Building segmentation in remote sensing images using region merging approach with convolutional neural network-based model
Image segmentation is a process used to delineate objects in an image as regions of interest (ROIs). Poor delineation can lead to over segmentation (OS), resulting in creation of small regions that do not represent meaningful segmented ROIs. Region merging is one of the common approaches used to pre...
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| Format: | Final Year Project / Dissertation / Thesis |
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2025
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| Online Access: | http://eprints.utar.edu.my/7318/1/fyp_CEA_2025_AS.pdf http://eprints.utar.edu.my/7318/ |
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| author | Shoaib, Asim |
| author_facet | Shoaib, Asim |
| author_sort | Shoaib, Asim |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Image segmentation is a process used to delineate objects in an image as regions of interest (ROIs). Poor delineation can lead to over segmentation (OS), resulting in creation of small regions that do not represent meaningful segmented ROIs. Region merging is one of the common approaches used to prevent OS in images. This approach iteratively merges adjacent regions based on merging criterion (MC) that defines the similarity of features between them. In the existing research works, feature map of labelled images were generated either manually or using a specialised software to derive MC. This process is labour intensive and time consuming. Therefore, in this research MC is derived with the assistance of convolutional neural network (CNN)-based deep learning model to perform region merging without any human intervention. In this research, AttentionU-Net model is used to generate feature map that is used to derive MC for merging building regions in WHU remote sensing images dataset. From experiments conducted, prominent features of building regions which are colour, texture, shape, and edges were extracted from the feature map
viii
to derive MC. This MC is used for merging the OS regions generated by simple linear iterative clustering (SLIC) algorithm. The proposed region merging approach has achieved an average F-measure of 0.91 in segmenting building regions in WHU remote sensing images. This is an improvement compared to previous research work on region merging, which achieved an average F-measure of 0.63 in delineating buildings regions in the same dataset. Moreover, the proposed region merging approach has achieved an average goodness of segmentation, |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.7318 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my-utar-eprints.73182026-03-03T09:49:16Z Building segmentation in remote sensing images using region merging approach with convolutional neural network-based model Shoaib, Asim T Technology (General) TD Environmental technology. Sanitary engineering Image segmentation is a process used to delineate objects in an image as regions of interest (ROIs). Poor delineation can lead to over segmentation (OS), resulting in creation of small regions that do not represent meaningful segmented ROIs. Region merging is one of the common approaches used to prevent OS in images. This approach iteratively merges adjacent regions based on merging criterion (MC) that defines the similarity of features between them. In the existing research works, feature map of labelled images were generated either manually or using a specialised software to derive MC. This process is labour intensive and time consuming. Therefore, in this research MC is derived with the assistance of convolutional neural network (CNN)-based deep learning model to perform region merging without any human intervention. In this research, AttentionU-Net model is used to generate feature map that is used to derive MC for merging building regions in WHU remote sensing images dataset. From experiments conducted, prominent features of building regions which are colour, texture, shape, and edges were extracted from the feature map viii to derive MC. This MC is used for merging the OS regions generated by simple linear iterative clustering (SLIC) algorithm. The proposed region merging approach has achieved an average F-measure of 0.91 in segmenting building regions in WHU remote sensing images. This is an improvement compared to previous research work on region merging, which achieved an average F-measure of 0.63 in delineating buildings regions in the same dataset. Moreover, the proposed region merging approach has achieved an average goodness of segmentation, 2025-05 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/7318/1/fyp_CEA_2025_AS.pdf Shoaib, Asim (2025) Building segmentation in remote sensing images using region merging approach with convolutional neural network-based model. Master dissertation/thesis, UTAR. http://eprints.utar.edu.my/7318/ |
| spellingShingle | T Technology (General) TD Environmental technology. Sanitary engineering Shoaib, Asim Building segmentation in remote sensing images using region merging approach with convolutional neural network-based model |
| title | Building segmentation in remote sensing images using region
merging approach with convolutional neural network-based model |
| title_full | Building segmentation in remote sensing images using region
merging approach with convolutional neural network-based model |
| title_fullStr | Building segmentation in remote sensing images using region
merging approach with convolutional neural network-based model |
| title_full_unstemmed | Building segmentation in remote sensing images using region
merging approach with convolutional neural network-based model |
| title_short | Building segmentation in remote sensing images using region
merging approach with convolutional neural network-based model |
| title_sort | building segmentation in remote sensing images using region
merging approach with convolutional neural network-based model |
| topic | T Technology (General) TD Environmental technology. Sanitary engineering |
| url | http://eprints.utar.edu.my/7318/1/fyp_CEA_2025_AS.pdf http://eprints.utar.edu.my/7318/ |
| url_provider | http://eprints.utar.edu.my |