Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/40337
Title: Parallel exploitation of a spatial-spectral classification approach for hyperspectral images on RVC-CAL
Authors: Lazcano, R.
Madroñal, D.
Fabelo, H. 
Ortega, Sarmiento 
Salvador, R.
Callicó, G. M. 
Juárez, E.
Sanz, C.
UNESCO Clasification: 220921 Espectroscopia
Keywords: Hyperspectral Imaging
Parallelism exploitation
Real-time processing
RVC-CAL
Spatial-spectral classification
Issue Date: 2017
Journal: Proceedings of SPIE - The International Society for Optical Engineering 
Conference: Conference on High-Performance Computing in Geoscience and Remote Sensing VII 
High-Performance Computing in Geoscience and Remote Sensing VII 2017 
Abstract: Hyperspectral Imaging (HI) assembles high resolution spectral information from hundreds of narrow bands across the electromagnetic spectrum, thus generating 3D data cubes in which each pixel gathers the spectral information of the reflectance of every spatial pixel. As a result, each image is composed of large volumes of data, which turns its processing into a challenge, as performance requirements have been continuously tightened. For instance, new HI applications demand real-time responses. Hence, parallel processing becomes a necessity to achieve this requirement, so the intrinsic parallelism of the algorithms must be exploited. In this paper, a spatial-spectral classification approach has been implemented using a dataflow language known as RVC-CAL. This language represents a system as a set of functional units, and its main advantage is that it simplifies the parallelization process by mapping the different blocks over different processing units. The spatial-spectral classification approach aims at refining the classification results previously obtained by using a K-Nearest Neighbors (KNN) filtering process, in which both the pixel spectral value and the spatial coordinates are considered. To do so, KNN needs two inputs: a one-band representation of the hyperspectral image and the classification results provided by a pixel-wise classifier. Thus, spatial-spectral classification algorithm is divided into three different stages: a Principal Component Analysis (PCA) algorithm for computing the one band representation of the image, a Support Vector Machine (SVM) classifier, and the KNN-based filtering algorithm. The parallelization of these algorithms shows promising results in terms of computational time, as the mapping of them over different cores presents a speedup of 2.69x when using 3 cores. Consequently, experimental results demonstrate that real-time processing of hyperspectral images is achievable.
URI: http://hdl.handle.net/10553/40337
ISBN: 9781510613249
ISSN: 0277-786X
DOI: 10.1117/12.2279613
Source: Proceedings of SPIE - The International Society for Optical Engineering[ISSN 0277-786X],v. 10430 (104300A)
Appears in Collections:Actas de congresos
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