From Event: SPIE Commercial + Scientific Sensing and Imaging, 2018
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).
Beatriz P. Garcia-Salgado, Volodymyr I. Ponomaryov, Marco A. Robles-Gonzalez, and Sergiy Sadovnychiy, "On the parallel classification system using hyperspectral images for remote sensing applications," Proc. SPIE 10670, Real-Time Image and Video Processing 2018, 1067002 (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 16, 2018; Published: 14 May 2018); https://doi.org/10.1117/12.2303666.
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