Comparison of CNN-based segmentation models for forest type classification

Abstract. We present the results from evaluating various Convolutional Neural Network (CNN) models to compare their usefulness for forest type classification. Machine Learning based on CNNs is known to be suitable to identify relevant patterns in remote sensing imagery. With the availability of free data sets (e.g. the Copernicus Sentinel-2 data), Machine Learning can be utilized for forest monitoring, which provides useful and timely information helping to measure and counteract the effects of climate change. To this end, we performed a case study with publicly available data from the federal state of North Rhine-Westphalia in Germany. We created an automated pipeline to preprocess and filter this data and trained the CNN models UNet, PSPNet, SegNet, and FCN-8. Since the data contained large rural areas, we augmented the imagery to improve classification results. We reapplied the trained models to the data, compared the results for each model, and evaluated the effect of augmentation. Our results show that UNet performs best with a categorical accuracy of 73% when trained with augmented imagery.