TY - GEN
T1 - On the parallel classification system using hyperspectral images for remote sensing applications
AU - Garcia-Salgado, Beatriz P.
AU - Ponomaryov, Volodymyr I.
AU - Robles-Gonzalez, Marco A.
AU - Sadovnychiy, Sergiy
N1 - Publisher Copyright:
© 2018 SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2018
Y1 - 2018
N2 - This work is orientated towards time optimization of the hyperspectral images classification. This kind of images represents an immense computational cost in the course of processing, particularly in tasks such as feature extraction and classification. In fact, numerous techniques in the state-of-the-art have suggested a reduction in the dimension of the information. Nevertheless, real-time applications require a fast information shrinkage with a feature extraction included in order to conduce to an agile classification. To solve the mentioned problem, this study is composed of a time and algorithm complexity comparison between three different transformations: Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). Furthermore, three feature selection criteria are likewise analyzed: Jeffrries-Matusita Distance (JMD), Spectral Angle Mapper (SAM) and the unsupervised algorithm N-FINDR. An application that takes into consideration the study previously described is developed performing the parallel programming paradigm in multicore mode via utilizing a cluster of two Raspberry Pi units and, comparing it in time and algorithm complexity with the sequential paradigm. Moreover, a Support Vector Machine (SVM) is incorporated in the application to perform the classification. The images employed to test the algorithms were acquired by the Hyperion sensor, the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and the Reflective Optics System Imaging Spectrometer (ROSIS).
AB - This work is orientated towards time optimization of the hyperspectral images classification. This kind of images represents an immense computational cost in the course of processing, particularly in tasks such as feature extraction and classification. In fact, numerous techniques in the state-of-the-art have suggested a reduction in the dimension of the information. Nevertheless, real-time applications require a fast information shrinkage with a feature extraction included in order to conduce to an agile classification. To solve the mentioned problem, this study is composed of a time and algorithm complexity comparison between three different transformations: Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). Furthermore, three feature selection criteria are likewise analyzed: Jeffrries-Matusita Distance (JMD), Spectral Angle Mapper (SAM) and the unsupervised algorithm N-FINDR. An application that takes into consideration the study previously described is developed performing the parallel programming paradigm in multicore mode via utilizing a cluster of two Raspberry Pi units and, comparing it in time and algorithm complexity with the sequential paradigm. Moreover, a Support Vector Machine (SVM) is incorporated in the application to perform the classification. The images employed to test the algorithms were acquired by the Hyperion sensor, the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and the Reflective Optics System Imaging Spectrometer (ROSIS).
KW - CPU multicores
KW - Feature extraction
KW - Hyperspectral image
UR - http://www.scopus.com/inward/record.url?scp=85049601002&partnerID=8YFLogxK
U2 - 10.1117/12.2303666
DO - 10.1117/12.2303666
M3 - Contribución a la conferencia
AN - SCOPUS:85049601002
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Real-Time Image and Video Processing 2018
A2 - Kehtarnavaz, Nasser
A2 - Carlsohn, Matthias F.
PB - SPIE
T2 - Real-Time Image and Video Processing 2018
Y2 - 16 April 2018 through 17 April 2018
ER -