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Learning powerful feature representations for image retrieval has always beena challenging task in the field of remote sensing. Traditional methods focus onextracting low-level hand-crafted features which are not only time-consumingbut also tend to achieve unsatisfactory performance due to the contentcomplexity of remote sensing images. In this paper, we investigate how toextract deep feature representations based on convolutional neural networks(CNN) for high-resolution remote sensing image retrieval (HRRSIR). To this end,two effective schemes are proposed to generate powerful feature representationsfor HRRSIR. In the first scheme, the deep features are extracted from thefully-connected and convolutional layers of the pre-trained CNN models,respectively; in the second scheme, we propose a novel CNN architecture basedon conventional convolution layers and a three-layer perceptron. The novel CNNmodel is then trained on a large remote sensing dataset to learn lowdimensional features. The two schemes are evaluated on several public andchallenging datasets, and the results indicate that the proposed schemes and inparticular the novel CNN are able to achieve state-of-the-art performance.

Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval