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首页> 外文期刊>International journal of remote sensing >A comprehensive evaluation of classification algorithms for coral reef habitat mapping: challenges related to quantity, quality, and impurity of training samples
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A comprehensive evaluation of classification algorithms for coral reef habitat mapping: challenges related to quantity, quality, and impurity of training samples

机译：珊瑚礁栖息地制图分类算法的综合评估：与培训样本的数量，质量和杂质有关的挑战

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摘要

The preparation of control data is a primary concern in many supervised classification schemes. In coral reef mapping, this issue becomes more severe for three reasons: (1) control samples, located beneath the water, are quite difficult and costly to access; (2) because of the high spatial variability of coral reef habitats, it is very difficult to obtain high-quality samples; and (3) pure training samples are also hardly achievable. These issues, namely quantity, quality, and impurity challenges, are the main focus of this study. Three classification algorithms, including Maximum Likelihood Classifier (MLC), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs), are comprehensively evaluated, and their requirements for control data are determined. To accomplish this, rich field data, collected from diving off of Lizard Island in eastern Australia, and Landsat-8 images are used as the input data. With respect to accuracy, ANN is best, as it can deal with the complexity of coral reef environments; however, it requires a higher number of training samples (i.e. ANN cannot manage the quantity challenge). On the other hand, SVM shows the best resistance against the quantity and impurity challenges. Being aware of these points, a coral reef map is produced, for the first time, of the northern Persian Gulf, a coral habitat with very special environmental conditions. In this region, SVM achieved 68.42% overall accuracy, even though a very limited field work campaign was conducted to provide the control data.
机译：在许多监督分类方案中，控制数据的准备是首要考虑的问题。在珊瑚礁制图中，由于以下三个原因，这个问题变得更加严重：（1）位于水下的对照样品非常困难且获取成本高； （2）由于珊瑚礁栖息地的空间变异性很大，因此很难获得高质量的样本； （3）也很难获得纯训练样本。这些问题，即数量，质量和杂质挑战，是本研究的重点。综合评估了三种分类算法，包括最大似然分类器（MLC），人工神经网络（ANN）和支持向量机（SVM），并确定了它们对控制数据的要求。为此，将从澳大利亚东部蜥蜴岛的潜水中采集的丰富的现场数据和Landsat-8图像用作输入数据。就准确性而言，人工神经网络是最好的，因为它可以处理复杂的珊瑚礁环境；但是，它需要更多数量的训练样本（即ANN无法管理数量挑战）。另一方面，SVM显示出对数量和杂质挑战的最佳抵抗力。意识到这些要点后，第一次绘制了北部波斯湾的珊瑚礁地图，该波斯湾具有非常特殊的环境条件。在该区域，即使进行了非常有限的现场工作来提供控制数据，SVM的总体准确性仍达到68.42％。

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  • 来源
    《International journal of remote sensing》 |2017年第14期|4224-4243|共20页
  • 作者

    Chegoonian A. M.; Mokhtarzade M.; Zoej M. J. Valadan;

  • 作者单位

    KN Toosi Univ Technol, Dept Photogrammetry & Remote Sensing, Tehran, Iran;

    KN Toosi Univ Technol, Dept Photogrammetry & Remote Sensing, Tehran, Iran;

    KN Toosi Univ Technol, Dept Photogrammetry & Remote Sensing, Tehran, Iran;

  • 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
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