...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Assessment of ALOS PALSAR 50 m Orthorectified FBD Data for Regional Land Cover Classification by Support Vector Machines
【24h】

Assessment of ALOS PALSAR 50 m Orthorectified FBD Data for Regional Land Cover Classification by Support Vector Machines

机译：支持向量机评估ALOS PALSAR 50 m正射FBD数据用于区域土地覆盖分类

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

From its launch in 2006, the phased array L-band synthetic aperture radar (PALSAR) onboard the advanced land observing satellite (ALOS) has acquired many dual-polarized (FBD) images with a 70-km swath width, aiming to produce spatially consistent coverage over tropical rainforest. This paper investigates the relevancy of PALSAR orthorectified FBD product at 50-m resolution for regional land cover classification by the support vector machines (SVM). Our test site is the Riau province, Sumatra island, Indonesia, known to hold vast area of natural peatland forest with an extreme biodiversity threatened by industrial deforestation. Since it is demonstrated the radiometric information (HH and HV channels) cannot be solely used to achieve a good classification, the spatial information in these orthorectified data is investigated. A new tool using the recursive feature elimination SVM-based process and the textural Haralick's parameters is introduced. The real contribution of textures within the land cover classification can be understood. A small set of textural parameters is determined at local scale while being optimal for the land cover discrimination. The SVM-based classifier is carried out across the whole Riau province and its results are compared with a Landsat-based estimation. The agreement is over 70% with six classes and 86% for the natural forest map. These results are remarkable since only one PALSAR FBD product is used and this assessment is performed on more than 40 million pixels. The results confirm the high potential of the PALSAR sensor for forest monitoring at regional, if not global scale.
机译：自2006年发射以来，先进的陆地观测卫星（ALOS）上的相控阵L波段合成孔径雷达（PALSAR）已获取许多幅宽70公里的双极化（FBD）图像，旨在产生空间一致的覆盖热带雨林。本文研究了支持向量机（SVM）在50 m分辨率下对PALSAR正射FBD产品进行区域土地覆盖分类的相关性。我们的测试地点是印度尼西亚苏门答腊岛的廖内省，那里拥有广阔的天然泥炭地森林，其生物多样性受到工业森林砍伐的威胁。由于已证明不能完全使用辐射信息（HH和HV通道）来实现良好的分类，因此对这些经过正射校正的数据中的空间信息进行了研究。介绍了一种使用基于SVM的递归特征消除方法和纹理Haralick参数的新工具。可以理解土地覆被分类中纹理的真正作用。在局部范围内确定一小部分纹理参数，同时对于土地覆盖判别是最佳的。在整个廖内省进行了基于SVM的分类器，并将其结果与基于Landsat的估计进行了比较。该协议超过了70％的等级，分为6个等级，而86％的等级为天然林地图。由于仅使用一种PALSAR FBD产品，并且对超过4000万个像素进行了此评估，因此这些结果非常出色。这些结果证实了PALSAR传感器在区域范围（即使不是全球范围）进行森林监测的巨大潜力。

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