원문정보
초록
영어
The remote sensing images are massively stored, so it is difficult for the traditional single-node mode to meet the real-time requirement for remote sensing image retrieval. In order to improve remote sensing image retrieval efficiency and accuracy, a kind of feature information MapReduce based remote sensing image retrieval algorithm is proposed in this article. Specifically, the color features and the texture features of the remote sensing image are firstly extracted, and then Map function is adopted to calculate the similarity between the remote sensing image to be retrieved and the image in the feature library according to the color features and the texture features, and finally Reduce function is adopted to collect the intermediate results of various node tasks and the remote sensing images are ranked by a descending order according to the similarity in order to obtain the remote sensing image retrieval result. The test result shows that the proposed algorithm can rapidly and accurately retrieve the remote sensing image, thus not only improving the remote sensing image retrieval efficiency, but also improving the remote sensing image retrieval accuracy.
목차
1. Introduction
2. Remote Sensing Image Feature and Similarity Matching
2.1. Remote Sensing Image Extraction
2.2. Similarity Matching
3. Mapreduce Based Remote Sensing Image Retrieval
3.1. Mapreduce Based Image Storage
3.2. Mapreduce Based Remote Sensing Image Retrieval
4. System Test and Analysis
4.1. Experiment Environment
4.2. Storage Performance Test and Analysis
4.3. Remote Sensing Image Retrieval Performance Test and Analysis
4.4. System Load Test
4.5. Remote Sensing Image Retrieval Result Comparison
5. Conclusion
References