A New Approach for Remote Sensing Image Sample Selection Based on Convex Theory

Advancements in remote sensing technology have led to improvements in the acquisition of land cover information. The extraction of accurate and timely knowledge about land cover from remote sensing imagery largely depends on the classification techniques used. Support vector machine has been receiving considerable attention as a promising method for classifying remote sensing imagery. However, the support vector machine learning process typically requires a large memory and significant computation time for treating a large sample set, in which some of the samples might be redundant and useless for the support vector machine model training. Therefore, higher-quality and fewer samples from the sample selection should be utilized for support vector machine-based remote sensing classification. A convex theory-based remote sensing sample selection algorithm for support vector machine classifiers is developed in this work. A Landsat-5 Thematic Mapper imagery acquired on August 31, 2009 (orbit number 113/27) is adopted in our experiments. The study area's land cover/use was divided into five categories. Using the region of interest tool, we select samples from the image of the study area, with each category consisting of 1000 independent pixels. Results show that for most cases, our method can achieve higher classification accuracy than random sample selection method