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pro vyhledávání: '"unsupervised change detection"'
Akademický článek
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Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 19451-19466 (2024)
In this article, we consider the issue of change detection (CD) for heterogeneous remote sensing images. Existing deep learning-based methods for CD usually utilize square convolution receptive fields, which do not sufficiently exploit the contextual
Externí odkaz:
https://doaj.org/article/075664dad18b4daab6d91707dd03dac1
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 10842-10861 (2024)
Due to the interference of multiplicative speckles, it is challenging to accurately detect changes in polarimetric synthetic aperture radar (PolSAR) images. Convolutional neural network has been proven to learn rich local features from PolSAR data. H
Externí odkaz:
https://doaj.org/article/441926ce1f23407f9def3a482d32dcd7
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 9880-9893 (2024)
As a fundamental task in remote sensing earth observation, hyperspectral change detection (HCD) aims to identify the changed pixels in bitemporal hyperspectral images. However, the water-absorption effect, poor weather conditions, noise and inconsist
Externí odkaz:
https://doaj.org/article/50f14ef260a945d0aa1f2e84ceef99c2
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 126, Iss , Pp 103635- (2024)
In this study, we propose a novel unsupervised Change Detection (CD) model to detect flood extent using Synthetic Aperture Radar (SAR) time series data. The proposed model is based on a spatiotemporal variational autoencoder, trained with reconstruct
Externí odkaz:
https://doaj.org/article/b955c150f0854e4dbbc467f889352631
Publikováno v:
Remote Sensing, Vol 16, Iss 8, p 1357 (2024)
Unsupervised change detection of land cover in multispectral satellite remote sensing images with a spatial resolution of 2–5 m has always been a challenging task. This paper presents a method of detecting land cover changes in high-spatial-resolut
Externí odkaz:
https://doaj.org/article/349e31706ca844939e514d25cbae5f95
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 124, Iss , Pp 103511- (2023)
Extracting difference features is a key technique for polarimetric synthetic aperture radar (PolSAR) image change detection. Although the current PolSAR change detection algorithms based on convolutional neural networks (CNNs) can capture the local i
Externí odkaz:
https://doaj.org/article/8474e1748e1c40c8990ce3e41aaf6068
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 9762-9776 (2023)
Time-series PolSAR are capable for continuous change monitoring of natural resources and urban land-covers regardless of weather and lighting conditions. However, in the big SAR data era, the scarcity of labeled PolSAR samples poses new challenge to
Externí odkaz:
https://doaj.org/article/daa21b65807c41f2a4090ca3360bffcb
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 7276-7292 (2023)
Automatic and accurate extraction of land use/cover change (LUCC) is essential for various applications, particularly studies on climate change and sustainable development. However, the automatic and accurate detection of LUCC in large-scale regions
Externí odkaz:
https://doaj.org/article/68a20753beaa49a29bc79e72dddba027
Publikováno v:
International Journal of Digital Earth, Vol 15, Iss 1, Pp 1056-1080 (2022)
The change detection (CD) of heterogeneous remote sensing images is an important but challenging task. The difficulty is to obtain the change information by directly comparing the different statistical characteristics of the images acquired by differ
Externí odkaz:
https://doaj.org/article/3fa5bd2d3c614cc9ae06734298773138