Comparative Assessment of Classification Algorithms for Land Cover Mapping Using Multispectral and PCA Images of Landsat

Zohaib; Nawai Habib; Abu Talha Manzoor; Sawaid Abbas; Samawia Rizwan

Authors

Zohaib Smart Sensing for Climate and Development, Centre for Geographic Information System, University of the Punjab, Lahore, Pakistan.
Nawai Habib Smart Sensing for Climate and Development, Centre for Geographic Information System, University of the Punjab, Lahore, Pakistan
Abu Talha Manzoor Smart Sensing for Climate and Development, Centre for Geographic Information System, University of the Punjab, Lahore, Pakistan
Sawaid Abbas Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong
Samawia Rizwan Smart Sensing for Climate and Development, Centre for Geographic Information System, University of the Punjab, Lahore, Pakistan

Keywords:

MLC, SVM, RF, PCA, Normalized indices, Landsat-8, ESA, LULC

Abstract

Introduction: The development of remote sensing techniques and access to free satellite data has enabled accurate mapping of land use land cover (LULC) to analyze landscape transformations and changes in the ecosystem and it is required to assess the accuracy of multiple LULC modeling algorithms for their effective applications.

Novelty Statement: This study performed a comprehensive assessment of supervised machine learning and conventional classification algorithm, applied to a Landsat 8 image with 30 m spatial resolution of the Shangla and Battagram districts, KPK, Pakistan.

Materials and Methods: Three classification algorithms, including Maximum Likelihood Classification (MLC), Support Vector Machines (SVM), and Random Forest (RF), were explored. We evaluate the performance of these algorithms on multispectral images as well as on composite images obtained from Principal Component Analysis (PCA) and Band Ratioing and/or Normalized Indices. Furthermore, the accuracy of these algorithms with different datasets was compared with the recently released WorldCover LULC by the European Space Agency (ESA).

Results and Discussions: The results revealed that the SVM algorithm exhibited superior performance, and achieved an overall accuracy of 90.43% and a kappa coefficient of 0.8792. MLC and RF algorithms also yielded promising results, with overall accuracies of 85.58% and 88.46%, respectively. Additionally, when assessed the accuracy of the WorldCover, with similar validation samples, we found that the overall accuracy of the ESA’s LULC was 70.67% in the study area.

Concluding Remarks: These findings highlight the strengths and limitations of each algorithm, offering insights into their suitability for LULC classification and the applicability of available global LULC maps.

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