Material Discrimination Algorithm Based on Hyperspectral Image | Zendy

Jian Zhou | Zendy; Zhuping Wang | Zendy; Yingjie Jiao | Zendy; Cong Nie | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Material Discrimination Algorithm Based on Hyperspectral Image

Author(s) -

Jian Zhou,

Zhuping Wang,

Yingjie Jiao,

Cong Nie

Publication year - 2021

Publication title -

scientific programming

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.269

H-Index - 36

eISSN - 1875-919X

pISSN - 1058-9244

DOI - 10.1155/2021/8329974

Subject(s) - camouflage , hyperspectral imaging , computer science , chromatic scale , artificial intelligence , selection (genetic algorithm) , cluster analysis , subspace topology , image (mathematics) , process (computing) , pattern recognition (psychology) , computer vision , chromatic aberration , algorithm , mathematics , combinatorics , operating system

Hyperspectral information can be used to express the material properties of objects, which has a strong effect on camouflage recognition. However, it is difficult to process it directly because of the huge hyperspectral image data. Therefore, this paper proposes a new band selection algorithm to achieve band selection by simulating visual perception. The subspace clustering self-attention adversarial network is constructed to realize the initial selection of band. According to the visual chromatic aberration principle, a model is constructed to determine the band that combines the strongest response intensity of a particular material, and then this band is selected as the final band, therefore realizing the algorithm of material demarcation in this way.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research