Novel Sample Augmentation Approach for Improving Classification Performance With High-Resolution Remote Sensing Imagery

Achieving satisfactory land cover classification performance with high-resolution remote sensing images (HRSIs) usually requires sufficient samples for a supervised classifier. However, labeling sufficient samples is labor-intensive and time-consuming. In this article, a Novel Sample Augmentation Approach (NSAA) is proposed to synthesize new samples and improve classification accuracies for HRSI when initial known samples are very limited. First, a very small sample set of each class is prepared manually for the algorithm’s initialization. Second, a sample generator based on normal cloud model is proposed, and an adaptive region growing algorithm is suggested to explore some potential samples around a known sample for parameter estimation of the sample generator. Third, to further refine the generated samples around an initial known sample, a near-to-far space constraint strategy is proposed based on the K-means clustering algorithm to improve the quality of the generated samples. The proposed sample augmentation approach is incorporated with a classifier iteratively, and a sample balancing strategy is suggested in the iterative progress. Experiment results based on six real HRSIs and compared with eight state-of-the-art methods demonstrate the feasibility and superiorities of the proposed sample augmentation approach. Moreover, the reliability and robustness of the generated samples are verified by popular deep-learning networks and typical traditional classifiers. The improvement achieved by our proposed approach is about 0.12% – 0.95% in terms of the overall accuracy.