3 research outputs found
Vectorization of Large Amounts of Raster Satellite Images in a Distributed Architecture Using HIPI
Vectorization process focus on grouping pixels of a raster image into raw
line segments, and forming lines, polylines or poligons. To vectorize massive
raster images regarding resource and performane problems, weuse a distributed
HIPI image processing interface based on MapReduce approach. Apache Hadoop is
placed at the core of the framework. To realize such a system, we first define
mapper function, and then its input and output formats. In this paper, mappers
convert raster mosaics into vector counterparts. Reduc functions are not needed
for vectorization. Vector representations of raster images is expected to give
better performance in distributed computations by reducing the negative effects
of bandwidth problem and horizontal scalability analysis is done.Comment: In Turkish, Proceedings of International Artificial Intelligence and
Data Processing Symposium (IDAP) 201
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,
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.Comment: Presented at 4th International GeoAdvances Worksho