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

Data acquired from multi-channel sensors is a highly valuable asset tointerpret the environment for a variety of remote sensing applications.However, low spatial resolution is a critical limitation for previous sensorsand the constituent materials of a scene can be mixed in different fractionsdue to their spatial interactions. Spectral unmixing is a technique that allowsus to obtain the material spectral signatures and their fractions fromhyperspectral data. In this paper, we propose a novel endmember extraction andhyperspectral unmixing scheme, so called \textit{EndNet}, that is based on atwo-staged autoencoder network. This well-known structure is completelyenhanced and restructured by introducing additional layers and a projectionmetric (i.e., spectral angle distance (SAD) instead of inner product) toachieve an optimum solution. Moreover, we present a novel loss function that iscomposed of a Kullback-Leibler divergence term with SAD similarity andadditional penalty terms to improve the sparsity of the estimates. Thesemodifications enable us to set the common properties of endmembers such asnon-linearity and sparsity for autoencoder networks. Lastly, due to thestochastic-gradient based approach, the method is scalable for large-scale dataand it can be accelerated on Graphical Processing Units (GPUs). To demonstratethe superiority of our proposed method, we conduct extensive experiments onseveral well-known datasets. The results confirm that the proposed methodconsiderably improves the performance compared to the state-of-the-arttechniques in literature.

EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing