Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval
This addresses the challenge of efficient and effective image retrieval for remote sensing applications, but it is incremental as it builds on existing CNN methods.
The paper tackled the problem of learning feature representations for high-resolution remote sensing image retrieval by proposing two schemes based on convolutional neural networks, with the novel CNN architecture achieving state-of-the-art performance on public datasets.
Learning powerful feature representations for image retrieval has always been a challenging task in the field of remote sensing. Traditional methods focus on extracting low-level hand-crafted features which are not only time-consuming but also tend to achieve unsatisfactory performance due to the content complexity of remote sensing images. In this paper, we investigate how to extract 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 representations for HRRSIR. In the first scheme, the deep features are extracted from the fully-connected and convolutional layers of the pre-trained CNN models, respectively; in the second scheme, we propose a novel CNN architecture based on conventional convolution layers and a three-layer perceptron. The novel CNN model is then trained on a large remote sensing dataset to learn low dimensional features. The two schemes are evaluated on several public and challenging datasets, and the results indicate that the proposed schemes and in particular the novel CNN are able to achieve state-of-the-art performance.