Understanding satellite images: a data mining module for Sentinel images
| Autor: | Mihai Datcu, Anna Pulak-Siwiec, Bartosz Kulawik, Mohanad Albughdadi, Corneliu Octavian Dumitru, Jose Lorenzo, Gottfried Schwarz |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Earth observation
010504 meteorology & atmospheric sciences Computer science classification maps 0211 other engineering and technologies 02 engineering and technology Land cover 01 natural sciences analytics Computers in Earth Sciences Data mining 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing EO Data Science business.industry lcsh:QE1-996.5 lcsh:Geography. Anthropology. Recreation 15. Life on land Computer Science Applications lcsh:Geology lcsh:G Analytics image semantics Sentinel-1 third party mission data Satellite Sentinel-2 business |
| Zdroj: | Big Earth Data, Vol 4, Iss 4, Pp 367-408 (2020) Big Earth Data |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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