Zobrazeno 1 - 10
of 922
pro vyhledávání: '"building detection"'
Autor:
Ryuhei Hamaguchi
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 16710-16726 (2024)
This article presents multibranch network architecture for addressing the problem of large intraclass variation in building detection task. Previous methods solved the problem by learning single structured and shared feature space with regularization
Externí odkaz:
https://doaj.org/article/b99dd66ff1894fd1992802e66df8ecde
Auditing Geospatial Datasets for Biases: Using Global Building Datasets for Disaster Risk Management
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 12579-12590 (2024)
The presence of biases has been demonstrated in a wide range of machine learning applications; however, it is not yet widespread in the case of geospatial datasets. This study illustrates the importance of auditing geospatial datasets for biases, wit
Externí odkaz:
https://doaj.org/article/d094b8cb5e544dc790015cc24af9d463
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 129, Iss , Pp 103839- (2024)
Recent advancements have significantly improved the field of segmentation-based change detection, particularly in the context of remote-sensing images. However, change detection datasets generally lack segmentation annotations, and the required label
Externí odkaz:
https://doaj.org/article/65d6c8a776cd4d8786099e90246dbbc4
Akademický článek
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Publikováno v:
Frontiers in Earth Science, Vol 11 (2023)
Building extraction from high-resolution remote sensing images is widely used in urban planning, land resource management, and other fields. However, the significant differences between categories in high-resolution images and the impact of imaging,
Externí odkaz:
https://doaj.org/article/a9a649519d3f47efafa1c6ab021752d5
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 8191-8203 (2023)
Detection of damaged buildings is a form of object detection and is essential for disaster emergency response efforts. In recent years, deep learning has been widely used in object detection, with successful target detection models such as Faster-Rcn
Externí odkaz:
https://doaj.org/article/81f7e16715cd467fa410ece3f1834e5e
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 3364-3377 (2023)
Building detection from panchromatic (PAN) and multispectral (MS) images is an essential task for many practical applications. In this article, a dual-stream asymmetric fusion network is proposed, named DAFNet. DAFNet can achieve effective informatio
Externí odkaz:
https://doaj.org/article/b190af3ca6df458eb0f7725e0945c2e4
Publikováno v:
GIScience & Remote Sensing, Vol 59, Iss 1, Pp 1199-1225 (2022)
Building detection from very high resolution (VHR) optical remote sensing images, which is an essential but challenging task in remote sensing, has attracted increased attention in recent years. However, despite the many methods that have been develo
Externí odkaz:
https://doaj.org/article/0f058b6c55fc4ea6b99ae0828bf6f196
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 5106-5106 (2024)
In polarimetric synthetic aperture radar (SAR) images, speckle is removed by multilooking and the local covariance matrix is the main parameter of interest. In the covariance matrix from a backscatter with reflection symmetry, the terms $\bm{S_{hh}}
Externí odkaz:
https://doaj.org/article/fc0902e2dfd34f1497230fe4f90876f7