Stochastic HHSVMs for Hyperspectral Image Classification

PROJECT TITLE :

Hyperspectral Imagery Classification via Stochastic HHSVMs

ABSTRACT:

The use of hyperspectral imagery (HSI) in real-world applications has demonstrated encouraging outcomes. Two key obstacles in HSI classification arise from the technological advancement of optical sensors: (1) the spectral band is often redundant and noisy, and (2) HSI with millions of pixels has become more widespread in real-world applications. For HSI applications, this study first studies the advantages of using a hybrid huberized support vector machine (HHSVM), which inherits the advantages of both lasso and ridge regression. The current HHSVM solvers are too expensive to run on large datasets. Simple and effective stochastic HHSVM algorithms for HSI classification are proposed in this study. It is shown that, in stochastic conditions, our algorithms can find an accurate solution utilising iterations with a probability of at least one-to-two. Large-scale problems can be handled by our algorithms because their convergence rate does not depend on how large the training set is. On large-scale binary and multiclass classification problems, we demonstrate the superiority of our techniques over the state-of-the art HHSVM solvers. When we apply our algorithms to real HSI categorization, we find promising results.

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