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首页> 外文期刊>International journal of applied earth observation and geoinformation >A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery
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A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery

机译:使用高分辨率WorldView 2影像比较选定的分类算法以绘制恒河下游平原上的竹片的比较

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Bamboo is used by different communities in India to develop indigenous products, maintain livelihood and sustain life. Indian National Bamboo Mission focuses on evaluation, monitoring and development of bamboo as an important plant resource. Knowledge of spatial distribution of bamboo therefore becomes necessary in this context. The present study attempts to map bamboo patches using very high resolution (VHR) WorldView 2 (WV 2) imagery in parts of South 24 Parganas, West Bengal, India using both pixel and object-based approaches. A combined layer of pan-sharpened multi-spectral (MS) bands, first 3 principal components (PC) of these bands and seven second order texture measures based Gray Level Cooccurrence Matrices (GLCM) of first three PC were used as input variables. For pixel-based image analysis (PBIA), recursive feature elimination (RFE) based feature selection was carried out to identify the most important input variables. Results of the feature selection indicate that the 10 most important variables include PC 1, PC 2 and their GLCM mean along with 6 MS bands. Three different sets of predictor variables (5 and 10 most important variables and all 32 variables) were classified with Support Vector Machine (SVM) and Random Forest (RF) algorithms. Producer accuracy of bamboo was found to be highest when 10 most important variables selected from RFE were classified with SVM (82%). However object-based image analysis (OBIA) achieved higher classification accuracy than PBIA using the same 32 variables, but with less number of training samples. Using object-based SVM classifier, the producer accuracy of bamboo reached 94%. The significance of this study is that the present framework is capable of accurately identifying bamboo patches as well as detecting other tree species in a tropical region with heterogeneous land use land cover (LULC), which could further aid the mandate of National Bamboo Mission and related programs.
机译:印度不同社区使用竹子开发土著产品,维持生计和维持生命。印度国家竹业代表团的重点是评估,监测和开发竹子作为重要植物资源的情况。因此,在这种情况下,有必要了解竹子的空间分布。本研究尝试使用像素和基于对象的方法,在印度西孟加拉邦的南24 Parganas的部分地区,使用高分辨率(VHR)WorldView 2(WV 2)影像绘制竹斑图。将全锐化多光谱(MS)波段,这些波段的前3个主成分(PC)和基于前三个PC的七个基于二阶纹理量度的灰度共生矩阵(GLCM)的组合层用作输入变量。对于基于像素的图像分析(PBIA),进行了基于递归特征消除(RFE)的特征选择,以识别最重要的输入变量。特征选择的结果表明,最重要的10个变量包括PC 1,PC 2及其GLCM平均值以及6个MS波段。使用支持向量机(SVM)和随机森林(RF)算法对三组不同的预测变量(5和10个最重要的变量以及所有32个变量)进行了分类。当使用SVM对从RFE中选择的10个最重要变量进行分类时,发现竹子的生产者准确性最高(82%)。但是,基于对象的图像分析(OBIA)在使用相同的32个变量的情况下比PBIA获得了更高的分类精度,但是训练样本的数量却更少。使用基于对象的SVM分类器,竹子的生产者准确性达到94%。这项研究的意义在于,该框架能够准确识别竹片并检测具有异质土地利用土地覆盖(LULC)的热带地区的其他树种,这可以进一步帮助执行国家竹节任务和相关任务程式。

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