%0 Journal Article %1 7486259 %A Zhang, L. %A Zhang, L. %A Du, B. %D 2016 %J IEEE Geoscience and Remote Sensing Magazine %K dl ml remote_sensing %N 2 %P 22-40 %R 10.1109/MGRS.2016.2540798 %T Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art %U http://ieeexplore.ieee.org/abstract/document/7486259/ %V 4 %X 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.

@article{7486259, 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.}, added-at = {2017-09-08T18:11:24.000+0200}, author = {Zhang, L. and Zhang, L. and Du, B.}, biburl = {https://www.bibsonomy.org/bibtex/2a891f8d5b4d20432e858690da212006c/mihail.andreev}, description = {Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art - IEEE Journals & Magazine}, doi = {10.1109/MGRS.2016.2540798}, interhash = {8e3f023f4df4cf02d411f191ef6eeae0}, intrahash = {a891f8d5b4d20432e858690da212006c}, issn = {2473-2397}, journal = {IEEE Geoscience and Remote Sensing Magazine}, keywords = {dl ml remote_sensing}, month = {June}, number = 2, pages = {22-40}, timestamp = {2017-09-08T18:11:24.000+0200}, title = {Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art}, url = {http://ieeexplore.ieee.org/abstract/document/7486259/}, volume = 4, year = 2016 }