Few‐shot real scenes convolutional anchor clustering twin network for hyperspectral image classification

Abstract

AbstractIn order to improve the performance of hyperspectral open set recognition processing with insufficient sample size, the authors propose a new supervised contrastive learning (SCL) framework (FSSCL‐OSC) that can achieve open‐set classification of hyperspectral images in scenarios with very few sample sizes, which consists of three steps. First, supervised contrastive learning (SCL) with geometric transformations is designed, which uses rotated labels as supervision to obtain lower‐level characteristics that more accurately capture various orientations and then makes use of a spectral‐spatial characteristic extraction network to maximize the utilization of HSI's spectrum and spatial information. Next, a class anchor open‐set classification module based on a twin clustering network is designed, which uses SCL and FSL to extract more specific personal information by mining the category‐invariant characteristics present in the known versus unknown class data. Finally, multiple convolution and open‐set recognition (OSC) is performed on the feature blocks. Experimental results on three classical HSI datasets show that FSSCL‐OSC provides a significant improvement over existing methods, under a sample size of only 10%, the overall accuracy reached 82.38%, 90.76% and 84.70%, respectively.