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Comparison of NAIP orthophotography and RapidEye satellite imagery for mapping of mining and mine reclamation

机译：NAIP正射影像和RapidEye卫星影像在采矿和矿山复垦方面的比较

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摘要

National Agriculture Imagery Program (NAIP) orthophotography is a potentially useful data source for land cover classification in the United States due to its nationwide and generally cloud-free coverage, low cost to the public, frequent update interval, and high spatial resolution. Nevertheless, there are challenges when working with NAIP imagery, especially regarding varying viewing geometry, radiometric normalization, and calibration. In this article, we compare NAIP orthophotography and RapidEye satellite imagery for high-resolution mapping of mining and mine reclamation within a mountaintop coal surface mine in the southern coalfields of West Virginia, USA. Two classification algorithms, support vector machines and random forests, were used to classify both data sets. Compared to the RapidEye classification, the NAIP classification resulted in lower overall accuracy and kappa and higher allocation disagreement and quantity disagreement. However, the accuracy of the NAIP classification was improved by reducing the number of classes mapped, using the near-infrared band, using textural measures and feature selection, and reducing the spatial resolution slightly by pixel aggregation or by applying a Gaussian low-pass filter. With such strategies, NAIP data can be a potential alternative to RapidEye satellite data for classification of surface mine land cover.
机译：国家农业影像计划（NAIP）正射摄影技术在美国范围内且总体上无云，公众成本低，更新间隔频繁且空间分辨率高，因此在美国进行土地覆被分类方面可能是有用的数据源。尽管如此，在使用NAIP图像时仍存在挑战，尤其是在改变观察几何形状，辐射归一化和校准方面。在本文中，我们将NAIP正射影像和RapidEye卫星图像进行比较，以对美国西维吉尼亚州南部煤田的山顶煤矿露天矿中的采矿和矿山回收进行高分辨率地图绘制。支持向量机和随机森林这两种分类算法用于对两个数据集进行分类。与RapidEye分类相比，NAIP分类导致较低的总体准确度和kappa，以及较高的分配异议和数量异议。但是，通过减少映射的类别数量，使用近红外波段，使用纹理度量和特征选择以及通过像素聚合或通过应用高斯低通滤波器来稍微降低空间分辨率，可以提高NAIP分类的准确性。 。通过这种策略，NAIP数据可以成为RapidEye卫星数据的潜在替代品，用于对露天矿土地覆盖进行分类。

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