Digital Object Identifier 10.1109/MGRS.2016.2540798 Date of publication: xxxxxxxx Deep Learning for Remote Sensing Data A technical tutorial on the state of the art LIANGPEI ZHANG, LEFEI ZHANG, AND BO DU Advances in Machine Learning for Remote Sensing and Geosciences IMAGE LICENSED BY INGRAM PUBLISHING 22 0274-6638/16©2016IEEE IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE JUNE 2016 D eep-learning (DL) algorithms, which learn the repre- sentative 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 fea- ture 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 Digital Object Identifier 10.1109/MGRS.2016.2540798 Date of publication: 13 June 2016 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 chal- lenging 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 excel- lent 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