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Many studies [1]–[2] show that classification techniques with both spectral and spatial information are effective to overcome the similar spectral properties in hyperspectral image classification problem. Moreover, kernel-based methods have attracted much attention in the area of pattern recognition and machine learning, many researches [3]–[5] show that kernel method is computationally efficient,...
Feature extraction is an important research aspect for hyperspectral remote sensing image classification to reduce the complexity and improve the classification accuracy. In this paper, a new feature extraction method, Kernel based Local Fisher Discriminative Analysis (KLFDA), is applied to hyperspectral remote sensing processing. This method integrates the advantages of conventional supervised Fisher...
Support vector machine (SVM) has received considerable interest in hyperspectral image classification. In order to make SVM work effectively one challenge is selection of training samples. In supervised classification it is generally done by random sampling for cross validation where two issues must be addressed. One is how many training samples required to allow SVM to produce good performance and...
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