Zobrazeno 1 - 10
of 49
pro vyhledávání: '"building change detection (BCD)"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 13725-13742 (2024)
In recent years, the field of remote sensing change detection (RSCD) has experienced transformative advancements through the application of convolutional neural networks (CNNs). However, inconsistencies in image quality, noise, and pseudochanges caus
Externí odkaz:
https://doaj.org/article/e8cf2b999f4542468e505d90b4a69970
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 11402-11418 (2024)
Building change detection (BCD) aims to identify new or disappeared buildings from bitemporal images. However, the varied scales and appearances of buildings, along with the challenge of pseudochange interference from complex backgrounds, make it dif
Externí odkaz:
https://doaj.org/article/28465589cd5e448da0b4c598c92ef975
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 9922-9935 (2024)
Building change detection (BCD) is a widely used method for monitoring human activities. Despite advancements in deep learning (DL) in computer vision, recent DL-based BCD methods still face challenges in extracting discriminative features due to irr
Externí odkaz:
https://doaj.org/article/f025aeb57b214d6fb6b91b6dc2154509
Autor:
Chuan Xu, Haonan Yu, Liye Mei, Ying Wang, Jian Huang, Wenying Du, Shuangtong Jin, Xinliu Li, Minglin Yu, Wei Yang, Xinghua Li
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 6174-6188 (2024)
Remote sensing (RS) image change detection (CD) methods based on deep learning, such as convolutional neural networks (CNNs) and transformers, are still spatial domain-based image processing methods by nature, and their detection accuracy is strongly
Externí odkaz:
https://doaj.org/article/21af7a11a4a949b78486f889019125cd
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 4917-4935 (2024)
Building change detection (BCD) holds significant value in the context of monitoring land use, whereas building damage assessment (BDA) plays a crucial role in expediting humanitarian rescue efforts post-disasters. To address these needs, we propose
Externí odkaz:
https://doaj.org/article/492fd7f4f00b4ea090f543397ebc982c
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 4963-4982 (2023)
Building change detection (BCD) from remote sensing images is essential in various practical applications. Recently, inspired by the achievement of deep learning in semantic segmentation (SS), methods that treat the BCD problem as a binary SS task us
Externí odkaz:
https://doaj.org/article/b3fe4d017fdc42f5bbc973fc82e1ae50
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 5065-5075 (2022)
In the study of human social development and ecological environment monitoring, building change detection (BCD) is essential. Rapid and accurate BCD in complicated scenes and multiview high-resolution (HR) remote sensing images have garnered a lot of
Externí odkaz:
https://doaj.org/article/7e25cf60b91540c9b408e523cb711c65
Publikováno v:
Remote Sensing, Vol 15, Iss 21, p 5127 (2023)
The objective of building change detection (BCD) is to discern alterations in building surfaces using bitemporal images. The superior performance and robustness of various contemporary models suggest that rapid development of BCD in the deep learning
Externí odkaz:
https://doaj.org/article/8d2c119de3e54487828316c300079b74
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