Abstract
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.
Highlights
Visible near-infrared band images are obtained by sensors through detecting the electromagnetic radiation reflection of objects
In 2016, Feng et al [17] utilized the multiple kernel learning based on discriminative kernel clustering for hyperspectral band selection; Liu et al [18] proposed a band selection algorithm based on the distribution of adjacent pixels
(1) It is difficult to establish a unified band selection model due to high dimensions of Scientific Programming hyperspectral data. (2) e quality of band selection cannot directly show its effect. erefore, according to the above problems, (1) a hyperspectral band selection algorithm is constructed based on vision and (2) a subspace clustering framework is proposed based on deep adversary for realizing the preliminary clustering of spectral information
Summary
Received 16 July 2021; Revised 18 August 2021; Accepted 24 August 2021; Published 13 September 2021. Hyperspectral information can be used to express the material properties of objects, which has a strong effect on camouflage recognition. It is difficult to process it directly because of the huge hyperspectral image data. Erefore, this paper proposes a new band selection algorithm to achieve band selection by simulating visual perception. E subspace clustering selfattention 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 this band is selected as the final band, realizing the algorithm of material demarcation in this way
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