Zobrazeno 1 - 10
of 1 738
pro vyhledávání: '"VHR"'
Autor:
Paul M. Montesano, Matthew J. Macander, Jordan Alexis Caraballo-Vega, Melanie J. Frost, Christopher S. R. Neigh, Gerald V. Frost, Glenn S. Tamkin, Mark L. Carroll
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 16526-16534 (2024)
Scientific analysis of Earth's land surface change benefits from well-characterized multispectral remotely sensed data for which models estimate and remove the effects of the atmosphere and sun-sensor geometry. Top-of-atmosphere (TOA) reflectance in
Externí odkaz:
https://doaj.org/article/594dc37b0a89445a9ad33d5f773df31f
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:
Xikun Hu, Wenlin Liu, Hao Wen, Ka-Veng Yuen, Tian Jin, Alberto Costa Nogueira Junior, Ping Zhong
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 13558-13569 (2024)
Active fire (AF) detection is essential for early warning of wildfires to help suppress and mitigate damage. This study presents an AF neural network (AF-Net) model based on object-contextual representations (OCR) for AF segmentation from very high-r
Externí odkaz:
https://doaj.org/article/5d14cdbcaaa04e6eb884ae07b12f2fd8
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 10051-10066 (2024)
Current land use classification models based on very high-resolution (VHR) remote sensing images often suffer from high sample dependence and poor transferability. To address these challenges, we propose an unsupervised multisource domain adaptation
Externí odkaz:
https://doaj.org/article/0743c9a63aaa46ad980407c015c23355
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 8888-8903 (2024)
The purpose of remote sensing image change detection (RSCD) is to detect differences between bitemporal images taken at the same place. Deep learning has been extensively used to RSCD tasks, yielding significant results in terms of result recognition
Externí odkaz:
https://doaj.org/article/48fa277c4d3e4636b9eee50801288d4c
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 7104-7123 (2024)
Given its capacity to generate 2-D fine images of observation areas, very high resolution (VHR) synthetic aperture radar (SAR) has become increasingly popular in myriad fields, including remote sensing, geoscience, and surveillance. In this article,
Externí odkaz:
https://doaj.org/article/579b1cb96f81498d99c44f548bccd692
Autor:
Myungje Choi, Alexei Lyapustin, Yujie Wang, Compton J. Tucker, Maudood N. Khan, Frederick Policelli, Christopher S. R. Neigh, Alfreda A. Hall
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 5460-5469 (2024)
The very high resolution commercial satellite constellation of Maxar offers unique opportunities for a wide range of Earth science research and applications. The key to their widespread and effective use is stable and consistent calibration. In this
Externí odkaz:
https://doaj.org/article/6378947351374f3b9add9b617137c788
Autor:
Yizhang Lin, Sicong Liu, Yongjie Zheng, Xiaohua Tong, Huan Xie, Hongming Zhu, Kecheng Du, Hui Zhao, Jie Zhang
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 3251-3261 (2024)
Multitemporal change detection (CD) plays a crucial role in the remote sensing application field. In recent years, supervised deep learning methods have shown excellent performance in detecting changes in very-high-resolution (VHR) images. However, t
Externí odkaz:
https://doaj.org/article/9c53d9a74a0e467891c0134f285bcf31
Fine-Grained Abandoned Cropland Mapping in Southern China Using Pixel Attention Contrastive Learning
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 2283-2295 (2024)
Cropland abandonment has multifaceted and controversial impacts on the natural environment and socioeconomic development. Utilizing remote sensing data offers the potential for comprehensive coverage and large-scale insights into automated abandoned
Externí odkaz:
https://doaj.org/article/31a78f74c3014c0299d708e49580fe5c
Autor:
Yang Yu, Wataru Takeuchi
Publikováno v:
Buildings, Vol 14, Iss 11, p 3585 (2024)
The difficulty in identifying collapsed houses and damaged structures in synthetic aperture radar (SAR) images after natural disasters represents a significant challenge in the monitoring of urban structural deformation using SAR. SAR image simulatio
Externí odkaz:
https://doaj.org/article/d6b111da41b14967874e0fb77dbfdf11