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Deep Filter Banks for Land-Use Scene Classification


Scalable and Secure Big Data I
Abstract


Land-use (LU) scene classification is one of the most challenging tasks in the field of remote sensing (RS) image processing due to its high intraclass variability and low interclass distance. Motivated by the challenge posed by this problem, we propose a novel hybrid architecture, deep filter banks, combining multicolumn stacked denoising sparse autoencoder (SDSAE) and Fisher vector (FV) to automatically learn the representative and discriminative features in a hierarchical manner for LU scene classification. SDSAE kernels describe local patches and a robust global feature of the RS image is built through the FV pooling layer. Unlike previous handcrafted features, we use machine-learning mechanisms to optimize our proposed feature extractor so that it can learn more suitable internal features from the RS data, boosting the final performance. Our approach achieves superior performance compared with the state-of-the-art methods, obtaining average classification accuracies of 92.7% and 90.4%, respectively, on the UC Merced and RSSCN7 data sets.

KeyWords
Deep filter banks, Fisher vector (FV), land-use (LU) scene classification, stacked denoising sparse autoencoder (SDSAE).



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