Hyperspectral Image Classification With Fuzzy Spatial-Spectral Class Discriminate Information

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Conventional active learning approaches for hyperspectral image classification (HSIC) have limitations such as incrementally growing training sets without considering class structure and heterogeneity within existing and new samples. Additionally, there is limited research leveraging both spectral and spatial information jointly, and stopping criteria are not well established. This study presents a novel fuzzybased spatial-spectral Within and Between method (FLG) for preserving local and global class discriminative information. The method first explores spatial fuzziness to identify misclassified samples. It then computes total within-class and between-class information locally and globally. This information is integrated into a discriminative objective function to selectively query heterogeneous samples, mitigating randomness among training data. Experimental results on benchmark Hyperspectral datasets demonstrate the FLG improves classification accuracy across generative, extreme learning machine, and sparse multinomial logistic regression models by jointly exploiting spectral and spatial information to expand labeled training sets strategically.