Hyperspectral remote sensing data has been widely used in Terrain Classification for its high resolution. The
classification of urban vegetation, identified as an indispensable and essential part of urban development system, is now
facing a major challenge as different complex land-cover classes having similar spectral signatures. For a better accuracy
in classification of urban vegetation, a classifier model was designed in this paper based on genetic algorithm (GA) and
support vector machine (SVM) to address the multiclass problem, and tests were made with the classification of PHI
hyperspectral remote sensing images acquired in 2003 which partially covers a corner of the Shanghai World Exposition
Park, while PHI is a hyper-spectral sensor developed by Shanghai Institute of Technical Physics.
SVM, based on statistical learning theory and structural risk minimization, is now widely used in classification in many
fields such as two-class classification, and also the multi-class classification later due to its superior performance. On the
other hand as parameters are very important factors affecting SVM's ability in classification, therefore, how to choose
the optimal parameters turned out to be one of the most urgent problems. In this paper, GA was used to acquire the
optimal parameters with following 3 steps. Firstly, useful training samples were selected according to the features of
hyperspectral images, to build the classifier model by applying radial basis function (RBF) kernel function and decision
Directed Acyclic Graph (DAG) strategy. Secondly, GA was introduced to optimize the parameters of SVM classification
model based on the gridsearch and Bayesian algorithm. Lastly, the proposed GA-SVM model was tested for results'
accuracy comparison with the maximum likelihood estimation and neural network model. Experimental results showed
that GA-SVM model performed better classified accuracy, indicating the coupling of GA and SVM model could
improve classification accuracy of hyperspectral remote sensing images, especially in vegetation classification.