Local structure learning in high resolution remote sensing image retrieval
By Z. Du, Xuelong Li, Xiaoqiang Lu. 2016
44 citations recorded by the source corpus.
High resolution remote sensing image captured by the satellites or the aircraft is of great help for military and civilian applications. In recent years, with an increasing amount of high resolution remote sensing images, it becomes more and more urgent to find a way to retrieve them. In this case, a few methods based on the statistical information of the local features are proposed, which have achieved good performances. However, most of the methods do not take the topological structure of the features into account. In this paper, we propose a new method to represent these images, by taking the structural information into consideration. The main contributions of this paper include: (1) mapping the features into a manifold space by a Lipschitz smooth function to enhance the representation ability of the features; (2) training an anchor set with several regularization constrains to get the intrinsic manifold structure. In the experiments, the method is applied to two challenging remote sensing image datasets: UC Merced land use dataset and Sydney dataset. Compared to the state-of-the-art approaches, the proposed method can achieve a more robust and commendable performance.
Subfields: Computer Vision and Pattern Recognition, Media Technology, Information Retrieval & Search
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Junwei Han
- Gong Cheng
- Xiaoqiang Lu
- Xuelong Li
- Xiangtao Zheng
- Binqiang Wang
- Dacheng Tao
- Peicheng Zhou
In Media Technology
- Gong Cheng
- Junwei Han
- Xiaoqiang Lu
- Xuelong Li
- Binqiang Wang
- Xiangtao Zheng
- Peicheng Zhou
- Guisong Xia
In Information Retrieval & Search
Citations in the corpus
- Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, background): “dataset is the most popular and has been widely used for the task of remote sensing image scene classification and retrieval [8, 10, 11, 14-16, 18-20, 24, 26, 28-30, 36, 38, 53, 55, 59, 61, 62, 81, 82, 84-98].”
- Exploring Models and Data for Remote Sensing Image Caption Generation (2017, background): “W ITH the development of remote sensing technology, remote sensing images have become available and attracted extensive attention in numerous applications [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15].”