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首页> 外文期刊>International journal of applied earth observation and geoinformation >An evaluation of ensemble classifiers for mapping Natura 2000 heathland in Belgium using spaceborne angular hyperspectral (CHRIS/Proba) imagery
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An evaluation of ensemble classifiers for mapping Natura 2000 heathland in Belgium using spaceborne angular hyperspectral (CHRIS/Proba) imagery

机译：使用星载角高光谱（CHRIS / Proba）影像评估系综分类器以绘制比利时Natura 2000荒地的地图

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摘要

Natura 2000 habitats are priority habitats for nature conservation in Europe and need to be monitored closely. In this study, angular hyperspectral CHRIS/Proba imagery was tested for mapping a Natura 2000 heathland site in the north of Belgium. Two ensemble classifiers, Random Forest (RF) and Adaboost, were used and their results compared with Support Vector Machines (SVM). Two classification scenarios were examined: (1) only the nadir images, and (2) both nadir and angular ±36° images. For accuracy assessments, a field dataset was randomly divided into two equal halves, one for training and one for testing. To avoid possible bias, we repeated this random separation of training and testing samples ten times. The mean accuracy and accuracy distribution of each classifier were then analyzed. The averaged result out of ten trials is found to be a better characterization of the classifiers. When only the nadir image was used, SVM outperformed both RF and Adaboost by 3-4%. After angular images were added, both RF and Adaboost achieved comparable accuracy as SVM. In terms of ease-of-use, RF and Adaboost are easier and faster to train than SVM because of less parameter tuning. Incorporating angular images benefitted RF and Adaboost with increases in accuracy by 2-8% and 2-5%, respectively. For SVM, degraded accuracy (1-3%) was seen in five trials. Small sample size with relatively high dimensional input explains the poor performance of SVM. Another advantage of adding angular images is that the final classification maps have a better formation of habitat patches with less salt-and-pepper effects. Among the heathland types, Molinia-encroached heath has an acceptable accuracy (75-80%). While overall accuracies are low because of the spectral similarity of the heathland classes and the limited spectral range of CHRIS (0.4-1 μm), our results point to the potential of hyperspectral sensors with an extended spectral range between 0.4 and 2.5 μm and future hyperspectral missions that are equipped with angular viewing capacity.
机译：Natura 2000栖息地是欧洲自然保护的优先栖息地，需要对其进行密切监控。在这项研究中，对角高光谱CHRIS / Proba影像进行了测试，以绘制比利时北部的Natura 2000荒地地带。使用了两个集成分类器，即随机森林（RF）和Adaboost，并将其结果与支持向量机（SVM）进行了比较。研究了两种分类方案：（1）仅最低点图像，以及（2）最低点和角度±36°图像。为了进行准确性评估，将现场数据集随机分为两个相等的一半，一个用于训练，另一个用于测试。为了避免可能的偏差，我们将训练和测试样本的这种随机分离重复了十次。然后分析每个分类器的平均准确性和准确性分布。发现十次试验的平均结果是对分类器更好的表征。当仅使用最低点图像时，SVM的性能优于RF和Adaboost 3-4％。添加角度图像后，RF和Adaboost都达到了与SVM相当的精度。在易用性方面，由于参数调整较少，因此与SVM相比，RF和Adaboost的训练更加轻松快捷。合并角度图像使RF和Adaboost的准确度分别提高了2-8％和2-5％。对于SVM，在五项试验中发现准确性降低（1-3％）。小样本量和较高维度的输入说明了SVM的性能较差。添加角度图像的另一个优点是最终的分类图可以更好地形成栖息地斑块，而盐和胡椒的影响则更少。在荒地类型中，受到莫利尼亚侵害的荒地具有可接受的精度（75-80％）。尽管由于欧石南地类的光谱相似性以及CHRIS的光谱范围有限（0.4-1μm），总体精度较低，但我们的结果表明，光谱范围在0.4至2.5μm之间的高光谱传感器和未来的高光谱潜力很大具备角度观察能力的任务。

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