Zobrazeno 1 - 10
of 985
pro vyhledávání: '"change detection (CD)"'
Publikováno v:
Geo-spatial Information Science, Vol 27, Iss 4, Pp 1192-1211 (2024)
Remote sensing Change Detection (CD) involves identifying changing regions of interest in bi-temporal remote sensing images. CD technology has rapidly developed in recent years through the powerful learning ability of Convolutional Neural Networks (C
Externí odkaz:
https://doaj.org/article/b9d429ba53d9493eb7c935f6ab1641e8
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 17710-17724 (2024)
Self-supervised contrastive learning can help alleviating the meet of large numbers of annotated samples and learning high-level representations from unlabeled data. However, the high diversities in ground objects make it difficult to learn the featu
Externí odkaz:
https://doaj.org/article/875f43783c1049fa9564550234a12d35
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 17503-17521 (2024)
Change detection (CD) between optical and synthetic aperture radar (SAR) remote sensing (RS) image pairs is a crucial and challenging task. Because of their disparate imaging mechanisms, direct comparison between them is not feasible. To confront thi
Externí odkaz:
https://doaj.org/article/78cad47054a54e2e97365da1218c640b
Autor:
Jinbo Wang, Lingling Zhang
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 16847-16859 (2024)
In the field of remote sensing, change detection in remote sensing images is vital. It is an important tool in urban planning, land use, and resource management. By leveraging the powerful self-learning capabilities of deep learning, it cannot only s
Externí odkaz:
https://doaj.org/article/8140330476754588be4a8901250c8c90
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 15407-15419 (2024)
With the rapid development of deep learning (DL), change detection (CD) in remote sensing (RS) image has achieved remarkable success. Nevertheless, as the image resolution improves, the visual features extracted by current methods have limited expres
Externí odkaz:
https://doaj.org/article/d5168cc8e0194d6896f23614f04f9b3a
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 14908-14918 (2024)
Change detection (CD) from multitemporal remote sensing (RS) images plays a crucial role in various fields. Motivated by the advancements in deep learning within computer vision, numerous deep learning-based methodologies have been proposed for CD. H
Externí odkaz:
https://doaj.org/article/508ec1b900ea4b838f27adbd725d7b3e
Autor:
Qingwang Wang, Zheng Hong, Jiangbo Huang, Xiaobin Zhao, Jian Song, Kai Zeng, Jianwu Shi, Tao Shen
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 14080-14092 (2024)
With the rapid advancement of remote sensing technology, bitemporal remote sensing change detection (CD) techniques have also seen significant progress. However, existing CD tasks still face two challenges: 1) Variations in lighting and seasonal fact
Externí odkaz:
https://doaj.org/article/6b1bbb909c6f4001bc2a9c632302b089
Autor:
Haicheng Qu, Lijuan Zhang
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 13925-13935 (2024)
Change detection (CD) is a fundamental operation in remote sensing image interpretation. This process employs a range of image processing and recognition techniques to identify semantic alterations in the same geographical region across different tem
Externí odkaz:
https://doaj.org/article/a9e7cae987214c129da820eccbcd7495
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 13853-13865 (2024)
In recent years, convolutional neural networks have achieved good results in the field of change detection (CD) owing to their exceptional feature extraction capabilities. However, accurately detecting objects with completely changing details, given
Externí odkaz:
https://doaj.org/article/a5781628127f459baa92dcd2c3972bb8
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 12637-12648 (2024)
Deep learning (DL) algorithms are currently the most effective methods for change detection (CD) from high-resolution multispectral (MS) remote-sensing (RS) images. Because a variety of satellites are able to provide a lot of data, it is now easy to
Externí odkaz:
https://doaj.org/article/3486777c5a304ea2abb6491fd7c2998a