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

Hyperspectral imaging, also known as image spectrometry, is a landmarktechnique in geoscience and remote sensing (RS). In the past decade, enormousefforts have been made to process and analyze these hyperspectral (HS) productsmainly by means of seasoned experts. However, with the ever-growing volume ofdata, the bulk of costs in manpower and material resources poses new challengeson reducing the burden of manual labor and improving efficiency. For thisreason, it is, therefore, urgent to develop more intelligent and automaticapproaches for various HS RS applications. Machine learning (ML) tools withconvex optimization have successfully undertaken the tasks of numerousartificial intelligence (AI)-related applications. However, their ability inhandling complex practical problems remains limited, particularly for HS data,due to the effects of various spectral variabilities in the process of HSimaging and the complexity and redundancy of higher dimensional HS signals.Compared to the convex models, non-convex modeling, which is capable ofcharacterizing more complex real scenes and providing the modelinterpretability technically and theoretically, has been proven to be afeasible solution to reduce the gap between challenging HS vision tasks andcurrently advanced intelligent data processing models.

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing