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

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

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, BR...

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Veröffentlicht in:arXiv.org 2018-08
Hauptverfasser: Eken, Suleyman, Aydin, Eray, Sayar, Ahmet
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Computer Science - Distributed, Parallel, and Cluster Computing
Corner detection
Feature extraction
Image detection
Image processing
Landsat satellites
Remote sensing
Spatial resolution
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Beschreibung
Zusammenfassung: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1808.08521