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Proceedings Paper

Machine learning methods for remote sensing applications: an overview
Author(s): Karsten Schulz; Ronny Hänsch; Uwe Sörgel
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Paper Abstract

Machine learning algorithms have shown a surprisingly successful development within the last years. Several data intensive technical and scientific fields – like search engines, speech recognition, and robotics – have an enormous benefit of these developments. Remote sensing tasks belong to data intensive applications as well. Today, remote sensing provides data over a wide range of the electromagnetic spectrum (UV, VIS, NIR, IR, and Radar). The capabilities of the sensors include single band images as well as multi- and even hyperspectral data. Due to the fact that remote sensing applications are often monitoring tasks, long time series data are in the focus of image exploitation. Several machine learning algorithms have been used in the remote sensing community since decades, ranging from basic algorithms such as PCA and K-Means to more sophisticated classification and regression frameworks like SVMs, decision trees, Random Forests, and artificial neural networks. Through a combination of data availability, algorithmic progress, and specialized hardware, deep learning methods and convolutional networks (ConvNets) came in the focus of the image exploitation community during the last years and are now on the verge between revolutionary success and illusionary hype. This overview aims to explore in which situations these new approaches are useful in remote sensing applications, which problems are actually solved, and which are still open.

Paper Details

Date Published: 9 October 2018
PDF: 11 pages
Proc. SPIE 10790, Earth Resources and Environmental Remote Sensing/GIS Applications IX, 1079002 (9 October 2018); doi: 10.1117/12.2503653
Show Author Affiliations
Karsten Schulz, Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB (Germany)
Ronny Hänsch, TU Berlin (Germany)
Uwe Sörgel, Univ. Stuttgart (Germany)

Published in SPIE Proceedings Vol. 10790:
Earth Resources and Environmental Remote Sensing/GIS Applications IX
Ulrich Michel; Karsten Schulz, Editor(s)

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