Self-Supervised Contrastive Learning Residual Network for Hyperspectral Image Classification Under Limited Labeled Samples

Author:

Yang Gan1ORCID,Wang Zhaohui1ORCID

Affiliation:

1. Faculty of Computer Science and Technology, Hainan University, China

Funder

the Hainan Key Research and Development Plan for Scientific and Technological Collaboration Projects

Publisher

ACM

Reference30 articles.

1. Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture

2. Estimating Soil Salinity Under Various Moisture Conditions: An Experimental Study

3. Wu L, Peng Y, Li C, “Hyperspectral image open set recognition based on the extreme value machine,” AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics. SPIE, 2022, vol. 11565, pp. 114-119.

4. R. Anand, S. Veni, and J. Ara inth, “Big data challenges in airborne hyperspectral image for urban landuse classification,” in Proc. 2017 Int. Conf. Adv. Comp., Com. and Inf. (ICACCI), 2017, pp. 1808-1814.

5. Classification of hyperspectral remote sensing images with support vector machines

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