Self-Mutating Network for Domain Adaptive Segmentation of Aerial Images

Abstract

The domain-adaptive semantic segmentation of aerial images using a deep-learning technique is still challenging owing to the domain gaps between aerial images obtained in different areas. Currently, various convolutional neural network (CNN)-based domain adaptation methods have been developed to decrease the domain gaps. However, they still show poor performance for object segmentation when they are applied to images from other domains. In this paper, we propose a novel CNN-based self-mutating network (SMN), which can adaptively adjust the parameter values of convolutional filters as a response to the domain of an input image for better domain-adaptive segmentation. For the SMN, the parameter mutation technique was devised for adaptively changing parameters, and a parameterfluctuationtechniquewasdevelopedtorandomlyconvulsetheparameters. By adopting the parameter mutation and fluctuation, adaptive self-changing and fine-tuning of parameters can be realized for images from different domains, resulting in better prediction in domain-adaptive segmentation. Meanwhile, the results of the ablation study indicate that the SMN provided 11.19% higher Intersection over Union values than other state-of-the-art methods, demonstrating its potential for the domain-adaptive segmentation of aerial images.