...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of remote sensing >Influence of image fusion approaches on classification accuracy: a case study
【24h】

Influence of image fusion approaches on classification accuracy: a case study

机译：图像融合方法对分类准确性的影响：一个案例研究

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

While many studies in the field of image fusion of remotely sensed data aim towards deriving new algorithms for visual enhancement, there is little research on the influence of image fusion on other applications. One major application in earth science is land cover mapping. The concept of sensors with multiple spatial resolutions provides a potential for image fusion. It minimises errors of geometric alignment and atmospheric or temporal changes. This study focuses on the influence of image fusion on spectral classification algorithms and their accuracy. A Landsat 7 ETM + image was used, where six multispectral bands (30m) were fused with the corresponding 15 m panchromatic channel. The fusion methods comprise rather common techniques like Brovey, hue-saturation-value transform, and principal component analysis, and more complex approaches, including adaptive image fusion, multisensor multiresolu-tion image fusion technique, and wavelet transformation. Image classification was performed with supervised methods, e.g. maximum likelihood classifier, object-based classification, and support vector machines. The classification was assessed with test samples, a clump analysis, and techniques accounting for classification errors along land cover boundaries. It was found that the adaptive image fusion approach shows best results with low noise content. It resulted in a major improvement when compared with the reference, especially along object edges. Acceptable results were achieved by wavelet, multisensor multiresolution image fusion, and principal component analysis. Brovey and hue-saturation-value image fusion performed poorly and cannot be recommended for classification of fused imagery.
机译：虽然在遥感数据的图像融合领域中的许多研究旨在推导用于视觉增强的新算法，但很少有关于图像融合对其他应用的影响的研究。地球科学的一项主要应用是土地覆盖制图。具有多种空间分辨率的传感器的概念为图像融合提供了潜力。它将几何对准和大气或时间变化的误差降到最低。这项研究的重点是图像融合对光谱分类算法及其准确性的影响。使用Landsat 7 ETM +图像，其中六个多光谱带（30m）与相应的15 m全色通道融合在一起。融合方法包括相当普遍的技术，例如Br​​ovey，色相饱和度值变换和主成分分析，以及更复杂的方法，包括自适应图像融合，多传感器多分辨率图像融合技术和小波变换。图像分类采用监督方法进行，例如最大似然分类器，基于对象的分类和支持向量机。使用测试样本，团块分析和考虑沿土地覆被边界的分类误差的技术对分类进行评估。已经发现，自适应图像融合方法显示出具有低噪声含量的最佳结果。与参考相比，尤其是沿对象边缘，它带来了重大改进。通过小波，多传感器多分辨率图像融合和主成分分析获得了可接受的结果。 Brovey和色相饱和度值图像融合效果不佳，因此不建议用于融合图像的分类。

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