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

TL;DR

This paper introduces two schemes, including a novel CNN architecture, to extract deep features for high-resolution remote sensing image retrieval, achieving state-of-the-art results on public datasets.

Contribution

The paper proposes a new CNN architecture and two schemes for extracting low-dimensional deep features tailored for high-resolution remote sensing image retrieval.

Findings

Deep features from pre-trained CNNs improve retrieval performance.

The novel CNN architecture outperforms traditional models.

Proposed methods achieve state-of-the-art results on public datasets.

Abstract

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.