Land Use and Land Cover Classification by Combining GLCM, SNIC, and Machine Learning Algorithms in Google Earth Engine Environment (Case Study: Part of the Lands of North Mahabad, West Azerbaijan)

Document Type : Original Article

Author

Dep. of Remote Sensing and GIS, Tarbiat Modares University, Tehran, Iran

Abstract

Introduction: In recent decades, land use and land cover changes information has been successfully derived from remote sensing data at various levels, from local to global scale. Accurate and frequent monitoring of these changes is required for urban planning, precision agriculture, and sustainable management of land resources. The availability of remote sensing data by providing different levels of spatial details, as well as the development of satellite image classification algorithms, has made object-oriented approaches more useful in land use and land cover (LULC) classification compared to traditional approaches. Therefore, in this study, an object-oriented approach using a combination of GLCM, SNIC, and machine learning algorithms is presented to classify the LULC of a part of the lands of North Mahabad, West Azerbaijan, in 2019 using satellite images in Google Earth Engine.
Data and Methods: For this purpose, after preparing the initial dataset, which contains the bands of Sentinel-1 and Sentinel-2 images, the ALOS digital surface model, and NDVI, BSI, SAVI, and total scattering power (TSP) indices, two pixel-based and object-oriented approaches, as well as the random forest algorithm, were used to classify land use and land cover, and their results were compared to explain the best approach in terms of the accuracy of the various classes. In the object-oriented approach, textural measures were extracted by applying the GLCM matrix to the initial dataset. Due to the increase in the number of bands, the PCA method was used to reduce the dimensions of the image. Finally, by combining the PC1 layer and the segmentation layer obtained from the SNIC algorithm, the random forest algorithm was considered to produce land use and land cover maps of the study area.
Results and Discussion: According to the research findings, the object-oriented approach performed better than the pixel-based approach in classifying various land use classes in the study area, with an overall accuracy and kappa coefficient of 86.40% and 0.8307, respectively, compared to 82.73% and 0.8028. The results of the accuracy evaluation criteria showed that the producer accuracy of most of the classes except for corn, fall irrigated vegetables, wheat, and barley irrigated in the object-oriented approach was higher than the pixel-based method, and their classification accuracy was more than 90%. Additionally, water, build-up, corn, and sugar beet classes have the highest user accuracy in the object-oriented LULC map.
Conclusion: The findings showed that the appropriate determination of the super-pixel size of the SNIC clustering algorithm and the use of GLCM texture criteria effectively improved the performance of the proposed approach in land use and land cover classification

Keywords

References

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