Land Cover Classification Using Sematic Image Segmentation with Deep Learning

We evaluated the land cover classification performance of SegNet, which features semantic segmentation of aerial imagery. We selected four semantic classes, i.e., urban, farmland, forest, and water areas, and created 2,000 datasets using aerial images and land cover maps. The datasets were divided at a 8: 2 ratio into training (1,600) and validation datasets (400); we evaluated validation accuracy after tuning the hyperparameters. SegNet performance was optimal at a batch size of five with 100,000 iterations. When 200 test datasets were subjected to semantic segmentation using the trained SegNet model, the accuracies were farmland 87.89, forest 87.18, water 83.66, and urban regions 85.48%; the overall accuracy was 85.48%. Thus, deep learning-based semantic segmentation can be used to classify land cover.