Understanding satellite images: a data mining module for Sentinel images. Issue 4 (1st October 2020)

• Record Type: Journal Article
• Authors: Dumitru, Corneliu Octavian; Schwarz, Gottfried; Pulak-Siwiec, Anna; Kulawik, Bartosz; Albughdadi, Mohanad; Lorenzo, Jose; Datcu, Mihai
• Is Part Of: Big earth data. Volume 4:Issue 4(2020)
• Page Start: 367
• Page End: 408
• Publication Date: 2020-10-01
• Subjects: Data mining -- Earth observation -- Sentinel-1 -- Sentinel-2 -- image semantics -- classification maps -- analytics -- third party mission data; Earth sciences -- Periodicals; Earth sciences -- Research -- Periodicals; Geographic information systems Periodicals; 550
• DOI: 10.1080/20964471.2020.1820168
• Languages: English
• ISSNs: 2096-4471

Abstract

ABSTRACT: The increased number of free and open Sentinel satellite images has led to new applications of these data. Among them is the systematic classification of land cover/use types based on patterns of settlements or agriculture recorded by these images, in particular, the identification and quantification of their temporal changes. In this paper, we will present guidelines and practical examples of how to obtain rapid and reliable image patch labelling results and their validation based on data mining techniques for detecting these temporal changes, and presenting these as classification maps and/or statistical analytics. This represents a new systematic validation approach for semantic image content verification. We will focus on a number of different scenarios proposed by the user community using Sentinel data. From a large number of potential use cases, we selected three main cases, namely forest monitoring, flood monitoring, and macro-economics/urban monitoring.