The issue of limited labeled samples is still grave in hyperspectral image (HSI) classification. Collaborative learning promotes a solution to this issue by combining active learning (AL) and semisupervised learning. However, it has been found that the performance of wrong pseudolabels added into the iteration may seriously deteriorate classification performance. To tackle this problem, we propose a reverification of pseudolabels algorithm based on superpixels segmentation, which is tripartite, including two AL selection strategies, the first-verification procedure based on three classifiers, and reverification procedure improving the correctness of pseudolabeled samples based on superpixels segmentation. Specifically, two AL strategies mainly generate training samples sets for two check classifiers, and three classifiers are main forces to implement the first-verification for unlabeled samples with predictive labels. Subsequently, a reverification procedure based on local similarity in superpixels is applied to reverify these unlabeled samples with predictive labels passing the first-verification procedure. The proposed algorithm is tested on three widely used HSI datasets and compared with three state-of-the-art collaborative learning algorithms with onefold verification. Experimental results illustrate numerically and visually the significantly superior performance of our proposed algorithm considering the spatial information to reverify the correctness of pseudolabels to unlabeled samples.