...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Optics Communications: A Journal Devoted to the Rapid Publication of Short Contributions in the Field of Optics and Interaction of Light with Matter >Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification
【24h】

Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification

机译：再现核希尔伯特空间中的小波支持向量机用于高光谱遥感图像分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Combining Support Vector Machine (SVM) with wavelet analysis, we constructed wavelet SVM (WSVM) classifier based on wavelet kernel functions in Reproducing Kernel Hilbert Space (RKHS). In conventional kernel theory, SVM is faced with the bottleneck of kernel parameter selection which further results in time-consuming and low classification accuracy. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. Airborne Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing image with 64 bands and Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands were used to experiment the performance and accuracy of the proposed WSVM classifier. The experimental results indicate that the WSVM classifier can obtain the highest accuracy when using the Coiflet Kernel function in wavelet transform. In contrast with some traditional classifiers, including Spectral Angle Mapping (SAM) and Minimum Distance Classification (MDC), and SVM classifier using Radial Basis Function kernel, the proposed wavelet SVM classifier using the wavelet kernel function in Reproducing Kernel Hilbert Space is capable of improving classification accuracy obviously.
机译：将支持向量机（SVM）与小波分析相结合，在再生内核希尔伯特空间（RKHS）中基于小波核函数构造了小波SVM（WSVM）分类器。在传统的内核理论中，SVM面临内核参数选择的瓶颈，这进一步导致了耗时和低分类精度。 RKHS中的小波核是一种多维小波函数，可以近似任意非线性函数。提出了对半参数估计的启示。使用64波段的机载操作模块化成像光谱仪II（OMIS II）高光谱遥感图像和115波段的反射光学系统成像光谱仪（ROSIS）数据来实验所提出的WSVM分类器的性能和准确性。实验结果表明，在小波变换中使用Coiflet核函数时，WSVM分类器可以获得最高的精度。与一些传统的分类器（包括光谱角映射（SAM）和最小距离分类（MDC）以及使用径向基函数核的SVM分类器）相比，所提出的在再生内核希尔伯特空间中使用小波核函数的小波SVM分类器能够提高分类准确度明显。

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