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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper aims at developing a clustering approach with spectral imagesdirectly from CASSI compressive measurements. The proposed clustering methodfirst assumes that compressed measurements lie in the union of multiplelow-dimensional subspaces. Therefore, sparse subspace clustering (SSC) is anunsupervised method that assigns compressed measurements to their respectivesubspaces. In addition, a 3D spatial regularizer is added into the SSC problem,thus taking full advantages of the spatial information contained in spectralimages. The performance of the proposed spectral image clustering approach isimproved by taking optimal CASSI measurements obtained when optimal codedapertures are used in CASSI system. Simulation with one real datasetillustrates the accuracy of the proposed spectral image clustering approach.

Spatial Sparse subspace clustering for Compressive Spectral imaging