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 spectrums and the 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 (FrFSSPN) is proposed to address the challenges of open-set HSIC in agricultural scenarios. First, fractional Fourier transform (FrFT) 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 the closed-set classification (CSC) 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 classes without sacrificing the classification accuracy of closed-set, and has satisfactory classification performance.