COMPARISON OF CLASSIFICATION ALGORITHMS OF IMAGES FOR THE MAPPING OF THE LAND COVERING IN TASSO FRAGOSO MUNICIPALITY, BRAZIL

P R M Pereira,F W D Costa,L Macarringe,A C Botelho,E L Bolfe

doi:10.5194/isprs-annals-v-3-2021-167-2021

Abstract

Abstract. One of the main applications of satellite images is the characterization of terrestrial coverage, that from the use of classification techniques, allows the monitoring of spatial transformations of the terrestrial surface, this process being directly associated with the potential of classifiers to differentiate the most diverse data present in the images, a fundamental aspect for the use of remote sensing data. This article evaluates the performance of different classification algorithms in the mapping classes of land use and land cover in medium resolution images from the Landsat 8 program, the test area of this test corresponds to the Municipality of Tasso Fragoso (State Maranhão - Brazil), stands out for a typical vegetation cover of the Cerrado Biome, presents similar spectral patterns that induce high difficulty of class differentiation automatically. In this paper, were analyzed the machine learning algorithms C5.0 and Random Forest in comparison to traditional classification algorithms being the Minimum Distance and the Spectral Angle Mapper. The best results were generated by Random Forest with 90% accuracy and Kappa of 0.861, followed by the C5.0 algorithm. Traditional algorithms, on the other hand, presented a lower precision rate with global accuracy, not exceeding 75% of accuracy and Kappa varying between 0.507 and 0.627. The accuracy of the producer showed that all the algorithms, in major or minor tendency presented difficulties in to differentiate the areas, with rates of mistakes varying between 25 and 75%, being the main, the confusion with pastoral areas.

Highlights

The data associated to the covering and land use enables the detailed comprehension of the spatial organization, and are considered basilar information to many environmental and social economical applications, being it a thematic of relevant interest in the most diversified areas (Azzari, Lobell, 2017; Jin et al 2017)
The general objective of this paper is to evaluate and compare the performance of classification algorithms: Minimal Distance (MD); Spectral Angle Mapper (SAM), Random Forest (RF), C5.0 Decision Tree for the mapping of classes of land covering to the cerrado biome as of Landsat 8 images
The algorithm RF is significantly better than the other ones (59.56), followed by C5.0 (49.3780), this result corroborates with the values of kappa index and smaller variance, which showed the superiority of these algorithms before the others

Summary

Introduction

The data associated to the covering and land use enables the detailed comprehension of the spatial organization, and are considered basilar information to many environmental and social economical applications, being it a thematic of relevant interest in the most diversified areas (Azzari, Lobell, 2017; Jin et al 2017). The information extraction represents a challenge, many factors, such as the complexity of landscape, scale of information, image processing and approaches of classification may affect the success of a classification (Lu, Weng, 2007). The automatic image classification is a complex process that can be affected by many factors. It is directly associated with the ability of the algorithms in the distinction of different patterns present in the image, which represents a challenge, especially in environments with high spectral homogeneity, which directly interferes in the result

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Conclusion