Assessment of Supervised Classifiers for Land Cover Categorization Based on Integration of ALOS PALSAR and Landsat Data

Dorothea Deus

doi:10.4236/ars.2018.72004

Abstract

Many supervised classification algorithms have been proposed, however, they are rarely evaluated for specific application. This research examines the performance of machine learning classifiers support vector machine (SVM), neural network (NN), Random Forest (RF) against maximum classifier (MLC) (traditional supervised classifier) in forest resources and land cover categorization, based on combination of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and Landsat Thematic Mapper (TM) data, in Northern Tanzania. Various data categories based on Landsat TM surface reflectance, ALOS PALSAR backscattering and their derivatives were generated for various classification scenarios. Then a separate and joint processing of Landsat and ALOS PALSAR data were executed using SVM, NN, RF and ML classifiers. The overall classification accuracy (OA), kappa coefficient (KC) and F1 score index values were computed. The result proves the robustness of SVM and RF in classification of forest resource and land cover using mere Landsat data and integration of Landsat and PALSAR (average OA = 92% and F1 = 0.7 to 1). A two sample t-statistics was utilized to evaluate the performance of the classifiers using different data categories. SVM and RF indicate there is no significance difference at 5% significance level. SVM and RF show a significant difference when compared to NN and ML. Generally, the study suggests that parametric classifiers indicate better performance compared to parametric classifier.

Highlights

Classification of satellite image is a very significant part of remote sensing image analysis, object and pattern recognition, mapping and monitoring of forest covers and natural resources
This research examines the performance of machine learning classifiers support vector machine (SVM), neural network (NN), Random Forest (RF) against maximum classifier (MLC) in forest resources and land cover categorization, based on combination of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and Landsat Thematic Mapper (TM) data, in Northern Tanzania
The classification results attained based on different data groups (A-C) (Table 1) and tested classifiers are presented in Figure 2 for Overall accuracy, Figure 3 for Kappa coefficients and Table 2 for F1 score attained for every land cover type

Summary

Introduction

Classification of satellite image is a very significant part of remote sensing image analysis, object and pattern recognition, mapping and monitoring of forest covers and natural resources. Many supervised image classification algorithms have been developed and utilized for forest and land cover mapping, ranging from machine learning algorithms to traditional classifiers [1] [2]. It is difficult to identify the best image classification algorithm which suits a particular environment. This is because numerous factors tend to affect the results: scheme of classification, satellite data in use, image pre-processing, training and validation sample selection and collection, learning algorithm and post processing approaches and validation techniques [4]. Evaluation of commonly applied machine learning algorithms is essential using same satellite dataset and scheme of classification to aid the selection of suitable algorithm for a particular application. It is very important to assess their performance in various kinds of environment using various types of remote sensing datasets [2]

Results

Discussion

Conclusion