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An Automated Approach to Agricultural Tile Drain Detection and Extraction Utilizing High Resolution Aerial Imagery and Object-Based Image Analysis.

机译：利用高分辨率航空影像和基于对象的图像分析技术，自动进行农用瓷砖排水检测和提取。

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摘要

Subsurface drainage from agricultural fields in the Maumee River watershed is suspected to adversely impact the water quality and contribute to the formation of harmful algal blooms (HABs) in Lake Erie. In early August of 2014, a HAB developed in the western Lake Erie Basin that resulted in over 400,000 people being unable to drink their tap water due to the presence of a toxin from the bloom. HAB development in Lake Erie is aided by excess nutrients from agricultural fields, which are transported through subsurface tile and enter the watershed. Compounding the issue within the Maumee watershed, the trend within the watershed has been to increase the installation of tile drains in both total extent and density. Due to the immense area of drained fields, there is a need to establish an accurate and effective technique to monitor subsurface farmland tile installations and their associated impacts.;This thesis aimed at developing an automated method in order to identify subsurface tile locations from high resolution aerial imagery by applying an object-based image analysis (OBIA) approach utilizing eCognition. This process was accomplished through a set of algorithms and image filters, which segment and classify image objects by their spectral and geometric characteristics. The algorithms utilized were based on the relative location of image objects and pixels, in order to maximize the robustness and transferability of the final rule-set. These algorithms were coupled with convolution and histogram image filters to generate results for a 10km2 study area located within Clay Township in Ottawa County, Ohio.;The eCognition results were compared to previously collected tile locations from an associated project that applied heads-up digitizing of aerial photography to map field tile. The heads-up digitized locations were used as a baseline for the accuracy assessment. The accuracy assessment generated a range of agreement values from 67.20% - 71.20%, and an average agreement of 69.76%. The confusion matrices calculated a range of kappa values from 0.273 - 0.416 with an overall K value of 0.382, considered fair in strength of agreement. This thesis provides a step forward in the ability to automatically identify and extract tile drains, and will assist future research in subsurface agricultural drainage modeling.
机译：有人认为，莫米河流域的农田地下排水会对水质产生不利影响，并有助于伊利湖中有害藻华的形成。 2014年8月上旬，伊利湖流域西部开发了一种HAB，由于开花中含有毒素，导致超过40万人无法饮用自来水。伊利湖的HAB开发得益于来自农田的过量养分，这些养分通过地下瓷砖运输并进入集水区。使Maumee流域内的问题更加复杂的是，流域内的趋势是增加了瓷砖排水装置的总体安装范围和密度。由于排水田面积巨大，因此需要建立一种准确有效的技术来监测地下农田瓷砖的安装及其相关的影响。本论文旨在开发一种自动方法，以从高分辨率中识别地下瓷砖的位置。通过应用利用电子认知的基于对象的图像分析（OBIA）方法来实现航空图像。该过程是通过一组算法和图像过滤器完成的，这些算法和图像过滤器根据图像对象的光谱和几何特征对其进行分割和分类。所使用的算法基于图像对象和像素的相对位置，以使最终规则集的鲁棒性和可传递性最大化。这些算法与卷积和直方图图像过滤器相结合，以生成位于俄亥俄州渥太华县克莱镇内10平方公里研究区域的结果;将eCognition结果与之前从相关项目中采集的瓦片位置进行了比较，该项目应用了平视数字化技术航拍图来绘制地砖。平视数字化位置用作准确性评估的基准。准确性评估产生的一致性值范围为67.20％-71.20％，平均一致性为69.76％。混淆矩阵计算的kappa值范围为0.273-0.416，总K值为0.382，这被认为在协议强度方面很合理。本论文为自动识别和提取瓷砖排水装置的能力提供了新的进展，并将有助于未来农业地下排水模型的研究。

著录项

  • 作者

    Johansen, Richard A.;

  • 作者单位

    The University of Toledo.;

  • 授予单位 The University of Toledo.;
  • 学科 Geography.;Remote sensing.;Agriculture.
  • 学位 M.A.
  • 年度 2015
  • 页码 78 p.
  • 总页数 78
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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