Land Use Classification in Remote Sensing Images by Convolutional Neural Networks

1 Aug 2015 · Marco Castelluccio, Giovanni Poggi, Carlo Sansone, Luisa Verdoliva ·

We explore the use of convolutional neural networks for the semantic classification of remote sensing scenes. Two recently proposed architectures, CaffeNet and GoogLeNet, are adopted, with three different learning modalities. Besides conventional training from scratch, we resort to pre-trained networks that are only fine-tuned on the target data, so as to avoid overfitting problems and reduce design time. Experiments on two remote sensing datasets, with markedly different characteristics, testify on the effectiveness and wide applicability of the proposed solution, which guarantees a significant performance improvement over all state-of-the-art references.

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General Classification

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1x1 Convolution • Auxiliary Classifier • Average Pooling • Convolution • Dense Connections • Dropout • GoogLeNet • Inception Module • Local Response Normalization • Max Pooling • ReLU • Softmax

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