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首页> 外文期刊>Journal of Applied Remote Sensing >Analyzing the impact of red-edge band on land use land cover classification using multispectral RapidEye imagery and machine learning techniques
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Analyzing the impact of red-edge band on land use land cover classification using multispectral RapidEye imagery and machine learning techniques

机译：利用多光谱凝视图像和机器学习技术分析红边频段对土地利用土地覆盖分类的影响

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RapidEye multispectral imager is the first satellite that consists of red-edge (RE) band. This study aims to analyze the effect of incorporating RE band information on land use land cover (LULC) classes. It further investigates the impact of integrating the most common vegetation indices: normalized difference vegetation index (NDVI) and its RE adaption, i.e., NDVI-RE in the classification process and sensitiveness of RE band information on LULC classes. In addition, this study also examines the potential of an advance ensemble technique, i.e., extreme gradient boosting (XGBoost) in comparison to the two state-of-the-art machine learning algorithms, random forest (RF) and support vector machine (SVM). A systematic comparison is performed using machine learning classifiers in case of inclusion and exclusion of RE and inclusion of NDVI versus NDVI-RE. Results show that inclusion of RE band improves accuracy of classification by +2.89%, +3.49%, and +3.03% over without RE using XGBoost, RF, and SVM, respectively. The results obtained using all classifiers confirmed the effectiveness of NDVI-RE over NDVI for LULC classification. However, XGBoost outperformed RF and SVM by achieving highest overall accuracy of 92.41% when NDVI-RE is included as an input feature. For class-specific performance, incorporation of RE shows a significant rise in accuracy of all vegetation classes, whereas the inclusion of NDVI-RE reported a maximum increase in the accuracy of shrubland. Furthermore, results clearly indicated that XGBoost has great potential, and it may be beneficial for more complex classification problems. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)
机译：Rapideye MultiSpectral Imager是第一个由红边（RE）频段组成的卫星。本研究旨在分析在土地利用陆地覆盖（LULC）级别的RE频段信息的效果。它进一步调查了整合最常见的植被指数的影响：归一化差异植被指数（NDVI）及其重新适应，即NDVI-RE在LULC课程中的RE频段信息的分类过程和敏感性中。此外，本研究还研究了预先集成技术的潜力，即与两个最先进的机器学习算法，随机林（RF）和支持向量机（SVM ）。使用机器学习分类器进行系统的比较，在包含和排除RE和包含NDVI与NDVI-RE的情况下。结果表明，在没有使用XGBoost，RF和SVM的情况下，包含RE频带的含量可提高分类的准确性+ 2.89％，+ 3.49％，+ 3.03％。使用所有分类器获得的结果证实了NDVI-RE对LULC分类的NDVI-RE的有效性。然而，当NDVI-RE包含作为输入特征时，XGBoost通过实现最高总精度为92.41％的总精度而表现优于RF和SVM。对于特定的类别性能，RE的掺入显示所有植被类的准确性显着上升，而纳入NDVI-RE的含量最大限度地增加了灌木丛的准确性。此外，结果清楚地表明XGBoost具有很大的潜力，并且对于更复杂的分类问题可能是有益的。 （c）2019年光学仪表工程师协会（SPIE）

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