IEEE Geoscience and Remote Sensing Magazine - December 2017 - 9
even humans in solving highly computational tasks (consider, e.g., the widely reported Go match between Google's
AlphaGo artificial intelligence program and the world Go
champion Lee Sedol). Based on such exciting successes,
deep learning is increasingly the model of choice in many
application fields.
For instance, convolutional NNs (CNNs) have proven to
be good at extracting mid- and high-level abstract features
from raw images by interleaving convolutional and pooling
layers (i.e., by spatially shrinking the feature maps layer by
layer). Recent studies indicate that the feature representations learned by CNNs are highly effective in large-scale
image recognition [2]-[4], object detection [5], [6], and semantic segmentation [7], [8]. Furthermore, recurrent NNs
(RNNs), an important branch of the deep learning family,
have demonstrated significant achievement on a variety of
tasks involved in sequential data analysis, such as action
recognition [9], [10] and image captioning [11].
In the wake of this success and thanks to the increased
availability of data and computational resources, the use of
deep learning is finally taking off in remote sensing as well.
Remote-sensing data present some new challenges for deep
learning, because satellite image analysis raises unique issues that pose difficult new scientific questions.
BRAIN IMAGE WITH MAP-IMAGE LICENSED BY INGRAM PUBLISHING, ELECTRONIC CIRCUIT BOARD-©ISTOCKPHOTO.COM/HENRIK5000
DECEMBER 2017
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
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