A MapReduce based Big-data Framework for Object Extraction from Mosaic Satellite Images
This work addresses scalability issues for researchers and practitioners in remote sensing and geospatial analysis, but it is incremental as it applies existing big-data techniques to a new domain.
The authors tackled the problem of extracting objects from large-scale mosaic satellite images by proposing a MapReduce-based big-data framework to address scalability and resource limitations, with effectiveness demonstrated through scalability and performance tests on real-world LandSat-8 satellite images.
We propose a framework stitching of vector representations of large scale raster mosaic images in distributed computing model. In this way, the negative effect of the lack of resources of the central system and scalability problem can be eliminated. The product obtained by this study can be used in applications requiring spatial and temporal analysis on big satellite map images. This study also shows that big data frameworks are not only used in applications of text-based data mining and machine learning algorithms, but also used in applications of algorithms in image processing. The effectiveness of the product realized with this project is also going to be proven by scalability and performance tests performed on real world LandSat-8 satellite images.