Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification From VHR Imagery

Imbalanced training sets are known to produce suboptimal maps for supervised classification. Therefore, one challenge in mapping land cover is acquiring training data that will allow classification with high overall accuracy in which each class is also mapped onto similar user’s accuracy. To solve this problem, we integrated local adaptive region and boxand-whisker plot (BP) techniques into an iterative algorithm to expand the size of the training sample for selected classes in the current study. The major steps of the proposed algorithm are as follows. First, a very small initial training sample for each class set is labeled manually. Second, potential new training samples are found within an adaptive region by conducting local spectral variation analysis. Lastly, three new training samples are acquired to capture information regarding intra-class variation; these samples lie in the lower, median, and upper quartiles of BP. After adding these new training samples to the initial training sample, classification is retrained and the process is continued iteratively until termination. The proposed approach was applied to three very high resolution (VHR) remote sensing images and compared with a set of cognate methods. The comparison demonstrated that the proposed approach produced the best result in terms of overall accuracy and exhibited superiority in balancing user’s accuracy. For example, the proposed approach was typically 2%-10% more accurate than the compared methods in terms of overall accuracy and it generally yielded the most balanced classification.