In the high-resolution remote sensing image retrieval, it is difficult for hand-crafted features to describe the images accurately. Thus a method based on aggregating convolutional neural network(CNN) features is proposed to improve the feature representation. First, the parameters from CNN pre-trained on large-scale datasets are transferred for remote sensing images. Given input images with different sizes, the CNN features which represent local information are extracted. Then, average pooling with different pooling region sizes and bag of visual words (BoVW) are adopted to aggregate the CNN features. Pooling features and BoVW features are obtained accordingly. Finally, the above two aggregation features are utilized for remote sensing image retrieval. Experimental results demonstrate that the input image with reasonable size is capable of improving the feature representation. When the pooling region size is between 60% and 80% of the feature map, the vast majority of the results of pooling features are superior to those of the traditional average pooling method. The optimal average normalized modified retrieval rank values of pooling feature and BoVW feature are 27.31% and 21.51% lower than those of hand-crafted feature. Therefore, both the average pooling and BoVW can improve the remote sensing image retrieval performance efficiently.
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