Fractional Fourier-Based Frequency-Spatial-Spectral Prototype Network for Agricultural Hyperspectral Image Open-Set Classification.

At present, hyperspectral image classification (HSIC) technology has been warmly concerned in all walks of life, especially in agriculture. However, existing classification methods operate under the closed-set assumption, which deviates from the real world with open properties. At the same time, there are more serious phenomena of different crops with similar spectrum and same crops with different spectrum in agricultural hyperspectral data, which is also a great challenge to existing methods. In this work, a fractional Fourier based frequency-spatial-spectral prototype network is proposed to address the challenges of open-set hyperspectral image classification in agricultural scenarios. Firstly, fractional Fourier transform is introduced into the network to combine the information in the frequency domain with the spatial-spectral information, so as to expand the difference between different classes on the premise of ensuring the similarity between classes. Then, the prototype learning strategy is introduced into the network to improve the feature recognition capability of the network through prototype loss. Finally, in order to break the stubbornly closed-set property of closed-set classification method, the open-set recognition module is proposed. The difference between the prototype vector and the feature vector is used to judge the unknown class. Experiments on three agricultural hyperspectral datasets show that this method can effectively identify unknown class without sacrificing the classification accuracy of closed-set, and has satisfactory classification performance.