An objective analysis of Support Vector Machine based classification for remote sensing
An objective analysis of Support Vector Machine based classification for remote sensing
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DOI：
10.1007/s11004-008-9156-6
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发表时间：
2008-05-01
影响因子：
2.6
通讯作者：
Bandopadhyay, Sukumar
中科院分区：
文献类型：
--
作者：
Oommen, Thomas;Misra, Debasmita;Bandopadhyay, Sukumar
准确的专题分类是遥感图像最常见的期望输出之一。最近的研究努力，以提高图像分类的可靠性和准确性，导致了支持向量分类（SVC）计划的介绍。SVC是基于统计学习理论的新一代有监督学习方法，旨在减少模型结构和数据适应度的不确定性。我们提出了一个比较分析的SVC与最大似然分类（MLC）的方法，这是最流行的传统监督分类技术。SVC是一个。最优化技术，其中分类精度严重依赖于识别最佳参数。通过一个案例研究，我们验证了一种方法来获得这些最佳参数，使SVC可以有效地应用。我们使用多光谱和高光谱图像开发已知岩性单元的专题类，以比较这两种方法的分类精度。我们改变了训练与测试数据的比例，以评估两种方法的相对稳健性和最佳训练样本要求，以达到可比的准确度水平。我们的研究结果表明，SVC提高了分类精度，是强大的，并没有遭受的维度问题，如休斯效应。
Accurate thematic classification is one of the most commonly desired outputs from remote sensing images. Recent research efforts to improve the reliability and accuracy of image classification have led to the introduction of the Support Vector Classification (SVC) scheme. SVC is a new generation of supervised learning method based on the principle of statistical learning theory, which is designed to decrease uncertainty in the model structure and the fitness of data. We have presented a comparative analysis of SVC with the Maximum Likelihood Classification (MLC) method, which is the most popular conventional supervised classification technique. SVC is an. optimization technique in which the classification accuracy heavily relies on identifying the optimal parameters. Using a case study, we verify a method to obtain these optimal parameters such that SVC can be applied efficiently. We use multispectral and hyperspectral images to develop thematic classes of known lithologic units in order to compare the classification accuracy of both the methods. We have varied the training to testing data proportions to assess the relative robustness and the optimal training sample requirement of both the methods to achieve comparable levels of accuracy. The results of our study illustrated that SVC improved the classification accuracy, was robust and did not suffer from dimensionality issues such as the Hughes Effect.