Paper Info
Title
AdaBoosted Extra Trees Classifier for Object-Based Multispectral Image Classification of Urban Fringe Area
Abstract
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.
Year
DOI
Venue
2022
10.1142/S0219467821400064
INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS
Keywords
DocType
Volume
Extra trees classifier, image classification, object-based classification, random forest
Journal
22
Issue
ISSN
Citations 
03
0219-4678
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Alpesh M. Patel100.34
Anil Suthar200.34