Mapping Ausangate glacier changes using clustering techniques on cloud computing infrastructure

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. © 2019 SPIE. Downloading of the abstract is permitted for personal use only