• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >A per-segment approach to improving aspen mapping from remote sensing imagery and its implications at different scales.
【24h】

A per-segment approach to improving aspen mapping from remote sensing imagery and its implications at different scales.

机译：一种基于细分的方法，可改善遥感影像的白杨制图及其在不同规模上的含义。

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

A per-segment classification system was developed to map aspen (Populus tremuloides) stands on Winter Ridge in central Oregon from remote sensing imagery. A 1-meter color infrared (CIR) image was segmented based on its hue and saturation values to generate aspen "candidates", which were then classified to show aspen coverage according to the mean values of spectral reflectance and multi-resolution texture within the segments. For a three-category mapping, an 88 percent overall accuracy with a K-hat statistic of 0.82 was achieved, while for a two-category mapping, a 90 percent overall accuracy with a K-hat statistic of 0.78 was obtained.; In order to compare these results to traditional per-pixel classifications, an unsupervised classification procedure based on the ISODATA algorithm was applied to both pixel-based and segment-based seven-layer images. While differences among various per-pixel classifications were found to be insignificant, the results from the per-segment system were consistently more than 20 percent better than those from per-pixel classifications.; Both the per-segment and per-pixel classifications were applied at various spatial resolutions in order to study the effect of spatial resolution on the relative performance of the two methods. The per-segment classifier outperformed the per-pixel classifier at the 1--4-m resolution, performed equally well at the 8--16-m resolution and showed no ability to classify accurately at the 32-m resolution due to the segmentation process used. Overall, the per-segment method was found to be more scale-sensitive than the per-pixel method and required some tuning to the segmentation algorithm at lower resolutions. These results illustrate the advantages of per-segment methods at high spatial resolutions but also suggest that segmentation algorithms should be applied carefully at different spatial resolutions.
机译：开发了一种基于细分的分类系统，以通过遥感影像绘制俄勒冈州中部温特里奇（Winter Ridge）上的白杨（Populus tremuloides）立场。根据其色相和饱和度值对1米长的彩色红外（CIR）图像进行分割，以生成白杨“候选对象”，然后根据这些部分中光谱反射率和多分辨率纹理的平均值对它们进行分类以显示白杨覆盖率。对于三类映射，K-hat统计量为0.82，总体准确度为88％；对于两类映射，K-hat统计量为0.78，总体准确度为90％。为了将这些结果与传统的按像素分类进行比较，将基于ISODATA算法的无监督分类程序应用于基于像素和基于段的七层图像。虽然发现各个像素分类之间的差异不明显，但基于每个细分系统的结果始终比基于像素分类的结果好20％以上。为了研究空间分辨率对这两种方法的相对性能的影响，按细分和按像素分类都适用于各种空间分辨率。按细分分类器在1--4-m分辨率下优于按像素分类器，在8--16-m分辨率下表现同样出色，并且由于分割而无法在32-m分辨率下准确分类使用的过程。总体而言，发现每段方法比每像素方法对比例更敏感，并且需要以较低的分辨率对分割算法进行一些调整。这些结果说明了在高空间分辨率下逐段方法的优势，但也建议应该在不同的空间分辨率下谨慎应用分割算法。

著录项

  • 作者

    Heyman, Ofer.;

  • 作者单位

    Oregon State University.;

  • 授予单位 Oregon State University.;
  • 学科 Environmental Sciences.; Remote Sensing.; Agriculture Forestry and Wildlife.
  • 学位 Ph.D.
  • 年度 2004
  • 页码 93 p.
  • 总页数 93
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 环境科学基础理论;遥感技术;森林生物学;
  • 关键词

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