DIFET: Distributed Feature Extraction Tool For High Spatial Resolution Remote Sensing Images

TL;DR

This paper introduces DIFET, a distributed feature extraction tool leveraging Hadoop for processing high-resolution remote sensing images, evaluating its scalability with multiple feature detection algorithms.

Contribution

DIFET is the first distributed feature extraction tool for high-resolution remote sensing images using Hadoop, integrating multiple algorithms for robustness and scalability.

Findings

DIFET demonstrates high horizontal scalability.

The tool effectively extracts features from LandSat-8 images.

Performance varies with different feature detection algorithms.

Abstract

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.