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IEEE GEOSCIENCE AND REMOTE SENSING …
ieee GEOSCIENCE AND REMOTE SENSING magazine , IN Learning in REMOTE SENSING : A ReviewXiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, FengXu, Friedrich FraundorferAbstractThis is the pre-acceptance version, to read the final version please go to ieee GEOSCIENCE andRemote SENSING magazine on ieee at the paradigm shift towards data-intensive science, machine learning techniques arebecoming increasingly important. In particular, as a major breakthrough in the field, deep learning hasproven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key toall? Or, should we resist a black-box solution? There are controversial opinions in the REMOTE sensingcommunity. In this article, we analyze the challenges of using deep learning for REMOTE SENSING dataanalysis, review the recent advances, and provide resources to make deep learning in REMOTE sensingridiculously simple to start with.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE, IN PRESS. 3 step is to develop novel architectures for the matching of images taken from different
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