Exploiting representations from pre-trained convolutional neural networks for high-resolution remote sensing image retrieval

With the increasing amount of high-resolution remote sensing images, it becomes more and more urgent to retrieve remote sensing images from large archives efficiently. The existing methods are mainly based on shallow features to retrieve images, while shallow features are easily affected by artificial intervention. Recently, convolutional neural networks (CNNs) are capable of learning feature representations automatically, and CNNs pre-trained on large-scale datasets are generic. This paper exploits representations from pre-trained CNNs for high-resolution remote sensing image retrieval. CNN representations from AlexNet, VGGM, VGG16, and GoogLeNet are first transferred for high-resolution remote sensing images, and then CNN features are extracted via two approaches. One is extracting the outputs of high-level layers directly and the other is aggregating the outputs of mid-level layers by means of average pooling with different pooling regions. Given the generalization and high dimensionality of the CNN features, feature combination and feature compression are also adopted to improve the feature representation. Experimental results demonstrate that aggregated features with pooling region smaller than the feature map size perform excellently, especially for VGG16 and GoogLeNet. Shallow feature makes a great contribution to enhance the retrieval precision when combined with CNN features, and compressed features reduce redundancy effectively. Compared with the state-of-the-art methods, the proposed feature extraction methods are very simple, and the features are able to improve retrieval performance significantly.