Sky segmentation by fusing clustering with neural networks

Abstract

Sky segmentation is an important task for many applications related to obstacle detection and path planning for autonomous air and ground vehicles. In this paper, we present a method for the automated sky segmentation by fusing K-means clustering and Neural Network (NN) classifications. The performance of the method has been tested on images taken by two Hazcams (ie., Hazard Avoidance Cameras) on NASA's Mars rover. Our experimental results show high accuracy in determining the sky area. The effect of various parameters is demonstrated using Receiver Operating Characteristic (ROC) curves. © 2013 Springer-Verlag.