Mapping Ausangate glacier changes using clustering techniques on cloud computing infrastructure

Victor Ayma, César Beltrán, Patrick Happ, Gilson Costa, Raul Feitosa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Earth's behavior comprehension can be achieved by the analysis of Remote Sensing data, but considering the unprecedented volumes of information currently provided by different satellites sensors, the problem can be regarded as a big data problem. Machine learning techniques have the potential to improve the analysis of this type of data; however, most current machine learning algorithms are unable to properly process such huge volumes of data. In the attempt to overcome the computational limitations related to Remote Sensing Big Data analysis, we implemented the K-Means algorithms, a clustering technique, as distributed solution, exploiting the capabilities of cloud computing infrastructure for processing very large datasets. The solution was developed over the InterCloud Data Mining Package, which is a suite of distributed classification methods, previously employed in hyperspectral image analysis. In this work we extended the functionalities of that package, by making it able to process multispectral images using the aforementioned clustering algorithm. To validate our proposal, we analyzed the Ausangate glacier, located on the Andes Mountains, in Peru, by mapping the changes in such environment through a multi-temporal Remote Sensing analysis. Our results and conclusions are focused on the thematic accuracy and the computational performance of the proposed solution. Thematic accuracy was assessed by comparing the automatically detected glacier areas with manually selected ground truth data. Moreover, we compared the computational load involved in executing the respective processes sequentially and in a distributed fashion, using a physical local machine and cloud computing infrastructure.

Original languageEnglish
Title of host publicationSeventh International Conference on Remote Sensing and Geoinformation of the Environment, RSCy 2019
EditorsKyriacos Themistocleous, Giorgos Papadavid, Silas Michaelides, Vincent Ambrosia, Diofantos G. Hadjimitsis
PublisherSPIE
ISBN (Electronic)9781510630611
DOIs
StatePublished - 2019
Event7th International Conference on Remote Sensing and Geoinformation of the Environment, RSCy 2019 - Paphos, Cyprus
Duration: 18 Mar 201921 Mar 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11174
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference7th International Conference on Remote Sensing and Geoinformation of the Environment, RSCy 2019
Country/TerritoryCyprus
CityPaphos
Period18/03/1921/03/19

Keywords

  • Big data
  • Cloud computing
  • Clustering technique
  • Glacier changes
  • Remote Sensing

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