Very limited training samples pose significant challenges for hyperspectral image (HSI) classification. To address this issue, small-sample learning methods based on classical machine learning or deep learning offer promising solutions. In this article, a novel two-stage learning-based small-sample classification framework is proposed for HSIs, termed edge-preserving features-based smooth ordering (EPFSO). In the proposed EPFSO, a self-training approach and two screening mechanisms are designed to iteratively learn newly labeled samples from a vast pool of unlabeled samples, thereby enhancing classification accuracies by incorporating these additional samples into the training set. The preprocessing step involves using edge-preserving filters to extract key features and generate low-dimensional feature images. Subsequently, all samples are ordered based on spectral similarity and spatial proximity, resulting in a smooth 1-D signal. In the case of limited labeled samples, a specialized self-training approach based on linear interpolation is utilized to iteratively learn newly labeled samples from unlabeled samples. This process continues until no further labeled samples are introduced, enabling gradual improvement in classification performance. In addition, two screening mechanisms are designed into the self-training process to strike a balance between the reliability and quantity of newly labeled samples. Finally, once a sufficient number of training samples are available, a majority voting mechanism is employed to efficiently classify the remaining samples. Experimental results on three open HSI datasets demonstrate that the proposed EPFSO framework outperforms several state-of-the-art methods, including six deep learning approaches. This validates the attractiveness of using EPFSO to address the challenges associated with limited labeled samples.