NDLI: Model Building to Investigate the Role of Spatial Location in Classifying Satellite Image Using SVM, CART and mBACT: A Case Study

Content Provider	Springer Nature Link
Author	Agarwal, Reshu
Copyright Year	2016
Abstract	This paper investigates the importance of spatial location of pixels in terms of row-column as an additional explanatory variable in classification along with available spectral bands of remotely sensed data. In view of this, a forward step-wise variable selection algorithm is used to select significant bands/variables and build an optimal model to extract the maximum accuracy. Author performed a case study on the area of town of Wolfville acquired by LANDSAT 5 TM data containing six 30 m resolution spectral bands and pixel location as an additional variable. Data are classified into seven classes using three advanced classifiers i.e. classification and regression trees (CART), support vector machines (SVM) and multi-class Bayesian additive classification tree (mBACT). Traditionally, it is assumed that addition of more explanatory variables always increase the accuracy of classified satellite images. However, results of this study show that adding more variables may sometimes confuse the classifier, that is, if selected carefully, fewer variables can provide the more accurate classification. Importance of row-column information turns out to be more beneficial for mBACT followed by SVM. Interestingly, spatial locations did not turn out to be useful for CART. Based on the findings of this study, mBACT appears to be a slightly better classifier than SVM and a substantially better than CART.
Starting Page	569
Ending Page	578
Page Count	10
File Format	PDF
ISSN	0255660X
Journal	Journal of the Indian Society of Remote Sensing
Volume Number	45
Issue Number	4
e-ISSN	09743006
Language	English
Publisher	Springer India
Publisher Date	2016-09-15
Publisher Place	New Delhi
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Classification and regression tree Support vector machine LANDSAT data mBACT Earth Sciences Remote Sensing/Photogrammetry
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Geography, Planning and Development

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