Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
Parallel exploitation of a spatial-spectral classification approach for hyperspectral images on RVC-CAL
Year:2017
Research Areas
  • Electronic technology and of the communications
Information
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 RVCCAL. 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.
International
Si
Congress
Society of Photo-Optical Instrumentation Engineers 2017
960
Place
Varsovia
Reviewers
Si
ISBN/ISSN
CDP08UPM
10.1117/12.2279613
Start Date
11/09/2017
End Date
14/09/2017
From page
1
To page
11
"Proceedings of SPIE"
Participants
  • Autor: Raquel Lazcano Lopez (UPM)
  • Autor: Daniel Madroñal Quintin (UPM)
  • Autor: Himar Fabelo (ULPGC)
  • Autor: Samuel Ortega (ULPGC)
  • Autor: Ruben Salvador Perea (UPM)
  • Autor: Gustavo Marrero (ULPGC)
  • Autor: Eduardo Juarez Martinez (UPM)
  • Autor: Cesar Sanz Alvaro (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Diseño Electrónico y Microelectrónico
  • Centro o Instituto I+D+i: Centro de Investigación en Tecnologías del Software y Sistemas Multimedia para la Sostenibilidad (CITSEM)
  • Departamento: Ingeniería Telemática y Electrónica
S2i 2020 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)