Spectral-spatial classification to pattern recognition of hyperspectral imagery

Tung-Ching Su

doi:10.1117/12.946597

2 June 2012 Spectral-spatial classification to pattern recognition of hyperspectral imagery

Tung-Ching Su

Proceedings Volume 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012); 83341F (2012) https://doi.org/10.1117/12.946597
Event: Fourth International Conference on Digital Image Processing (ICDIP 2012), 2012, Kuala Lumpur, Malaysia

Abstract

Recently several spectral-spatial classification methods had been presented and applied to pattern recognition of hyperspectral imagery. However, the present methods are especially suitable for classifying images with large spatial structures in spite of the derived classification accuracies of above 90%. To classify hyperspectral images with larger as well as smaller spatial structures, a novel spectral-spatial classification method was presented and tested on an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) image with 145×145 pixels and 220 bands. Firstly, the AVIRIS image was implemented a spectral mixture analysis using minimum noise fraction (MNF). Based on the obtained n-dimensional eigenimage, support vector machine (SVM) was used to classify the AVIRIS image. Simultaneously, the eigenimage was calculated the mathematical morphology-based image gradients for the n dimensions so to obtain n watershed segmentation images. Finally, the SVM classification map was turned into several new ones through a series of post-processing. The experimental results verify that the proposed spectral-spatial classification method has the capability to detect larger as well as smaller spatial structures in hyperspectral imagery.

Citation Download Citation

Tung-Ching Su "Spectral-spatial classification to pattern recognition of hyperspectral imagery", Proc. SPIE 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012), 83341F (2 June 2012); https://doi.org/10.1117/12.946597

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KEYWORDS

Image segmentation

Image classification

Hyperspectral imaging

Pattern recognition

Mathematical morphology

Spectroscopy

Civil engineering

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