Parallel K-dimensional tree classification based on semi-matroid structure for remote sensing applications

Yang-Lang Chang; Zhi-Ming Chen; Jin-Nan Liu; Lena Chang; Jyh Perng Fang

doi:10.1117/12.862715

24 August 2010 Parallel K-dimensional tree classification based on semi-matroid structure for remote sensing applications

Yang-Lang Chang, Zhi-Ming Chen, Jin-Nan Liu, Lena Chang, Jyh Perng Fang

Author Affiliations +

Proceedings Volume 7810, Satellite Data Compression, Communications, and Processing VI; 78100S (2010) https://doi.org/10.1117/12.862715
Event: SPIE Optical Engineering + Applications, 2010, San Diego, California, United States

Abstract

Satellite remote sensing images can be interpreted to provide important information of large-scale natural resources, such as lands, oceans, mountains, rivers, forests and minerals for Earth observations. Recent advances of remote sensing technologies have improved the availability of satellite imagery in a wide range of applications including high dimensional remote sensing data sets (e.g. high spectral and high spatial resolution images). The information of high dimensional remote sensing images obtained by state-of-the-art sensor technologies can be identified more accurately than images acquired by conventional remote sensing techniques. However, due to its large volume of image data, it requires a huge amount of storages and computing time. In response, the computational complexity of data processing for high dimensional remote sensing data analysis will increase. Consequently, this paper proposes a novel classification algorithm based on semi-matroid structure, known as the parallel k-dimensional tree semi-matroid (PKTSM) classification, which adopts a new hybrid parallel approach to deal with high dimensional data sets. It is implemented by combining the message passing interface (MPI) library, the open multi-processing (OpenMP) application programming interface and the compute unified device architecture (CUDA) of graphics processing units (GPU) in a hybrid mode. The effectiveness of the proposed PKTSM is evaluated by using MODIS/ASTER airborne simulator (MASTER) images and airborne synthetic aperture radar (AIRSAR) images for land cover classification during the Pacrim II campaign. The experimental results demonstrated that the proposed hybrid PKTSM can significantly improve the performance in terms of both computational speed-up and classification accuracy.

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Yang-Lang Chang, Zhi-Ming Chen, Jin-Nan Liu, Lena Chang, and Jyh Perng Fang "Parallel K-dimensional tree classification based on semi-matroid structure for remote sensing applications", Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 78100S (24 August 2010); https://doi.org/10.1117/12.862715

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KEYWORDS

Remote sensing

Computer architecture

Hyperspectral imaging

Library classification systems

Image classification

Synthetic aperture radar

Computing systems

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