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

Existing deep learning-based hyperspectral image (HSI) classification worksstill suffer from the limitation of the fixed-sized receptive field, leading todifficulties in distinctive spectral-spatial features for ground objects withvarious sizes and arbitrary shapes. Meanwhile, plenty of previous works ignoreasymmetric spectral-spatial dimensions in HSI. To address the above issues, wepropose a multi-stage search architecture in order to overcome asymmetricspectral-spatial dimensions and capture significant features. First, theasymmetric pooling on the spectral-spatial dimension maximally retains theessential features of HSI. Then, the 3D convolution with a selectable range ofreceptive fields overcomes the constraints of fixed-sized convolution kernels.Finally, we extend these two searchable operations to different layers of eachstage to build the final architecture. Extensive experiments are conducted ontwo challenging HSI benchmarks including Indian Pines and Houston University,and results demonstrate the effectiveness of the proposed method with superiorperformance compared with the related works.

A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification