Cloud detection based on minimizing support vector count of SVM

The classifier plays an important role for cloud detection in remote sensing image. Traditional Classifiers demand excessive training samples and have risks to fall into local optimum. To solve these deficiencies, SVM was presented as the classifier to achieve cloud detection based on SVD as feature vectors. Meanwhile, the method of minimizing the support vector count was introduced to substitute cross-validation method for optimal parameters selection. Experiment over high resolution remote sensing images QuickBird showed, with this method, the correction rate of cloud detection could be higher than 99%. It also suggested support vector count could reflect the classifier's estimation accuracy and was more easy to compute. The SVM classifier established in this way, compared with BP neural network, needed fewer training samples but achieved higher accuracy, it showed better performance in cloud detection field.