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Classification of remote sensing image using SVM kernels

机译：使用SVM内核进行遥感图像的分类

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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：在全球范围内，显而易见的是，支持向量机（SVM），机器学习算法已经证明了关于图像分类的优异成果的记录。但是，只要分类是关注的，遥感图像被认为是最复杂的。监督遥感图像的分类需要更精确的机器学习模型，这将是快速有效的。就遥感图像关注，SVM为全世界的研究人员满足世界各地的研究人员。基本上，SVM是基于非参数统计学习的模型，其起法类似二进制分类器。 SVM表示一组优质机器学习算法，其中将问题的参数分解为二次优化技术。因此，SVM用于定位类之间的最佳边界，这在返回中，这在返回中，以在分离两个类的所有可能边界之间的错误中的不均义样本。 SVM使用密度估计功能来开发简单高效的学习参数。与其他监督算法一样，SVM也经历了分类任何图像的培训，学习和测试阶段。除了所有参数外，培训采样选择和优化是影响遥感图像的分类准确性的关键部分。我们需要在我们的项目中解决这个问题，以便设计贵族算法或方法，这可能使SVM，更强大的统计学习模型。

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