Deep sparse representations for land use scene classification in remote sensing images
Land use scene classification which provides highlevel interpretation for remote sensing images is a challenging task, and the performance of this task mainly lies in the representative ability of extracted scene level features. Motivated by the good generalization ability of deep convolutional neural network (CNN) we design a novel method, called deep sparse representation (DSR) building scene level features via directly transferring CNN models pre trained on general purpose image dataset for land use scene classification. We first extract deep CNN features in a feedforward way, and then compute sparse codes for the deep features through optimizing a sparse coding formulation; we finally max pool the obtained sparse codes to form the image representations. Extensive experiments on a public land use scene benchmark demonstrate that the image features generated by the proposed DSR, even with a simple linear classifier, can result in remarkable classification accuracies, which now become the new state of the art performance on the benchmark.
2016 IEEE 13th International Conference on Signal Processing (ICSP)