Zobrazeno 1 - 10
of 1 781
pro vyhledávání: '"M Datcu"'
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B3-2021, Pp 455-462 (2021)
When we want to extract knowledge form satellite images, several well-known image classification and analysis techniques can be concatenated or combined to gain a more detailed target understanding. In our case, we concentrated on specific extended t
Externí odkaz:
https://doaj.org/article/45540168e67347e18ae352a797ca5e08
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B2-2020, Pp 1343-1346 (2020)
These last decades, Earth Observation brought a number of new perspectives from geosciences to human activity monitoring. As more data became available, Artificial Intelligence (AI) techniques led to very successful results for understanding remote s
Externí odkaz:
https://doaj.org/article/9c2e42868c224cdaa6815d06a711b480
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-2-W16, Pp 83-89 (2019)
Today, radar imaging from space allows continuous and wide-area sea ice monitoring under nearly all weather conditions. To this end, we applied modern machine learning techniques to produce ice-describing semantic maps of the polar regions of the Ear
Externí odkaz:
https://doaj.org/article/dc314edbdfc3450ab163aa7833e4380f
Akademický článek
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Publikováno v:
Telfor Journal, Vol 8, Iss 1, Pp 20-25 (2016)
Synthetic Aperture Radar (SAR) tomography can reconstruct the elevation profile of each pixel based on a set of co-registered complex images of a scene. Its main advantage over classical interferometric methods consists in the capability to improve t
Externí odkaz:
https://doaj.org/article/7ca0dc9e79b6463c9055b8bc901f31bf
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol III-3, Pp 473-480 (2016)
This paper describes a deep learning approach to semantic segmentation of very high resolution (aerial) images. Deep neural architectures hold the promise of end-to-end learning from raw images, making heuristic feature design obsolete. Over the last
Externí odkaz:
https://doaj.org/article/9c12940b47d4460d81dfb21babafb212
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XL-1-W5, Pp 185-188 (2015)
This paper presents the semantic indexing of TerraSAR-X images and in situ data. Image processing together with machine learning methods, relevance feedback techniques, and human expertise are used to annotate the image content into a land use land c
Externí odkaz:
https://doaj.org/article/a0f56e8ad44b41948a3a2bb9f81e6779
Autor:
D. Espinoza Molina, M Datcu
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XL-7/W3, Pp 627-633 (2015)
The continuous increase in the size of the archives and in the variety and complexity of Earth-Observation (EO) sensors require new methodologies and tools that allow the end-user to access a large image repository, to extract and to infer knowledge
Externí odkaz:
https://doaj.org/article/b040dd80c18d4103938d06ec9dcccea6
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol II-3/W4, Pp 103-110 (2015)
Deep Learning techniques have lately received increased attention for achieving state-of-the-art results in many classification problems, including various vision tasks. In this work, we implement a Deep Learning technique for classifying above-groun
Externí odkaz:
https://doaj.org/article/f538232160ff40deb6d054fe2d5ca135
Akademický článek
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