English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This work proposes an adaptive trace lasso regularized L1-norm based graphcut method for dimensionality reduction of Hyperspectral images, called as`Trace Lasso-L1 Graph Cut' (TL-L1GC). The underlying idea of this method is togenerate the optimal projection matrix by considering both the sparsity as wellas the correlation of the data samples. The conventional L2-norm used in theobjective function is sensitive to noise and outliers. Therefore, in this workL1-norm is utilized as a robust alternative to L2-norm. Besides, for furtherimprovement of the results, we use a penalty function of trace lasso with theL1GC method. It adaptively balances the L2-norm and L1-norm simultaneously byconsidering the data correlation along with the sparsity. We obtain the optimalprojection matrix by maximizing the ratio of between-class dispersion towithin-class dispersion using L1-norm with trace lasso as the penalty.Furthermore, an iterative procedure for this TL-L1GC method is proposed tosolve the optimization function. The effectiveness of this proposed method isevaluated on two benchmark HSI datasets.

A Trace Lasso Regularized L1-norm Graph Cut for Highly Correlated Noisy Hyperspectral Image