Semantic segmentation model for land cover classification from satellite images in Gambella National Park, Ethiopia

Abstract This work uses machine learning approaches to present semantic segmentation for land cover classification in Gambella National Park (GNP). Land cover classification has become more accurate due to developments in remote sensing data. Land cover classification from satellite images has been studied, but the methodologies and satellite data employed so far are not suitable for research regions with the possibility to find heterogeneous land cover classes within small areas. Previous studies found issues with the satellite images coarser spatial resolution, the use of standard statistical methods as classifiers, and the difficulty in optimal patch size selection when patch-based classification is used. To address these issues, we suggested a deep learning-based semantic segmentation model that could be utilized as a pixel-level land cover classification technique. The suggested technique employed high-resolution Sentinel-2 satellite images of our study area (GNP) as a dataset and constructed and assessed pixel-level classification models. As a deep learning-based classification model, we have used the Link-Net architecture and its encoder part was modified further to incorporate the state-of-the-art architecture called ResNet34. The developed models, support vector machine with CNN features (CNN–SVM), random forest with CNN features (CNN-RF), LinkNet model with ResNet-34 as encoder (LinkNet-ResNet34), attain average F1-Score values of 81%,82%, and 87.4% respectively.