Sky segmentation by fusing clustering with neural networks

Document Type

Conference Proceeding


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.


Biomedical | Controls and Control Theory | Electrical and Computer Engineering | Electrical and Electronics | Electromagnetics and Photonics | Power and Energy | Signal Processing


Use Find in Your Library, contact the author, or interlibrary loan to garner a copy of the item. Publisher policy does not allow archiving the final published version. If a post-print (author's peer-reviewed manuscript) is allowed and available, or publisher policy changes, the item will be deposited.

UNLV article access