AdaBoosted Extra Trees Classifier for Object-Based Multispectral Image Classification of Urban Fringe Area

作     者：Patel, Alpesh M. Suthar, Anil 

作者机构：Vishwakarma Govt Engn Coll Dept Elect & Commun Chandkheda India Gujarat Technol Univ Ahmadabad 382424 Gujarat India LJ Inst Engn & Technol Ahmadabad Gujarat India 

出 版 物：《INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS》 (国际影像与图形学杂志)

年 卷 期：2022年第22卷第3期

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors would like to thank the Space Applications Centre of Indian Space Research Organisation  Ahmedabad  India for providing the LISS IV Image. The author would also like to thank all the anonymous reviewers for their valuable suggestions and comments 

主　　题：Extra trees classifier image classification object-based classification random forest 

摘      要：In the past decade, it is proven that satellite image classification using an object-based technique is better than the standard pixel-based technique. With the increasing need for classifying multispectral satellite images for urban planning, the accuracy of the classification becomes a significant performance parameter. Object-based classification (OBC) is a technique in which group of pixels having similar spectral properties, called objects, are generated using image segmentation and then these objects are classified based on their attributes. In this paper, the combination of a multiclass AdaBoost algorithm with extra trees classifier (ETC) is proposed with higher prediction accuracy for the OBC of the urban fringe area. The performance of the AdaBoost algorithm is found to be better in terms of classification accuracy than benchmarked SVM and RF classifiers for OBC. These classification methods were applied to IRS-R2 LISS IV data. The AdaBoosted extra trees classifier (ABETC) has demonstrated the highest accuracy with overall accuracy (OA) of 88.47% and a kappa coefficient of 0.85. The computational time of the ABETC is found to be much smaller than the RF algorithm. In detail, the sensitivity of the classifiers was investigated using stratified random sampling with various sample sizes.