Hyperspectral Image Classification Based on Semisupervised Self-Learning and Multiview Information Fusion

Hyperspectral images (HSIs) have been utilized in various fields due to abundant information, whose application is to a great extent limited by the number of labeled samples. Hence, how to exploit the diverse and complementary characteristics of multiple information from different views inherent in the HSIs is very critical for improving the classification performance under the condition of small-sized training set. This letter presents a novel method to tackle the small labeled sample size problem with semisupervised self-learning (S3L) and multiview information fusion. First, a sample augmentation scheme based on S3L and high-reliable neighborhood structure is designed for realizing training set enlargement. Thus, the pseudo-labeled samples with high quality would be automatically picked out from the unlabeled data by exploiting complementary information from multiple views, i.e., semantic information, spectral bands, texture, and geospatial information. Then, we retrain the classifier by the enlarged training set and generate an intermediate classification map. Finally, we simultaneously utilize the location and gradient information of the samples to adaptively realize the refined land-cover classification. Experimental results on three widely used datasets compared with several representative methods for small-sized HSI classification validate the effectiveness of the proposed method.