Classifying natural aerial scenery for autonomous aircraft emergency landing

Mejias, Luis (2014) Classifying natural aerial scenery for autonomous aircraft emergency landing. In Proceedings of the 2014 International Conference on Unmanned Aerial Systems (ICUAS'14), IEEE, Orlando, Florida, USA, pp. 1236-1242.

Abstract

In this paper, we present an approach for image-based surface classification using multi-class Support Vector Machine (SVM). Classifying surfaces in aerial images is an important step towards an increased aircraft autonomy in emergency landing situations. We design a one-vs-all SVM classifier and conduct experiments on five data sets. Results demonstrate consistent overall performance figures over 88% and approximately 8% more accurate to those published on multi-class SVM on the KTH TIPS data set. We also show per-class performance values by using normalised confusion matrices. Our approach is designed to be executed online using a minimum set of feature attributes representing a feasible and ready-to-deploy system for onboard execution.

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ID Code: 72213
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: CEDM
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100) > Aircraft Performance and Flight Control Systems (090104)
Divisions: Current > Research Centres > Australian Research Centre for Aerospace Automation
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
Copyright Owner: Copyright 2014 IEEE
Copyright Statement: Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Final version of the publication can be found at http://ieeexplore.ieee.org/
Deposited On: 04 Jun 2014 22:17
Last Modified: 12 Sep 2016 16:32

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