利用NAIP图像进行精确的大面积土地利用/土地覆盖制图:德克萨斯州中部的案例研究

M. Subedi, Carlos Portillo-Quintero, S. Kahl, N. McIntyre, R. Cox, Gad Perry
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引用次数: 0

摘要

由于场景之间的辐射差异、图像的低光谱深度、景观异质性和计算限制,使用高分辨率图像进行大面积土地利用土地覆盖(LULC)制图仍然具有挑战性。使用随机森林(RF)监督机器学习算法,我们提出了一种基于地理对象的图像分析方法,将220个国家农业图像计划的大型马赛克图像分类为lulc类别。该方法在美国德克萨斯州中部应用,覆盖面积超过6000平方公里。我们为每个对象生成了36个变量,并考虑了样本数据的空间结构,以确定样本在空间上独立的距离。最终的rf模型在独立分层随机样本上的准确率为94.8%。此外,植被和水体指数、主成分均值和标准差以及纹理特征提高了分类精度。本研究展示了一种利用高空间/低光谱分辨率正影像制作精确的多类别土地利用/土地覆盖地图的经济有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging NAIP Imagery for Accurate Large-Area Land Use/land Cover Mapping: A Case Study in Central Texas
Large-area land use land cover (LULC) mapping using high-resolution imagery remains challenging due to radiometric differences between scenes, the low spectral depth of the imagery, landscape heterogeneity, and computational limitations. Using a random forest (RF)- supervised machine-learning algorithm, we present a geographic object-based image analysis approach to classifying a large mosaic of 220 National Agriculture Imagery Program orthoimagery into lulc categories. The approach was applied in central Texas, USA, covering over 6000 km2. We generated 36 variables for each object and accounted for spatial structures of sample data to determine the distance at which samples were spatially independent. The final rf model produced 94.8% accuracy on independent stratified random samples. In addition, vegetation and water indices, the mean and standard deviation of principal components, and texture features improved classification accuracy. This study demonstrates a cost-effective way of producing an accurate multi-class land use/land cover map using high-spatial/low-spectral resolution orthoimagery.
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