Zobrazeno 1 - 10
of 61
pro vyhledávání: '"D. Wittich"'
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-1-W1-2023, Pp 961-970 (2023)
Deforestation is considered one of the main causes of global warming and biodiversity reduction. Therefore, early detection of deforestation processes is of paramount importance to preserve environmental resources. Currently, there is plenty of resea
Externí odkaz:
https://doaj.org/article/e91462c537a648cc9b89bc81fcfbd349
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2022, Pp 307-315 (2022)
We present an approach for detecting early signs for upcoming forest damages by training a Convolutional Neural Network (CNN) for the pixel-wise prediction of the remaining life-time (RLT) of trees in forests based on Sentinel-2 imagery. We focus on
Externí odkaz:
https://doaj.org/article/0a34dd37ac624a0e9ba6938ec7d5a708
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-3-2021, Pp 151-158 (2021)
Although very efficient in a number of application fields, deep learning based models are known to demand large amounts of labeled data for training. Particularly for remote sensing applications, responding to that demand is generally expensive and t
Externí odkaz:
https://doaj.org/article/9d20dc62df944db6957b744dbe4cfbfd
Autor:
A. K. Neves, T. S. Körting, L. M. G. Fonseca, C. D. Girolamo Neto, D. Wittich, G. A. O. P. Costa, C. Heipke
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-3-2020, Pp 505-511 (2020)
Large-scale mapping of the Brazilian Savanna (Cerrado) vegetation using remote sensing images is still a challenge due to the high spatial variability and spectral similarity of the different characteristic vegetation types (physiognomies). In this p
Externí odkaz:
https://doaj.org/article/6dea6743abdd47179297418c1fb5492e
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2020, Pp 641-648 (2020)
This paper proposes several methods for training a Convolutional Neural Network (CNN) for learning the similarity between images of silk fabrics based on multiple semantic properties of the fabrics. In the context of the EU H2020 project SILKNOW (htt
Externí odkaz:
https://doaj.org/article/b5b38c0ca1a64733a3f658ad3b5ff4da
Autor:
D. Wittich
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2020, Pp 591-598 (2020)
Fully convolutional neural networks (FCN) are successfully used for the automated pixel-wise classification of aerial images and possibly additional data. However, they require many labelled training samples to perform well. One approach addressing t
Externí odkaz:
https://doaj.org/article/eb9fbae5fa084bdb8accd38c841498f7
Autor:
D. Wittich, F. Rottensteiner
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol IV-2-W7, Pp 197-204 (2019)
Domain adaptation (DA) can drastically decrease the amount of training data needed to obtain good classification models by leveraging available data from a source domain for the classification of a new (target) domains. In this paper, we address deep
Externí odkaz:
https://doaj.org/article/44e964cebb01465aa84fa8500bfd927c
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-4, Pp 335-342 (2018)
In this work, we consider the exploitation of social media data in the context of Remote Sensing and Spatial Information Sciences. To this end, we explore a way of augmenting and integrating information represented by geo-located feature vectors into
Externí odkaz:
https://doaj.org/article/e7aae1dbabdf494b927a18e32d82d98b
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