access icon free Hyperspectral image classification based on adaptive-weighted LLE and clustering-based FSVMs

© The Institution of Engineering and Technology
Received 12/10/2017, Accepted 27/12/2017, Published 09/01/2018

An improved version of supervised locally linear embedding is proposed. In this algorithm, the weight factors of the supervised method are adaptively achieved. This method can simplify the supervised feature extraction algorithm by reducing parameters. To improve classification accuracy, a clustering-based fuzzy support vector machine (FSVM) is proposed. Different from traditional FSVMs, the proposed method constructs the fuzzy weights by inner-class clusters. In the proposed method, loose density is defined to express the compactness of the inner-class clusters. The proposed algorithm can restrain the noise and outliers by exploiting the method of endowing with smaller weight for big loose density and bigger weight for the small loose density of samples in the clusters. To inspect the performance of the proposed methods, we conduct experiments on two hyper-spectral images. Results show that the two methods are competitive among the competitors.

Inspec keywords: hyperspectral imaging; image classification; fuzzy set theory; support vector machines; feature extraction

Other keywords: supervised feature extraction algorithm; adaptive-weighted LLE; supervised locally linear embedding; hyperspectral image classification; clustering-based FSVM

Subjects: Image recognition; Combinatorial mathematics; Knowledge engineering techniques; Computer vision and image processing techniques; Combinatorial mathematics

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