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Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images

机译：使用RF算法对Worldview-2和Landsat 8影像进行沿海湿地土地覆盖分类

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Wetlands are one of the world’s most important ecosystems, playing an important role in regulating climate and protecting the environment. However, human activities have changed the land cover of wetlands, leading to direct destruction of the environment. If wetlands are to be protected, their land cover must be classified and changes to it monitored using remote sensing technology. The random forest (RF) machine learning algorithm, which offers clear advantages (e.g., processing feature data without feature selection and preferable classification result) for high spatial image classification, has been used in many study areas. In this research, to verify the effectiveness of this algorithm for remote sensing image classification of coastal wetlands, two types of spatial resolution images of the Linhong Estuary wetland in Lianyungang—Worldview-2 and Landsat-8 images—were used for land cover classification using the RF method. To demonstrate the preferable classification accuracy of the RF algorithm, the support vector machine (SVM) and k -nearest neighbor (k-NN) methods were also used to classify the same area of land cover for comparison with the results of RF classification. The study results showed that (1) the overall accuracy of the RF method reached 91.86%, higher than the SVM and k-NN methods by 4.68% and 4.72%, respectively, for Worldview-2 images; (2) at the same time, the classification accuracies of RF, SVM, and k-NN were 86.61%, 79.96%, and 77.23%, respectively, for Landsat-8 images; (3) for some land cover types having only a small number of samples, the RF algorithm also achieved better classification results using Worldview-2 and Landsat-8 images, and (4) the addition texture features could improve the classification accuracy of the RF method when using Worldview-2 images. Research indicated that high-resolution remote sensing images are more suitable for small-scale land cover classification image and that the RF algorithm can provide better classification accuracy and is more suitable for coastal wetland classification than the SVM and k-NN algorithms are.
机译：湿地是世界上最重要的生态系统之一，在调节气候和保护环境方面发挥着重要作用。然而，人类活动改变了湿地的土地覆盖，导致环境的直接破坏。如果要保护湿地，则必须对其湿地进行分类，并使用遥感技术对其变化进行监控。随机森林（RF）机器学习算法为高空间图像分类提供了明显的优势（例如，在没有特征选择的情况下处理特征数据和更好的分类结果），已在许多研究领域中使用。在这项研究中，为了验证该算法对沿海湿地遥感图像分类的有效性，将连云港临洪河口湿地的两种空间分辨率图像（Worldview-2和Landsat-8图像）用于土地覆盖分类。射频方法。为了证明RF算法具有更好的分类精度，还使用了支持向量机（SVM）和k近邻（k-NN）方法对同一土地面积进行分类，以与RF分类结果进行比较。研究结果表明：（1）对于Worldview-2图像，RF方法的总体准确度达到91.86％，比SVM和k-NN方法分别高4.68％和4.72％。 （2）同时，Landsat-8图像的RF，SVM和k-NN的分类准确性分别为86.61％，79.96％和77.23％; （3）对于一些样本数量较少的土地覆盖类型，RF算法还使用Worldview-2和Landsat-8图像获得了更好的分类结果，并且（4）附加的纹理特征可以提高RF的分类精度。使用Worldview-2图像时的方法。研究表明，高分辨率遥感图像更适合于小规模的土地覆盖分类图像，与SVM和k-NN算法相比，RF算法可以提供更好的分类精度，并且更适合于沿海湿地分类。

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