最小化支持向量数分类器的云检测

针对。感卫星图像的云检测，提出了基于最小化支持向量数分类器的云检测方案，解决传统分类器训练样本多、易陷入局部最优的问题。使用该分类器对QuickBird高分辨率。感图像进行云检测，检测正确率达99%以上。实验表明:在确定分类器内部结构参数过程中，与传统的交叉验证法相比，基于支持向量数的方法不仅能够准确预测分类器推广性能的变化趋势，从而确立最优化的参数组合，并且实现简单，大大减少了计算的复杂度。与传统的BP神经网络相比，该方法所需训练样本少，分类性能好。

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.