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In recent years, transfer learning with pretrained convolutional networks (CNets) has been successfully applied to land-use classification with high spatial resolution (HSR) imagery. The commonly used transfer CNets partially use the feature descriptor part of the pretained CNets, and replace the classifier part of the pretrained CNets in the old task with a new one. This causes the separation and...
Recently, efficiently representing the scenes from a large volume of high spatial resolution (HSR) images is a critical problem to be solved. Traditional scene classification problems were solved by utilizing the spatial, spectral and structural features of the HSR images separately or jointly, which lacks considering all those features of the images integrally and automatically. In this paper, we...
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