Deep Neural Networks Capabilities for Semantic Segmentation of Noisy Aerial Images

Abstract

Semantic segmentation is one of the important ways of extracting information about objects in images. State of the art neural network algorithms allow to perform highly accurate semantic segmentation of images, including aerial photos. However, in most of the works authors use high-quality low-noise images. In this work, we study the ability of neural networks to correctly segment images with intensive uncorrelated Gaussian noise. The study brings us three main conclusions. Firstly, it demonstrates that neural network algorithms are capable of working with extreme image distortions without using additional filtration or image recovery techniques. Secondly, the experiments quantitatively show that distortion intensity can be negated with increased training set size. Such process is similar to model’s quality improvement and generalization due to training dataset enlargement. Finally, we quantitatively demonstrate how image aggregation techniques affect training with noised data.