the three binary change images using the major voting technique. Based on the accuracy assessment, the proposed method produced the highest F1-score and kappa values compared with other CD results. The proposed method can be used for detecting new buildings in built-up areas as well as changes related to demolished buildings with a low rate of false alarms and missed detections compared with other existing CD methods. |
| first_indexed | 2024-03-10T16:24:54Z |
| format | Article |
| id | doaj.art-d5397254ee9d43cf81223c53bd6168c7 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T16:24:54Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d5397254ee9d43cf81223c53bd6168c72023-11-20T13:24:16ZengMDPI AGRemote Sensing2072-42922020-09-011218295210.3390/rs12182952Object-Based Building Change Detection by Fusing Pixel-Level Change Detection Results Generated from Morphological Building IndexAisha Javed0Sejung Jung1Won Hee Lee2Youkyung Han3Department of Convergence & Fusion System Engineering, Kyungpook National University, Sangju 37224, KoreaDepartment of Spatial Information, Kyungpook National University, Daegu 41566, KoreaSchool of Convergence & Fusion System Engineering, Kyungpook National University, Sangju 37224, KoreaSchool of Convergence & Fusion System Engineering, Kyungpook National University, Sangju 37224, KoreaChange detection (CD) is an important tool in remote sensing. CD can be categorized into pixel-based change detection (PBCD) and object-based change detection (OBCD). PBCD is traditionally used because of its simple and straightforward algorithms. However, with increasing interest in very-high-resolution (VHR) imagery and determining changes in small and complex objects such as buildings or roads, traditional methods showed limitations, for example, the large number of false alarms or noise in the results. Thus, researchers have focused on extending PBCD to OBCD. In this study, we proposed a method for detecting the newly built-up areas by extending PBCD results into an OBCD result through the Dempster–Shafer (D–S) theory. To this end, the morphological building index (MBI) was used to extract built-up areas in multitemporal VHR imagery. Then, three PBCD algorithms, change vector analysis, principal component analysis, and iteratively reweighted multivariate alteration detection, were applied to the MBI images. For the final CD result, the three binary change images were fused with the segmented image using the D–S theory. The results obtained from the proposed method were compared with those of PBCD, OBCD, and OBCD results generated by fusing the three binary change images using the major voting technique. Based on the accuracy assessment, the proposed method produced the highest F1-score and kappa values compared with other CD results. The proposed method can be used for detecting new buildings in built-up areas as well as changes related to demolished buildings with a low rate of false alarms and missed detections compared with other existing CD methods.https://www.mdpi.com/2072-4292/12/18/2952pixel-based changed detection (PBCD)object-based change detection (OBCD)morphological building index (MBI)very-high resolution (VHR) imagessegmentationDempster–Shafer (D–S) theory |
| spellingShingle | Aisha Javed Sejung Jung Won Hee Lee Youkyung Han Object-Based Building Change Detection by Fusing Pixel-Level Change Detection Results Generated from Morphological Building Index Remote Sensing pixel-based changed detection (PBCD) object-based change detection (OBCD) morphological building index (MBI) very-high resolution (VHR) images segmentation Dempster–Shafer (D–S) theory |
| title | Object-Based Building Change Detection by Fusing Pixel-Level Change Detection Results Generated from Morphological Building Index |
| title_full | Object-Based Building Change Detection by Fusing Pixel-Level Change Detection Results Generated from Morphological Building Index |
| title_fullStr | Object-Based Building Change Detection by Fusing Pixel-Level Change Detection Results Generated from Morphological Building Index |
| title_full_unstemmed | Object-Based Building Change Detection by Fusing Pixel-Level Change Detection Results Generated from Morphological Building Index |
| title_short | Object-Based Building Change Detection by Fusing Pixel-Level Change Detection Results Generated from Morphological Building Index |
| title_sort | object based building change detection by fusing pixel level change detection results generated from morphological building index |
| topic | pixel-based changed detection (PBCD) object-based change detection (OBCD) morphological building index (MBI) very-high resolution (VHR) images segmentation Dempster–Shafer (D–S) theory |
| url | https://www.mdpi.com/2072-4292/12/18/2952 |
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