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首页> 外文期刊>International journal of remote sensing >Combining EO-1 Hyperion and Envisat ASAR data for mangrove species classification in Mai Po Ramsar Site, Hong Kong
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Combining EO-1 Hyperion and Envisat ASAR data for mangrove species classification in Mai Po Ramsar Site, Hong Kong

机译：结合EO-1 Hyperion和Envisat ASAR数据对香港米埔拉姆萨尔遗址的红树林物种进行分类

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摘要

Mangrove habitat is one of the most highly productive ecosystems. The distribution of mangrove species acts as an inventory to formulate conservation management plans. This study explored the potential of combining hyperspectral (Earth-observing (EO)-1 Hyperion) and multi-temporal synthetic aperture radar (SAR) (Environmental Satellite (Envisat) ASAR) data, supported by in situ field surveys, to map mangrove species. Hyperspectral imaging captures a number of narrow contiguous spectral bands providing richer spectral details than those obtained from traditional broadband sensors. All-weather radar sensing allows continuous data acquisition and its signal penetrability can reveal canopy structural characteristics, which offer an additional data dimension that is not available in optical sensing. Through combining the two data types, this study achieved three objectives. First, facing the issue of dimensionality and limited field samples, feature selection techniques from computer science were adopted to select spectral and radar features that are crucial for mangrove species discrimination. Second, classification accuracy using various combinations of spectral and radar features was evaluated. Third, classification algorithms including maximum likelihood (ML), decision tree (DT), artificial neural network (ANN), and support vector machine (SVM) were used to estimate species distribution, and classification accuracy was compared. Results suggested that feature selection techniques are capable of identifying salient features in spectral and radar space that can effectively discriminate between mangrove species. Combining optical and radar data can improve classification accuracy. Among the classifiers, ANN produces more accurate and robust estimation.
机译：红树林栖息地是生产力最高的生态系统之一。红树林物种的分布是制定保护管理计划的清单。这项研究探索了结合高光谱（对地观测（EO）-1 Hyperion）和多时相合成孔径雷达（SAR）（环境卫星（Envisat）ASAR）数据的潜力，并进行了实地调查，以绘制红树林物种图。高光谱成像捕获了许多窄的连续光谱带，这些光谱带比从传统宽带传感器获得的光谱细节更丰富。全天候雷达感测允许连续数据采集，并且其信号穿透性可以揭示冠层结构特征，这提供了光学感测中无法提供的附加数据维度。通过结合两种数据类型，本研究实现了三个目标。首先，面对维度和田间样本有限的问题，采用了来自计算机科学的特征选择技术来选择对于红树林物种歧视至关重要的光谱和雷达特征。第二，评估使用光谱和雷达特征的各种组合进行分类的准确性。第三，使用分类算法包括最大似然（ML），决策树（DT），人工神经网络（ANN）和支持向量机（SVM）来估计物种分布，并比较分类准确性。结果表明，特征选择技术能够识别光谱和雷达空间中的显着特征，从而可以有效地区分红树林物种。结合光学和雷达数据可以提高分类精度。在分类器中，ANN产生更准确，更可靠的估计。

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  • 来源
    《International journal of remote sensing》 |2014年第24期|7828-7856|共29页
  • 作者

    Wong Frankie K. K.; Fung Tung;

  • 作者单位

    Chinese Univ Hong Kong, Dept Geog & Resource Management, Fac Social Sci, Shatin, Hong Kong, Peoples R China;

    Chinese Univ Hong Kong, Dept Geog & Resource Management, Fac Social Sci, Shatin, Hong Kong, 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
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