Zobrazeno 1 - 10
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pro vyhledávání: '"temporal change detection"'
Data-driven deep learning models have enabled tremendous progress in change detection (CD) with the support of pixel-level annotations. However, collecting diverse data and manually annotating them is costly, laborious, and knowledge-intensive. Exist
Externí odkaz:
http://arxiv.org/abs/2412.15541
Detecting temporal changes in geographical landscapes is critical for applications like environmental monitoring and urban planning. While remote sensing data is abundant, existing vision-language models (VLMs) often fail to capture temporal dynamics
Externí odkaz:
http://arxiv.org/abs/2410.19552
Change detection (CD) is a critical task in studying the dynamics of ecosystems and human activities using multi-temporal remote sensing images. While deep learning has shown promising results in CD tasks, it requires a large number of labeled and pa
Externí odkaz:
http://arxiv.org/abs/2310.00689
Autor:
Zhan, Zisen1 (AUTHOR) 202183250030@nuist.edu.cn, Ren, Hongjin1 (AUTHOR) 202212220006@nuist.edu.cn, Xia, Min1 (AUTHOR) ws804641@student.reading.ac.uk, Lin, Haifeng2 (AUTHOR) haifeng.lin@njfu.edu.cn, Wang, Xiaoya1,3 (AUTHOR), Li, Xin2 (AUTHOR) csxinli@njfu.edu.cn
Publikováno v:
Remote Sensing. May2024, Vol. 16 Issue 10, p1765. 21p.
Autor:
Abbasi, Mohammad, Hosseiny, Benyamin, Stewart, Rodney A., Kalantari, Mohsen, Patorniti, Nicholas, Mostafa, Sherif, Awrangjeb, Mohammad
Publikováno v:
In Remote Sensing Applications: Society and Environment April 2024 34
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Akademický článek
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We introduce WECS (Wavelet Energies Correlation Sreening), an unsupervised sparse procedure to detect spatio-temporal change points on multi-temporal SAR (POLSAR) images or even on sequences of very high resolution images. The procedure is based on w
Externí odkaz:
http://arxiv.org/abs/2103.14444
Autor:
Makineci, Hasan Bilgehan1 hbmakineci@ktun.edu.tr
Publikováno v:
Nigde Omer Halisdemir University Journal of Engineering Sciences / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi. 2023, Vol. 12 Issue 2, p626-636. 11p.
Traditional change detection methods usually follow the image differencing, change feature extraction and classification framework, and their performance is limited by such simple image domain differencing and also the hand-crafted features. Recently
Externí odkaz:
http://arxiv.org/abs/2003.06583