Testing Multivariate Adaptive Regression Splines (MARS) as a Method of Land Cover Classification of TERRA-ASTER Satellite Images

Elia Quirós,Ángel M Felicísimo,Aurora Cuartero

doi:10.3390/s91109011

Abstract

This work proposes a new method to classify multi-spectral satellite images based on multivariate adaptive regression splines (MARS) and compares this classification system with the more common parallelepiped and maximum likelihood (ML) methods. We apply the classification methods to the land cover classification of a test zone located in southwestern Spain. The basis of the MARS method and its associated procedures are explained in detail, and the area under the ROC curve (AUC) is compared for the three methods. The results show that the MARS method provides better results than the parallelepiped method in all cases, and it provides better results than the maximum likelihood method in 13 cases out of 17. These results demonstrate that the MARS method can be used in isolation or in combination with other methods to improve the accuracy of soil cover classification. The improvement is statistically significant according to the Wilcoxon signed rank test.

Highlights

Conventional classification methods used in remote sensing have some basic problems due to the fact that they are not adapted to the real characteristics of image data
The number of knots and their placement are fixed for regression splines, and in the multivariate adaptive regression splines (MARS) procedure, knots are determined by a search that occurs both forwards and backwards in a stepwise fashion
It is necessary to perform a separate accuracy study, and as mentioned before, receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) statistics were calculated for this purpose

Summary

Introduction

Conventional classification methods used in remote sensing have some basic problems due to the fact that they are not adapted to the real characteristics of image data. They lack proper configurations, and there is generally minimal user interaction. The first family is parametric, and includes the ML, bayesian methods, etc.; in this family, initial conditions (such as Gaussian distributions of reflectances or homoscedasticity) are not usually met in the remote sensing images This means that the power of these tests is seriously undermined, and classifications can be unnecessarily weak. The second family includes non-parametric methods (e.g., neural networks and classification trees) This second group addresses the problems posed by the first group by making no assumptions regarding reflectance distributions. MARS generates a model with an excessive number of knots; knots that contribute least to the overall fit are eliminated

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Results

Conclusion