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Intensity prediction of tropical cyclone using multilayer multi-block local binary pattern and tree-based classifiers over North Indian Ocean
Computers & Geosciences ( IF 4.2 ) Pub Date : 2021-05-12 , DOI: 10.1016/j.cageo.2021.104798
Chinmoy Kar , Sreeparna Banerjee

Local binary pattern (LBP) is an extensively used method in image analysis and pattern recognition related works across various domains. In this paper, we explore cyclone cloud evolution patterns using a modified LBP. The multilayer multi-block local binary pattern (MMLBP) is an extended version of Completed LBP (CLBP) with multiple blocks and many layers. A tropical cyclone (TC) image is converted into 3 × 3 identical blocks to generate central pixels using CLBP magnitude descriptor block-wise (CLBP_MB). These central pixels are used to find the next layer, which is further divided into 3 × 3 blocks to generate central pixels by CLBP_MB descriptor. This technique will iterate until the dimension of the final layer reached to 1 × 1. These central pixels are extracted from each layer and gather in a vector called a feature vector. The proposed method is applied to 600 tropical cyclone (TC) images and each image generates one feature vector. Hence, 600 vectors are passed through tree-based classifiers to classify cyclone images of various classes. The proposed method of feature extraction and classification reaches a maximum of 84.66% accuracy using Random Forest (RF) classifier. The root mean squared error (RMSE) of the MMLBP method is 9.27 kt, which is better than the LBP and MBLBP approaches. Finally, the size of the original feature vector is reduced to 97.4% using correlation based feature subset selection method.



中文翻译:

北印度洋上多层多层块二元模式和基于树的分类器对热带气旋强度的预测

本地二进制模式(LBP)是跨各个领域的图像分析和模式识别相关工作中广泛使用的方法。在本文中,我们使用改进的LBP探索气旋云的演变模式。多层多块局部二进制模式(MMLBP)是具有多个块和多个层的Completed LBP(CLBP)的扩展版本。将热带气旋(TC)图像转换为3×3相同的块,以使用逐块CLBP大小描述符(CLBP_MB)生成中心像素。这些中心像素用于查找下一层,然后将其进一步分为3×3块,以通过CLBP_MB描述符生成中心像素。这项技术将反复进行,直到最后一层的尺寸达到1×1。从每个层中提取这些中心像素,并将它们聚集在一个称为特征向量的向量中。所提出的方法应用于600个热带气旋(TC)图像,每个图像生成一个特征向量。因此,600个矢量通过基于树的分类器进行分类,以对各种类别的气旋图像进行分类。提出的特征提取和分类方法使用随机森林(RF)分类器可达到84.66%的精度。MMLBP方法的均方根误差(RMSE)为9.27 kt,比LBP和MBLBP方法更好。最后,使用基于相关性的特征子集选择方法将原始特征向量的大小减小到97.4%。提出的特征提取和分类方法使用随机森林(RF)分类器可达到84.66%的精度。MMLBP方法的均方根误差(RMSE)为9.27 kt,比LBP和MBLBP方法更好。最后,使用基于相关性的特征子集选择方法将原始特征向量的大小减小到97.4%。提出的特征提取和分类方法使用随机森林(RF)分类器可达到84.66%的精度。MMLBP方法的均方根误差(RMSE)为9.27 kt,比LBP和MBLBP方法更好。最后,使用基于相关性的特征子集选择方法将原始特征向量的大小减小到97.4%。

更新日期:2021-05-23
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