An improved coverage-oriented retrieval algorithm for large-area remote sensing data. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Yan, Xuejing; Liu, Shibin; Liu, Wei; Dai, Qin
• Is Part Of: International journal of digital earth. Volume 15:Issue 1(2022)
• Page Start: 606
• Page End: 625
• Publication Date: 2022-12-31
• Subjects: Remote sensing data -- large-area -- data retrieval -- optimal image combination; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2022.2030816
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: With the rapid development of satellite technology, the amount of remote sensing data and demand for remote sensing data analysis over large areas are greatly increasing. Hence, it is necessary to quickly filter out an optimal dataset from massive dataset to support various remote sensing applications. However, with the improvements in temporal and spatial resolution, remote sensing data have become fragmented, which brings challenges to data retrieval. At present, most data service platforms rely on the query engines to retrieve data. Retrieval results still have a large amount of data with a high degree of overlap, which must be manually selected for further processing. This process is very labour-intensive and time-consuming. This paper proposes an improved coverage-oriented retrieval algorithm that aims to retrieve an optimal image combination with the minimum number of images closest to the imaging time of interest while maximized covering the target area. The retrieval efficiency of this algorithm was analysed by applying different implementation practices: Arcpy, PyQGIS, and GeoPandas. The experimental results confirm the effectiveness of the algorithm and suggest that the GeoPandas-based approach is most advantageous when processing large-area data.