A MapReduce based Big-data Framework for Object Extraction from Mosaic Satellite Images

TL;DR

This paper introduces a distributed MapReduce-based framework for extracting objects from large-scale mosaic satellite images, enhancing scalability and resource efficiency in big data image processing.

Contribution

It presents a novel distributed framework for object extraction from satellite images, demonstrating the application of big data techniques in image processing tasks.

Findings

Framework effectively handles large satellite images.

Scalability and performance validated on real LandSat-8 data.

Extends big data applications to image processing domains.

Abstract

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.