  Remote sensing image scene classification is a fundamental but challenging
task in understanding remote sensing images. Recently, deep learning-based
methods, especially convolutional neural network-based (CNN-based) methods have
shown enormous potential to understand remote sensing images. CNN-based methods
meet with success by utilizing features learned from data rather than features
designed manually. The feature-learning procedure of CNN largely depends on the
architecture of CNN. However, most of the architectures of CNN used for remote
sensing scene classification are still designed by hand which demands a
considerable amount of architecture engineering skills and domain knowledge,
and it may not play CNN's maximum potential on a special dataset. In this
paper, we proposed an automatically architecture learning procedure for remote
sensing scene classification. We designed a parameters space in which every set
of parameters represents a certain architecture of CNN (i.e., some parameters
represent the type of operators used in the architecture such as convolution,
pooling, no connection or identity, and the others represent the way how these
operators connect). To discover the optimal set of parameters for a given
dataset, we introduced a learning strategy which can allow efficient search in
the architecture space by means of gradient descent. An architecture generator
finally maps the set of parameters into the CNN used in our experiments.
