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High-Resolution Remote Sensing Image Retrieval Based on CNNs from a Dimensional Perspective
2017
Remote Sensing
Because of recent advances in Convolutional Neural Networks (CNNs), traditional CNNs have been employed to extract thousands of codes as feature representations for image retrieval. In this paper, we propose that more powerful features for high-resolution remote sensing image representations can be learned using only several tens of codes; this approach can improve the retrieval accuracy and decrease the time and storage requirements. To accomplish this goal, we first investigate the learning
doi:10.3390/rs9070725
fatcat:qacnq6rizzai7ivhek5wgt5s7a