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首页> 外文期刊>Arabian journal of geosciences >Evaluating the impact of classification algorithms and spatial resolution on the accuracy of land cover mapping in a mountain environment in Pakistan
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Evaluating the impact of classification algorithms and spatial resolution on the accuracy of land cover mapping in a mountain environment in Pakistan

机译：评估分类算法和空间分辨率对巴基斯坦山区环境中陆地覆盖映射精度的影响

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摘要

Satellite images of various spatial resolutions and different image classification techniques have been utilized for land cover (LC) mapping at local and regional scale studies. Mapping capabilities and achievable accuracies of LC classification in a mountain environment are, however, influenced by the spatial resolution of the utilized images and applied classification techniques. Hence, developing and characterizing regionally optimized methods are essential for the planning and monitoring of natural resources. In this study, the potential of four non-parametric image classification techniques, i.e., k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and neural network (NN) on the accuracy of LC classification was evaluated in the Hindu Kush mountains ranges of northern Pakistan. Moreover, we have assessed the impact of the spatial resolution of the utilized satellite imagery, i.e., SPOT-5 with 2.5 m and Landsat-8 with 30 m on the accuracy of the derived LC classification. For the classification of LC based on SPOT-5, we have achieved the highest overall classification accuracy (OCA) = 89% with kappa coefficient (KC = 0.86) using SVM followed by k-NN, RF, and NN. However, for LC classification derived from Landsat-8 imagery, we achieved the highest OCA = 71% with KC = 0.59 using RF and SVM followed by k-NN and NN. The higher accuracy derived from SPOT-5 versus Landsat-8 indicated that the results of LC classification based on SPOT-5 are more accurate and reliable than Landsat-8. The findings of the present study will be useful for the classification and mapping task of LC in a mountain environment using SPOT-5 and Landsat-8 at local and regional scale studies.
机译：已经利用了各种空间分辨率和不同图像分类技术的卫星图像，用于当地和区域规模研究的陆地覆盖（LC）测绘。然而，山地环境中LC分类的映射能力和可实现的精度是受利用图像和应用分类技术的空间分辨率的影响。因此，开发和表征区域优化的方法对于对自然资源的规划和监测至关重要。在本研究中，四个非参数图像分类技术的潜力，即K-CORMALE邻居（K-NN），支持向量机（SVM），随机林（RF）和神经网络（NN）的精度在巴基斯坦北部的印度教诗山范围内评估了LC分类。此外，我们已经评估了利用卫星图像的空间分辨率的影响，即4.5米和Landsat-8，衍生LC分类的准确性为30米的Spot-5。对于基于SPOP-5的LC的分类，我们使用SVM实现了KAPPA系数（KC = 0.86）的最高总体分类精度（OCA）= 89％，然后是K-NN，RF和NN。然而，对于来自Landsat-8图像的LC分类，我们使用RF和SVM与KC = 0.59进行了最高的OCA = 71％，然后用K-Nn和Nn。来自Spot-5与Landsat-8的更高的精度表明，基于Spot-5的LC分类结果比Landsat-8更准确可靠。目前研究的结果将在山环境中使用Spot-5和Landsat-8在局部和区域规模研究中对LC的分类和映射任务有用。

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