Matrix computations (3rd ed.)
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing, Third Edition: Models and Methods for Image Processing
Remote Sensing, Third Edition: Models and Methods for Image Processing
Fusion of SVMs in wavelet domain for hyperspectral data classification
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
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Due to the lack of training samples, hyperspectral classification often adopts the minimum distance classification method based on spectral metrics. This paper proposes a novel multiresolution spectral-angle-based hyperspectral classification method, where band subsets will be selected to simultaneously minimize the average within-class spectral angle and maximize the average between-class spectral angle. The method adopts a pairwise classification framework (PCF), which decomposes the multiclass problem into two-class problems. Based on class separability criteria, the original set of bands is recursively decomposed into band subsets for each two-class problem. Each subset is composed of adjacent bands. Then, the subsets with high separability are selected to generate subangles, which will be combined to measure the similarity. Following the PCF, the outputs of all the two-class classifiers are combined to obtain the final output. Tested with an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data set for a six-class problem, the results demonstrate that our method outperforms the previous spectral metric-based classification methods.