Segmentation of Imbalanced Classes in Satellite Imagery using Adaptive Uncertainty Weighted Class Loss

Abstract

We propose a novel loss function for the training of deep Convolutional Neural Networks (CNNs) focusing on land use and land cover classification in remote sensed data. In satellite imagery, object classes are often highly imbalanced leading to poor pixel-wise classification results when using standard training methods only. In this work, we introduce a loss function which leverages the per class uncertainty of the model during training together with median frequency balancing of the class pixels. We evaluate our result on aerial images of the state-of-the-art dataset Vaihingen. We obtain a significant improvement of the F1-Score and pixel accuracy against the standard cross entropy loss on the small car class. The overall Fl-Score using a single CNN achieves 89.35% resulting in an error reduction of 21.22% against the baseline.