Abstract
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.
Highlights
Land cover information has been identified as one of the crucial data components for many aspects of global change studies and environmental applications
A convex theory-based remote sensing sample selection algorithm for support vector machine classifiers is developed in this work
Using the region of interest tool, we select samples from the image of the study area, with each category consisting of 1000 independent pixels
Summary
Land cover information has been identified as one of the crucial data components for many aspects of global change studies and environmental applications. Classification algorithm analyzes samples with selected pixels and obtains a remote sensing image classification result. Support Vector Machine (SVM) has been received increasing attention in the study of remote sensing classification [1, 2]. Convex optimization theory was applied in the algorithm to train and find a hyper-plane [13] This training process, in geometric interpretation, is equivalent to finding the nearest points among convex hulls in Hilbert spaces [14,15].The aforementioned research shows that the position of a sample relative to a convex hull (the geometric interpretation of SVM) can play an important role in classification, for identifying the relationship between training samples and SVM classification results. A convex theory-based remote sensing sample selection algorithm (CTRSSSA) for support vector machine classifiers is developed in this work.
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