Hyperspectral image open set recognition based on the extreme value machine

Luting Wu; Yuanxi Peng; Chunchao Li

doi:10.1117/12.2577081

5 November 2020 Hyperspectral image open set recognition based on the extreme value machine

Luting Wu, Yuanxi Peng, Chunchao Li

Proceedings Volume 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics; 115650Q (2020) https://doi.org/10.1117/12.2577081
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

Hyperspectral remote sensing technology has made good progress in recent years and is often used in military and civil fields. Hyperspectral images(HIS) are three-dimensional data composed of two dimensional spatial information and one dimensional spectral information of ground objects, which can be used to classify and study HIS. The current research is generally based on closed sets, that is, the classes appearing in the testing time all appear during the training time. However, the setting of this closed set is difficult to achieve in the real situation, so the concept of open sets is introduced, that is, unknown targets that may appear in the testing time but do not appear during the training time. However, there are few effective algorithms to study the open set of HIS. To solve this problem, we propose an open set recognition method for HIS based on Extreme Value Machine(EVM). The pre-processed HIS data was used as the input of EVM algorithm and the EVM model based on Weibull distribution was established. The test data were used to detect the classification of unknown targets and known targets. Compared with other classification algorithms, EVM can classify known targets in HIS and detect unknown targets with good accuracy.

Citation Download Citation

Luting Wu, Yuanxi Peng, and Chunchao Li "Hyperspectral image open set recognition based on the extreme value machine", Proc. SPIE 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics, 115650Q (5 November 2020); https://doi.org/10.1117/12.2577081

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KEYWORDS

Hyperspectral imaging

Data modeling

Detection and tracking algorithms

Target detection

Hyperspectral target detection

Visualization

Image classification

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