Proof of concept of a novel cloud computing approach for object-based remote sensing data analysis and classification

Advances in the development of Earth observation data acquisition systems have led to the continuously growing production of remote sensing datasets, for which timely analysis has become a major challenge. In this context, distributed computing technology can provide support for efficiently handling large amounts of data. Moreover, the use of distributed computing techniques, once restricted by the availability of physical computer clusters, is currently widespread due to the increasing offer of cloud computing infrastructure services. In this work, we introduce a cloud computing approach for object-based image analysis and classification of arbitrarily large remote sensing datasets. The approach is an original combination of different distributed methods which enables exploiting machine learning methods in the creation of classification models, through the use of a web-based notebook system. A prototype of the proposed approach was implemented with the methods available in the InterCloud system integrated with the Apache Zeppelin notebook system, for collaborative data analysis and visualization. In this implementation, the Apache Zeppelin system provided the means for using the scikit-learn Python machine learning library in the design of a classification model. In this work we also evaluated the approach with an object-based image land-cover classification of a GeoEye-1 scene, using resources from a commercial cloud computing infrastructure service provided. The obtained results showed the effectiveness of the approach in efficiently handling a large data volume in a scalable way, in terms of the number of allocated computing resources.