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

We present a new and effective approach for Hyperspectral Image (HSI)classification and clutter detection, overcoming a few long-standing challengespresented by HSI data characteristics. Residing in a high-dimensional spectralattribute space, HSI data samples are known to be strongly correlated in theirspectral signatures, exhibit nonlinear structure due to several physical laws,and contain uncertainty and noise from multiple sources. In the presentedapproach, we generate an adaptive, structurally enriched representationenvironment, and employ the locally linear embedding (LLE) in it. There are twostructure layers external to LLE. One is feature space embedding: the HSI dataattributes are embedded into a discriminatory feature space wherespatio-spectral coherence and distinctive structures are distilled andexploited to mitigate various difficulties encountered in the nativehyperspectral attribute space. The other structure layer encloses the ranges ofalgorithmic parameters for LLE and feature embedding, and supports amultiplexing and integrating scheme for contending with multi-sourceuncertainty. Experiments on two commonly used HSI datasets with a small numberof learning samples have rendered remarkably high-accuracy classificationresults, as well as distinctive maps of detected clutter regions.

Hyperspectral Image Classification and Clutter Detection via Multiple Structural Embeddings and Dimension Reductions