A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data

Lilu Zhu,Kun Fu,Yanfeng Hu,Xiaolu Su,Xianqing Tai

doi:10.3390/rs13122333

Abstract

It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.

Highlights

In order to realize the unified organization and management of multi-source remote sensing data, data, eliminate eliminate the thestructural structuraldifferences differencesbetween betweendifferent different types remote senstypes ofof remote sensing ing the application of view, and analyze the internal connections the datadata fromfrom the application pointpoint of view, and analyze the internal connections in the in multimulti-dimensional feature space of remote sensing data and establish correlations, this dimensional feature space of remote sensing data and establish correlations, this paper paper proposes local association algorithm for querying data: STLAQ
In view of the current issues of remote sensing big data management and retrieval, we propose a spatio-temporal local association query algorithm, STLAQ
We design a we propose a spatio-temporal local association query algorithm, STLAQ

Summary

Introduction

The magnitude of remote sensing data has increased from GBs to TBs and PBs, and it will continue to increase in the future [1] These remote sensing data are collections of geographic information related to the location, based on a unified space-time reference [1,2,3]. Remote sensing big data mainly includes space-time reference data, geodetic survey data, gravity and magnetic data, remote sensing image data, and location-related spatial media data It has been widely used in many fields such as national defense, agriculture, water conservancy, land planning, smart cities, disaster warning, geological surveys, emergency monitoring and so on [3,4,5]. These massive remote sensing data come from different sources and have different structures, they are often potentially related to each other due to their own spatio-temporal with regard to jurisdictional claims in published maps and institutional affiliations

Methods

Results

Conclusion