• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Indian Journal of Science and Technology >Performance Analysis of GA and PSO based Feature Selection Techniques for Improving Classification Accuracy in Remote Sensing Images
【24h】

Performance Analysis of GA and PSO based Feature Selection Techniques for Improving Classification Accuracy in Remote Sensing Images

机译：基于GA和PSO的特征选择技术提高遥感影像分类精度的性能分析。

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Background/Objectives: - Feature Selection is applicable to decrease the number of features in various applications wherein the data include hundreds and thousands of features. The objective of this study is to choose Genetic Algorithm for feature selection to obtain better fitness function. Methods/Statistical Analysis: Particle Swarm Optimization (PSO) approach is used for selecting the subset from the combination of texture based features and providing the better fitness values. In this paper PSO is used to obtain the feature sets and the performance is compared with genetic algorithm. Support vector machine classifier is used to improve the classification accuracy. Findings: The experimental results shows that PSO overall accuracy is improved to LISS IV 1.7%, 1.4% and 2.9% and the kappa coefficient is improved to 0.06%, 0.012% and 0.39% as compared to GA. Application/Improvements: The Fitness value obtained by GA is more complex and not accurate. To reduce the complexity and increase the accuracy Particle Swarm optimization is used. Hence PSO improved the quality of texture based images.
机译：背景/目的：-特征选择适用于减少其中数据包括成百上千个特征的各种应用中的特征数量。本研究的目的是选择遗传算法进行特征选择，以获得更好的适应度函数。方法/统计分析：粒子群优化（PSO）方法用于从基于纹理的特征组合中选择子集，并提供更好的适应性值。本文将PSO用于获得特征集，并将其性能与遗传算法进行比较。支持向量机分类器用于提高分类精度。结果：实验结果表明，与GA相比，PSO的整体准确度提高了LISS IV 1.7％，1.4％和2.9％，kappa系数提高了0.06％，0.012％和0.39％。应用/改进：GA获得的适应度值更复杂且不准确。为了降低复杂度并提高准确性，使用了粒子群算法。因此，PSO改善了基于纹理的图像的质量。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号