Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art
Top Cited Papers
- 7 June 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Geoscience and Remote Sensing Magazine
- Vol. 4 (2), 22-40
- https://doi.org/10.1109/mgrs.2016.2540798
Abstract
Deep-learning (DL) algorithms, which learn the representative and discriminative features in a hierarchical manner from the data, have recently become a hotspot in the machine-learning area and have been introduced into the geoscience and remote sensing (RS) community for RS big data analysis. Considering the low-level features (e.g., spectral and texture) as the bottom level, the output feature representation from the top level of the network can be directly fed into a subsequent classifier for pixel-based classification. As a matter of fact, by carefully addressing the practical demands in RS applications and designing the input?output levels of the whole network, we have found that DL is actually everywhere in RS data analysis: from the traditional topics of image preprocessing, pixel-based classification, and target recognition, to the recent challenging tasks of high-level semantic feature extraction and RS scene understanding.In this technical tutorial, a general framework of DL for RS data is provided, and the state-of-the-art DL methods in RS are regarded as special cases of input-output data combined with various deep networks and tuning tricks. Although extensive experimental results confirm the excellent performance of the DL-based algorithms in RS big data analysis, even more exciting prospects can be expected for DL in RS. Key bottlenecks and potential directions are also indicated in this article, guiding further research into DL for RS data.Keywords
This publication has 101 references indexed in Scilit:
- Deep, Big, Simple Neural Nets for Handwritten Digit RecognitionNeural Computation, 2010
- Object based image analysis for remote sensingISPRS Journal of Photogrammetry and Remote Sensing, 2010
- Learning multiple layers of representationTrends in Cognitive Sciences, 2007
- A Fast Learning Algorithm for Deep Belief NetsNeural Computation, 2006
- Classification and feature extraction for remote sensing images from urban areas based on morphological transformationsIEEE Transactions on Geoscience and Remote Sensing, 2003
- Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysisIEEE Transactions on Geoscience and Remote Sensing, 2002
- A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classificationIEEE Transactions on Geoscience and Remote Sensing, 1999
- Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciencesAtmospheric Environment, 1998
- Gradient-based learning applied to document recognitionProceedings of the IEEE, 1998
- Introductory digital image processing: A remote sensing perspectiveGeocarto International, 1987