...
  • 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 >Exponentially sampling scale parameters for the efficient segmentation of remote-sensing images
【24h】

Exponentially sampling scale parameters for the efficient segmentation of remote-sensing images

机译：指数采样比例参数可有效分割遥感图像

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

Scale parameter(s) of multi-scale hierarchical segmentation (MSHS), which groups pixels as objects in different size and hierarchically organizes them in multiple levels, such as the multiresolution segmentation (MRS) embedded into the eCognition software, directly determines the average size of segmented objects and has significant influences on following geographic object-based image analysis. Recently, some studies have provided solutions to search the optimal scale parameter(s) by supervised strategies (with reference data) or unsupervised strategies (without reference data). They focused on designing metrics indicating better scale parameter(s) but neglected the influences of the linear sampling method of the scale parameter they used as default. Indeed, the linear sampling method not only requires a proper increment and a proper range to balance the accuracy and the efficiency by supervised strategies, but also performs badly in the selection of multiple key scales for the MSHS of complex landscapes by unsupervised strategies. Against these drawbacks, we propose an exponential sampling method. It was based on our finding that the logarithm of the segment count and the logarithm of the scale parameter are linearly dependent, which had been extensively validated on different landscapes in this study. The scale parameters sampled by the exponential sampling method and the linear sampling method with increments 2, 5, 10, 25, and 100 that most former studies used were evaluated and compared by two supervised strategies and an unsupervised strategy. Results indicated that, when searching by the supervised strategies, the exponential sampling method achieved both high accuracy and efficiency where the linear sampling method had to balance them through the experiences of an expert; and when searching by the unsupervised strategy, multiple key scale parameters in MSHS of complex landscapes could be identified among the exponential sampling results, while the linear sampling results hardly achieved this. Considering these two merits, we recommend the exponential sampling method to replace the linear sampling method when searching the optimal scale parameter(s) of MRS.
机译：多尺度分层分割（MSHS）的尺度参数将像素划分为不同大小的对象，然后将其分层组织为多个级别，例如嵌入到eCognition软件中的多分辨率分段（MRS），直接确定平均大小分割的对象，对后续基于地理对象的图像分析有重大影响。最近，一些研究提供了通过监督策略（带有参考数据）或非监督策略（没有参考数据）来搜索最佳比例参数的解决方案。他们专注于设计指标，以指示更好的比例参数，但忽略了线性采样方法对默认参数的影响。确实，线性抽样方法不仅需要适当的增量和适当的范围来通过监督策略来平衡准确性和效率，而且在通过非监督策略选择复杂景观MSHS的多个关键尺度时表现不佳。针对这些缺点，我们提出了一种指数采样方法。基于我们的发现，分段计数的对数和比例尺参数的对数是线性相关的，这在本研究中已在不同景观上得到了广泛验证。通过两种监督策略和一种无监督策略，评估并比较了大多数以前的研究采用指数抽样方法和线性抽样方法以增量2、5、10、25和100抽样的比例参数。结果表明，当采用监督策略进行搜索时，指数抽样方法既要达到高准确性又要达到效率，而线性抽样方法必须通过专家的经验来平衡它们。当采用无监督策略搜索时，可以在指数采样结果中识别出复杂景观MSHS中的多个关键尺度参数，而线性采样结果很难做到这一点。考虑到这两个优点，在搜索MRS的最佳比例参数时，我们建议使用指数采样法代替线性采样法。

著录项

  • 来源
    《International journal of remote sensing》 |2018年第6期|1628-1654|共27页
  • 作者

    Wang Zhihua; Lu Chen; Yang Xiaomei;

  • 作者单位

    Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China;

    Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China;

    Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 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