Machine learning methods for remote sensing applications: an overview

Karsten Schulz; Ronny Hänsch; Uwe Sörgel

doi:10.1117/12.2503653

9 October 2018 Machine learning methods for remote sensing applications: an overview

Karsten Schulz, Ronny Hänsch, Uwe Sörgel

Proceedings Volume 10790, Earth Resources and Environmental Remote Sensing/GIS Applications IX; 1079002 (2018) https://doi.org/10.1117/12.2503653
Event: SPIE Remote Sensing, 2018, Berlin, Germany

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.

Citation Download Citation

Karsten Schulz, Ronny Hänsch, and Uwe Sörgel "Machine learning methods for remote sensing applications: an overview", Proc. SPIE 10790, Earth Resources and Environmental Remote Sensing/GIS Applications IX, 1079002 (9 October 2018); https://doi.org/10.1117/12.2503653

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available