Iterative Spatial-Spectral Training Sample Augmentation for Effective Hyperspectral Image Classification

Factors such as insufficient training samples, high-dimensional data features, and unbalanced data classes can degrade the accuracy of hyperspectral classification. To this end, this paper proposes an iterative spatial-spectral training sample augmentation (ITSA) algorithm and a new classification model incorporating maximum marginal projection (ITSA-MMP). First, ITSA iteratively augments samples by a similar region clustering strategy (SRCS) integrating spatial-spectral metric. Then, box-plot for representative sample selection (BPRSS) is adopted to screen optimal samples for the final augmented sample set (ASS). Next, based on the ASS, MMP projects the hyperspectral image into a low-dimensional subspace to explore the local structure of the data manifold and improve the inter-class separability of the data. Finally, the MMP-reduced data is classified by support vector machine. Experiments on two real hyperspectral datasets validate that ITSA-MMP can effectively increase the training sample set especially for small initial sample set and unbalanced dataset, and obtain a higher classification accuracy.