Training convolutional neural networks for semantic classification of remote sensing imagery
We explore the use of convolutional neural networks for the semantic classification of remote sensing images. 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 that only need to be fine-tuned on the target data, so as to avoid overfitting problems and reduce design time. Experiments on three remote sensing datasets, with markedly different characteristics, testify on the effectiveness and wide applicability of the proposed solution, which provides a state-of-the-art performance.