• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Sensing and imaging >Comparative Analysis of SVM and ANN Classifiers using Multilevel Fusion of Multi‑Sensor Data in Urban Land Classification
【24h】

Comparative Analysis of SVM and ANN Classifiers using Multilevel Fusion of Multi‑Sensor Data in Urban Land Classification

机译：使用多级别融合的城市土地分类多级融合的SVM和ANN分类器的比较分析

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

Multi-sensor data fusion has recently received remarkably more attraction in urban land classification. The fusion of multi-resolution and multi-sensor remote sensing data can help in comprehending more information about the same land cover features, thereby, enhancing the classification accuracy. In this field of study, a combination of hyperspectral data in a long-wave infrared range and a very high-resolution data in a visible range has been extensively used for exploring the spectral and spatial features for decision level fusion classification. This paper proposes a novel method of integrating the classifier decisions with the additional ancillary information derived from spectral and spatial features for improvement in the classification accuracy of natural and man-made objects in urban land cover. The paper also presents a detailed performance comparative evaluation of two classifiers i.e., support vector machine (SVM) and artificial neural network (ANN) to show the effectiveness of these classifiers. The results obtained from a decision-based multilevel fusion of spectral and spatial information using hyperspectral and visible data have shown improvement in classification accuracy. The results also reveal that the classification accuracy of the SVM classifier is better than ANN in multi-sensor data using decision level fusion of combined feature set analysis.
机译：多传感器数据融合最近在城市土地分类中获得了更多的吸引力。多分辨率和多传感器遥感数据的融合可以帮助理解有关相同覆盖特征的更多信息，从而提高分类准确性。在该研究领域中，在可见范围内的长波红外范围内的高光谱数据和非常高分辨率数据的组合已经广泛地广泛地用于探索判定级融合分类的光谱和空间特征。本文提出了一种与源自光谱和空间特征的附加辅助信息集成了分类器决策的新方法，以改善城市陆地覆盖中的自然和人造物体的分类精度。本文还提出了对两个分类器的详细性能比较评估，即支持向量机（SVM）和人工神经网络（ANN）以显示这些分类器的有效性。使用超光谱和可见数据的谱和空间信息的决策多级融合获得的结果表明了分类精度的提高。结果还揭示了SVM分类器的分类精度优于使用组合特征集分析的决策电平融合的多传感器数据中的ANN。

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