ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W4, 209-213, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W4-209-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Nov 2017
DIFET: DISTRIBUTED FEATURE EXTRACTION TOOL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES
S. Eken, E. Aydın, and A. Sayar Kocaeli University, Dept. of Computer Engineering, 41380, İzmit Kocaeli, Turkey
Keywords: Distributed Feature Extraction, Apache Hadoop, HIPI, Big Data, Remote Sensing 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.
Conference paper (PDF, 833 KB)


Citation: Eken, S., Aydın, E., and Sayar, A.: DIFET: DISTRIBUTED FEATURE EXTRACTION TOOL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W4, 209-213, https://doi.org/10.5194/isprs-annals-IV-4-W4-209-2017, 2017.

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