Image Spectral Data Classification Using Pixel-Purity Kernel Graph Cuts And Support Vector Machines: A Case Study Of Vegetation Identification In Indian Pine Experimental Area

Salt and pepper phenomenon of pixel-based images classification, has a major negative impacts on the accuracy of Imaging Spectra classification. Various kernel-based methods, such as Kernel graph cuts (KGC) and support vector machine (SVM), are used to solve the nonlinear problems by mapping the original nonlinear data into higher dimensional space. Four experiment schemes, including Original-Pixel-SVM (OPSVM), Original-PKGC-SVM (OPKGCSVM), PCA-PKGC-SVM (PPKGCSVM), and MNF-PKGC-SVM (MPKGCSVM), are designed to class AVRIS in India Pine of USA for comparison of classification User Accuracy (UA), Producer Accuracy (PA), Overall Accuracy (OA) and Kappa index quantitatively. The average UAs of MPKGCSVM, PPKGCSVM, OPKGCSVM and OPSVM are 91.92%, 83.09%, 84.51% and 75.55%, the average PAs are 95.33%, 91.47%, 88.03% and 87.78% their OAs are 93.57%,88.99%,85.35% and 82.36%, their Kappa indexes are 0.92,0.85,0.83 and 0.79 respectively. From MPKGCSVM to OPSVM, the OA and Kappa indexes are improved 11.21% and 0.13 respectively. Therefore, PKGC reduce the salt-and-pepper effects of classification obviously, and improve the accuracy and robustness greatly. Besides, dimensionality reduction pre-processing before PKGA of HSI with Minimum noise fraction can enhance the performance of final classification than other transformations.