Accuracy analysis of various classification algorithms for used land

N. Suresh Kumar; M. Arun

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Accuracy analysis of various classification algorithms for used land

机译：土地利用分类算法的精度分析

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摘要

This research work describes the region-based approach for satellite image classification to extract land use details in the Vellore District of Tamil Nadu, India. The objective is to find the greenery and used lands of the study area by classifying the satellite imagery using fuzzy-based, K-nearest neighbourhood, support vector machine classification methods and spectral information of a LANDSAT satellite image. The LANDS AT image is applied along with the image processing algorithms to get classified image. These algorithms are implemented and the objects are identified. These identified objects and ground truth values of study area are compared. By comparing producer's accuracy, user's accuracy, omission error and commission error, the overall accuracy is calculated and the algorithm which gives the better performance is identified for LANDSAT image of the study area, Vellore District, Tamil Nadu, India.

机译：这项研究工作描述了基于区域的卫星图像分类方法，以提取印度泰米尔纳德邦韦洛尔区的土地利用细节。目的是通过使用基于模糊，K近邻，支持向量机分类方法和LANDSAT卫星图像的光谱信息对卫星图像进行分类，找到研究区域的绿地和已用土地。将LANDS AT图像与图像处理算法一起应用以获得分类图像。实现这些算法并确定对象。比较这些确定的对象和研究区域的地面真实值。通过比较生产者的准确性，用户的准确性，遗漏误差和佣金误差，可以计算出总体准确性，并为印度泰米尔纳德邦韦洛尔区的研究区域的LANDSAT图像确定了性能更好的算法。

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来源
《International journal of enterprise network management》 |2016年第2期|113-132|共20页
作者
N. Suresh Kumar; M. Arun;
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作者单位

School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India;

School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India;

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原文格式 PDF
正文语种 eng
中图分类
关键词
fuzzy; K-nearest neighbour; K-NN; support vector machine; SVM; classification; India;

机译：模糊;K近邻;神经网络支持向量机支持向量机;分类;印度;

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Accuracy analysis of various classification algorithms for used land

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