BCDetNet: a deep learning architecture for building change detection from bi-temporal high resolution satellite images-Reference-Cited by-同舟云学术

BCDetNet: a deep learning architecture for building change detection from bi-temporal high resolution satellite images

Published:2023-06-25 Issue:12 Volume:14 Page:4047-4062
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Basavaraju K. S.,Hiren N. Solanki,Sravya N.,Lal Shyam^ORCID,Nalini J.,Reddy Chintala Sudhakar

Funder

Indian Space Research Organisation

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-023-01880-z.pdf

Reference47 articles.

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3. Wang D, Chen X, Jiang M, Du S, Xu B, Wang J (2021) Ads-net: An attention-based deeply supervised network for remote sensing image change detection. Int J Appl Earth Obs Geoinform 101:102348

4. Vakalopoulou M, Karatzalos K, Komodakis N, Paragios N (2015) Simultaneous registration and change detection in multitemporal, very high resolution remote sensing data. In: 2015 IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 61–69. https://doi.org/10.1109/CVPRW.2015.7301384

5. Vakalopoulou M, Platias C, Papadomanolaki M, Paragios N, Karantzalos K (2016) Simultaneous registration, segmentation and change detection from multisensor, multitemporal satellite image pairs. In: 2016 IEEE international geoscience and remote sensing symposium (IGARSS), pp. 1827–1830. https://doi.org/10.1109/IGARSS.2016.7729469

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