Novel Grid Collection and Management Model of Remote Sensing Change Detection Samples-Reference-Cited by-同舟云学术

Novel Grid Collection and Management Model of Remote Sensing Change Detection Samples

Published:2023-11-27 Issue:23 Volume:15 Page:5528
ISSN:2072-4292
Container-title:Remote Sensing
language:en
Short-container-title:Remote Sensing

Author:

Zhu Daoye¹²³^ORCID,Han Bing²^ORCID,Silva Elisabete A.³,Li Shuang⁴^ORCID,Huang Min⁵⁶^ORCID,Ren Fuhu²,Cheng Chengqi²⁷

Affiliation:

1. College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China

2. Center for Data Science, Peking University, Beijing 100871, China

3. Lab of Interdisciplinary Spatial Analysis, University of Cambridge, Cambridge CB3 9EP, UK

4. Institute of Chinese Historical Geography, Fudan University, Shanghai 200433, China

5. School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China

6. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

7. College of Engineering, Peking University, Beijing 100871, China

Abstract

Remote sensing data have become an important data source for urban and regional change detection, owing to their advantages of authenticity, objectivity, immediacy, and low cost. The method of collection and management for remote sensing change detection samples (RS_CDS) assumes a crucial role in the effectiveness of remote sensing intelligent change detection (RSICD). To achieve rapid collection and real-time sharing of RS_CDS, this study proposes a grid collection and management model of RS_CDS based on GeoSOT (GCAM-GeoSOT), including the grid collection method of RS_CDS (GCM-SD) and grid management method of RS_CDS (GMM-SD). To verify the feasibility and retrieval efficiency of GMM-SD, Oracle and PostgreSQL databases were combined and the retrieval efficiency and database capacity were compared with the corresponding spatial databases, Oracle Spatial and PostgreSQL + PostGIS, respectively. The experimental results showed that GMM-SD not only ensures the reasonable capacity consumption of the database but also has a higher retrieval efficiency for the RS_CDS. This results in a noteworthy comprehensive performance enhancement, with a 47.63% improvement compared to Oracle Spatial and a 40.24% improvement compared to PostgreSQL + PostGIS.

Funder

National Key Research and Development Programs of China

talent startup fund of Fuzhou University

National Nature Science Foundation of China (NSFC) program

Youth Program of Major Discipline Academic and Technical Leaders Training Program of Jiangxi Talents Supporting Project

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Link

https://www.mdpi.com/2072-4292/15/23/5528/pdf

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