Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/94289
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | - |
dc.contributor | Research Institute for Sustainable Urban Development | - |
dc.creator | Cao, D | - |
dc.creator | Xing, H | - |
dc.creator | Wong, MS | - |
dc.creator | Kwan, MP | - |
dc.creator | Xing, H | - |
dc.creator | Meng, Y | - |
dc.date.accessioned | 2022-08-11T02:01:40Z | - |
dc.date.available | 2022-08-11T02:01:40Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/94289 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Cao, D., Xing, H., Wong, M. S., Kwan, M. P., Xing, H., & Meng, Y. (2021). A stacking ensemble deep learning model for building extraction from remote sensing images. Remote Sensing, 13(19), 3898 is available at https://doi.org/10.3390/rs13193898 | en_US |
dc.subject | Building extraction | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Remote sensing image | en_US |
dc.subject | Stacking ensemble | en_US |
dc.title | A stacking ensemble deep learning model for building extraction from remote sensing images | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 13 | - |
dc.identifier.issue | 19 | - |
dc.identifier.doi | 10.3390/rs13193898 | - |
dcterms.abstract | Automatically extracting buildings from remote sensing images with deep learning is of great significance to urban planning, disaster prevention, change detection, and other applications. Various deep learning models have been proposed to extract building information, showing both strengths and weaknesses in capturing the complex spectral and spatial characteristics of buildings in remote sensing images. To integrate the strengths of individual models and obtain fine-scale spatial and spectral building information, this study proposed a stacking ensemble deep learning model. First, an optimization method for the prediction results of the basic model is proposed based on fully connected conditional random fields (CRFs). On this basis, a stacking ensemble model (SENet) based on a sparse autoencoder integrating U-NET, SegNet, and FCN-8s models is proposed to combine the features of the optimized basic model prediction results. Utilizing several cities in Hebei Province, China as a case study, a building dataset containing attribute labels is established to assess the performance of the proposed model. The proposed SENet is compared with three individual models (U-NET, SegNet and FCN-8s), and the results show that the accuracy of SENet is 0.954, approximately 6.7%, 6.1%, and 9.8% higher than U-NET, SegNet, and FCN-8s models, respectively. The identification of building features, including colors, sizes, shapes, and shadows, is also evaluated, showing that the accuracy, recall, F1 score, and intersection over union (IoU) of the SENet model are higher than those of the three individual models. This suggests that the proposed ensemble model can effectively depict the different features of buildings and provides an alternative approach to building extraction with higher accuracy. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Remote sensing, Oct. 2021, v. 13, no. 19, 3898 | - |
dcterms.isPartOf | Remote sensing | - |
dcterms.issued | 2021-10 | - |
dc.identifier.scopus | 2-s2.0-85116253332 | - |
dc.identifier.eissn | 2072-4292 | - |
dc.identifier.artn | 3898 | - |
dc.description.validate | 202208 bckw | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a1572 | en_US |
dc.identifier.SubFormID | 45487 | en_US |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Research Institute for Sustainable Urban Development; The Chinese University of Hong Kong | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
remotesensing-13-03898.pdf | 9.8 MB | Adobe PDF | View/Open |
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