Abstract
With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR) has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF). In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP), gray level co-occurrence (GLCM), maximal response 8 (MR8), and scale-invariant feature transform (SIFT). In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM) dataset and the Wuhan University (WH) dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches.
Highlights
With the rapid development of remote sensing technology, the volume of acquired high-resolution remote sensing images has dramatically increased
In order to address these problems in CB-HRRS-IR, this paper proposes a novel approach using unsupervised feature learning and collaborative metric fusion
This paper proposes a robust high-resolution remote sensing Image Retrieval approach via Multiple Feature Representation and Collaborative Affinity Metric Fusion, which is called IRMFRCAMF in the following
Summary
With the rapid development of remote sensing technology, the volume of acquired high-resolution remote sensing images has dramatically increased. In order to address these problems in CB-HRRS-IR, this paper proposes a novel approach using unsupervised feature learning and collaborative metric fusion. In [14], unsupervised multilayer feature learning is proposed for high-resolution remote sensing image scene classification. To make multiple complementary features effective in CB-HRRS-IR, we utilize the graph-based cross-diffusion model [19] to measure the similarity between the query image and the test image. The proposed similarity measure approach is named collaborative metric fusion because it can collaboratively exchange information from multiple feature spaces in the fusion process.
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