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Convolutional neural networks (CNNs) have shown great potential for remote sensing image classification. As the features obtained from a deep CNN generally exhibit high generalization capacity, the subsequent classifier is normally able to provide good results without the need for careful optimization. However it is well-known that, in the pursuit of high classification results, it is generally difficult...
Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful...
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