지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.4
- 수록면
- 123 - 135 (13page)
- DOI
- 10.7848/ksgpc.2024.42.2.123
이용수
초록· 키워드
오류제보하기
Automated identification of HRBs (High-rise Buildings) on satellite images is challenging when densely populated areas are concerned. Factors that increase complexity are, among others, roads and both the azimuth and elevation angle of the sensor. In this study, two different effective HRB detection techniques are proposed. The first method is using CNNs (Convolutional Neural Networks), an extensively used tool for pattern recognition in the field of machine learning. However, domain movement considerably reduces the CNN"s performance on the test data in other domains, making it difficult to generalize. Besides, obtaining the dense annotations on the remote sensing images is expensive and time-consuming. Therefore, a new object-based approach is proposed that includes multi-resolution segmentation and relief displacement by azimuth angles of the sensor. Both methods were tested using images from four regions in South Korea using VHR (Very High Resolution) satellite imagery from the KOMPSAT-3 and WorldView-3. The results show that the performance of both methods heavily depends on factors such as building size and density as well as on external factors such as the position, shape, and size of HRBs. It can be concluded that our proposed method using the relationship between the azimuth angle of the sensor and the relief displacement of the building has several distinct advantages over the CNN-based approach. E.g. the CNN performance considerably relies on the availability of a large number of training data. In addition, quantitative evaluation showed an accuracy improvement rate of at least 30% in intersection over union and F1 score compared to the object-based benchmark models. Eventually, our proposed method allows to evaluate the performance of each image individually, which helps to identify the scenarios where a certain method works best.
목차
Abstract
1. Introduction
2. Dataset Description
3. High-rise Building Detection using Convolutional Neural Networks
4. High-rise Building Detection using object detection approach
5. Case Study Results and Discussion
6. Conclusion
References
참고문헌 (29)
참고문헌 신청
Aggarwal, C.C. (2018), Neural Networks and Deep Learning, Springer, US.
Alidoost, F. and Arefi, H. (2018), A CNN-based approach for automatic building detection and recognition of roof types using a single aerial image, PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science, Vol. 86, pp. 235-248. https://doi.org/10.1007/s41064-018-0060-5
Borovykh, A., Bohte, S., and Oosterlee, C.W. (2017), Conditional time series forecasting with convolutional neural networks, arXiv preprint arXiv:1703.04691. https://doi.org/10.48550/arXiv.1703.04691
Cao, J.N., Liao, J., Jin Zhang, B., Wang, K., and Zhao, W. (2021), Object-based change detection of very high-resolution emote sensing images incorporating multiscale uncertainty analysis by fusing pixel-based change detection, Journal of Electronic Imaging, Vol. 30, No. 5, 053003p. https://doi.org/10.1117/1.JEI.30.5.053003
Chen, Y., Tang, L., Huang, W., Guo, J., and Yang, G. (2023), A novel spectral indices-driven spectral-spatial-context attention network for automatic cloud detection, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 16, pp. 3092-3103.
Alidoost, F. and Arefi, H. (2018), A CNN-based approach for automatic building detection and recognition of roof types using a single aerial image, PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science, Vol. 86, pp. 235-248. https://doi.org/10.1007/s41064-018-0060-5
Borovykh, A., Bohte, S., and Oosterlee, C.W. (2017), Conditional time series forecasting with convolutional neural networks, arXiv preprint arXiv:1703.04691. https://doi.org/10.48550/arXiv.1703.04691
Cao, J.N., Liao, J., Jin Zhang, B., Wang, K., and Zhao, W. (2021), Object-based change detection of very high-resolution emote sensing images incorporating multiscale uncertainty analysis by fusing pixel-based change detection, Journal of Electronic Imaging, Vol. 30, No. 5, 053003p. https://doi.org/10.1117/1.JEI.30.5.053003
Chen, Y., Tang, L., Huang, W., Guo, J., and Yang, G. (2023), A novel spectral indices-driven spectral-spatial-context attention network for automatic cloud detection, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 16, pp. 3092-3103.
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