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Classification of remote sensing image using SVM kernels

机译：使用SVM内核对遥感影像进行分类

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With reference to the literature worldwide, it is obvious that Support Vector Machine (SVM), a machine learning algorithm has proven records for excellent results regarding Classification of Image. But, Remote Sensing Images are considered as most complex in nature as far as classification is concern. Supervised classification of Remote Sensing Images needs more precise machine learning models, which will be fast and efficient. SVM do satisfy researchers all over the world as far as Remote Sensing Images are concern. Basically, SVM is non-parametric statistical learning based model, which acts like binary classifier. SVM represents a group of superior machine learning algorithms, where it decomposes the parameter of the problem into a quadratic optimization technique. Hence, SVM is used to locate optimum boundaries between classes, which in return generalize to unseen samples with least error among all possible boundaries separating two classes. SVM uses density estimation function for developing easy and efficient learning parameters. Like other supervised algorithms, SVM also undergo into Training, Learning and Testing Phase for classifying any image. Besides all parameters, training sample selection and optimization is crucial part that affects the classification accuracy of remote sensing images. We need to address this issue in our project so as to devise noble algorithm or approach, which could make SVM, a more robust statistical learning model.
机译：参考世界各地的文献，很明显，支持向量机（SVM）是一种机器学习算法，已被证明具有很好的图像分类效果记录。但是，就分类而言，遥感图像被认为是最复杂的。遥感图像的监督分类需要更精确的机器学习模型，该模型将快速有效。就遥感影像而言，SVM确实使全世界的研究人员满意。基本上，SVM是基于非参数统计学习的模型，其作用类似于二进制分类器。 SVM代表了一组出色的机器学习算法，其中将问题的参数分解为二次优化技术。因此，支持向量机用于在类之间找到最佳边界，这反过来一般化为在分离两个类的所有可能边界中误差最小的未见样本。 SVM使用密度估计功能来开发简单有效的学习参数。像其他监督算法一样，SVM也进入训练，学习和测试阶段以对任何图像进行分类。除所有参数外，训练样本的选择和优化是影响遥感影像分类精度的关键部分。我们需要在我们的项目中解决这个问题，以便设计出高贵的算法或方法，从而使SVM成为更强大的统计学习模型。

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