Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Ronny Hänsch"'
Autor:
Xian Sun, Dongshuo Yin, Fei Qin, Hongfeng Yu, Wanxuan Lu, Fanglong Yao, Qibin He, Xingliang Huang, Zhiyuan Yan, Peijin Wang, Chubo Deng, Nayu Liu, Yiran Yang, Wei Liang, Ruiping Wang, Cheng Wang, Naoto Yokoya, Ronny Hänsch, Kun Fu
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-13 (2023)
Dumpsites are hard to locate globally. Here the authors apply deep networks to satellite images to provide an effective and low-cost way to detect dumpsites with the new method saving more than 96.8% of the manual time with a strong sensitivity to du
Externí odkaz:
https://doaj.org/article/62e27f5757914b99adb9643e00d83edd
Publikováno v:
Remote Sensing, Vol 10, Iss 11, p 1742 (2018)
In this paper, we provide the first in-depth evaluation of exploiting Tomographic Synthetic Aperture Radar (TomoSAR) for the task of supervised land-cover classification. Our main contribution is the design of specific TomoSAR features to reach this
Externí odkaz:
https://doaj.org/article/3db0362d3d2949a0a9ba48cd52c83de5
Autor:
Ronny Hänsch, Olaf Hellwich
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 7, Iss 2, p 74 (2018)
This paper proposes the use of Stacked Random Forests (SRF) for the classification of Polarimetric Synthetic Aperture Radar images. SRF apply several Random Forest instances in a sequence where each individual uses the class estimate of its predecess
Externí odkaz:
https://doaj.org/article/c844ee1629ad44df97f797c071048538
Autor:
Claudio Persello, Ronny Hänsch, Gemine Vivone, Kaiqiang Chen, Zhiyuan Yan, Deke Tang, Hai Huang, Michael Schmitt, Xian Sun
Publikováno v:
IEEE Geoscience and Remote Sensing Magazine. 11:94-97
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e18503f2486ff9caf74d361e136acad1
https://doi.org/10.1109/mgrs.2023.3240233
https://doi.org/10.1109/mgrs.2023.3240233
The Antarctic Ice Sheet is an important indicator of climate change and a major contributor to sea level rise. Hence, precise, long-term observations of surface elevation change are required to assess changes and their contribution to sea level rise.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3c1ebc6eefa5b5242d2a608259a5484
https://doi.org/10.5194/egusphere-egu23-11954
https://doi.org/10.5194/egusphere-egu23-11954
Circumboreal forests represent close to 30% of all forested areas and are changing in response to climate, with potentially important feedback mechanisms to regional and global climate through altered carbon cycles and albedo dynamics (e.g., Loranty
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::11d7973b6169771630c331de08c5fe0d
https://doi.org/10.5194/egusphere-egu23-16242
https://doi.org/10.5194/egusphere-egu23-16242
Autor:
Léa Enguehard, Birgit Heim, Stefan Kruse, Begüm Demir, Robert Jackisch, Josias Gloy, Sarah Haupt, Laura Schild, Femke Van Geffen, Veronika Döpper, Ronny Hänsch, Nicola Falco, Ulrike Herzschuh
Boreal forests, which represent roughly one-third of the world’s total forested area, provide critical ecosystem services including carbon stocks, climate feedback, permafrost stability, biodiversity, and economic benefits. Located in the northern
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a55636873935d963e3f931cb6dc26526
https://doi.org/10.5194/egusphere-egu23-7624
https://doi.org/10.5194/egusphere-egu23-7624
Autor:
Ronny Hänsch, Pengchao Wei
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-12
Random Ferns -- as a less known example of Ensemble Learning -- have been successfully applied in many Computer Vision applications ranging from keypoint matching to object detection. This paper extends the Random Fern framework to the semantic segme
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65c1903f94cba15fcb90df6a204078cf
https://doi.org/10.1109/tgrs.2021.3131418
https://doi.org/10.1109/tgrs.2021.3131418
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 59:2877-2887
The estimation of soil permittivity under fully covered grassland is a challenging task that can be approached by either model-based polarimetric decomposition techniques or data-driven machine-learning (ML) methods. In this study, we test the benefi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::282424667b4350bea4b5dc18628cfbe9
https://doi.org/10.1109/tgrs.2020.3010104
https://doi.org/10.1109/tgrs.2020.3010104
Autor:
Qi Huang, Yanbiao Ma, Yuxin Li, Colin Prieur, Kexin Feng, Naoto Yokoya, Yu Xia, Hana Malha, Pedram Ghamisi, Giorgio Licciardi, Jocelyn Chanussot, Hongyan Zhang, Ronny Hänsch
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 12375-12385 (2021)
In this paper, we elaborate on the scientific outcomes of the 2021 Data Fusion Contest (DFC2021), which was organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society, on the subject of geosp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c54c3fc5933f74518224174413d56905
https://doi.org/10.1109/jstars.2021.3130446
https://doi.org/10.1109/jstars.2021.3130446