A training sample refining method is proposed to improve the classification performance of very high-spatial resolution (VHR) remote sensing images. The proposed approach involves three major steps. First, for a given image, an initial sample set with a limited number for each class is prepared manually. Second, neighboring pixels around each available labeled pixel are gradually distinguished by an adaptive extension algorithm. When an iterative extension around the available pixel is terminated, the neighboring pixels that are within the extended region are taken into account as candidate training samples. The candidate training sample is then used to refine the signature of each initial sample. Third, when the whole available labeled pixels are scanned and processed pixel-by-pixel in the above manner, the revised training sample set is trained specially for a supervised classifier for classification. Three VHR remote sensing images with limited initial samples are used for evaluating different classifiers and advanced methods based on spatial spectral features to investigate the feasibility and performance of the proposed approach. Higher classification performance and accuracies are obtained by our proposed approach with respect to the classification maps based on the initial training sample set and an existing method that improves the initial training set by a regular window.
Bibliographical noteFunding Information:
The authors would like to express their gratitude to the editor-in-chief, the associate editor, and the reviewers for their insightful comments and suggestions. This work was supported by the National Science Foundation of China (No. 41801387 and 61701396), and the Natural Science Foundation of Shaanxi Province (No. 2018JQ4009). The authors declare no conflicts of interest.
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE).
- Contextual spatial information
- Limited training sample
- Supervised classification
- Very high-spatial resolution remote sensing image