Abstract
With continuous improvement of earth observation technology, source, and volume of remote sensing data are gradually enriched. It is critical to realize unified organization and to form data sharing service capabilities for massive remote sensing data effectively. We design a hierarchical multi-dimensional hybrid indexing model (HMDH), to address the problems in underlying organization and management, and improve query efficiency. Firstly, we establish remote sensing data grid as the smallest unit carrying and processing spatio-temporal information. We implement the construction of the HMDH in two steps, data classification based on fuzzy clustering algorithm, and classification optimization based on recursive neighborhood search algorithm. Then, we construct a hierarchical “cube” structure, filled with continuous space filling curves, to complete the coding of the HMDH. The HMDH reduces the amount of data to 6–17% and improves the accuracy to more than eight times than traditional grid model. Moreover, it can reduce the query time to 25% in some query scenarios than algorithms selected as the baseline in this paper. The HMDH model proposed can be used to solve the efficiency problems of fast and joint retrieval of remote sensing data. It extends the pattens of data sharing service and has a high application value.
Highlights
We use fuzzy clustering algorithm to achieve partition based on data features to divide remote sensing data grid into different datasets, and we use recursive neighborhood search algorithm to complete the completion of the dataset based on spatio-temporal characteristics, providing the complete description of the classification under the spatiotemporal grid and forming a structured model of multi-dimensional hybrid index
Refer to the traditional pyramid technology, the remote sensing data grid is the data grid model based on the spatial pyramid, which breaks up data according to uniform rules, associates spatio-temporal information is with data grids of different scales, and optimize data structure using data spatiotemporal attributes
We use Apache JMeter as a performance testing tool to continuously access the interface of the hierarchical multi-dimensional hybrid indexing model (HMDH) model we proposed and the traditional remote sensing data model, by virtualizing a certain number of users, to conduct efficiency tests on the remote sensing data access process, so as to complete the comparison of the retrieval efficiency of the above two models in the spatial, time and target dimension
Summary
With the continuous development of earth observation technology, data from different imaging methods coexist. Remote sensing data presents obvious "big data" characteristics, such as large capacity, high efficiency, multiple types, difficult to identify, high value, etc It has been widely used in many fields such as national defense, agriculture, water conservancy, land planning, smart cities, disaster warning, geological survey, and emergency monitoring and so on [3,4,5]. Efficient access of remote sensing data is the basis for realizing remote sensing big data management and analysis, in order to meet real-time, comprehensive, and system requirements Suffering from their discrepancies in data format, metadata structure and processing methods, remote sensing data obtained by different types of sensors are usually difficult to organize in a unified manner, easy to lead to isolated data island. Solving the consistency problem of multi-source remote sensing data organization specifications, realizing efficient organization and management of multi-source remote sensing big data, and providing multi-dimensional and efficient data retrieval capabilities, are the foci of remote sensing big data research field
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