A game-theoretic tree matching approach for object detection in high-resolution remotely sensed images

Yilong Liang; Nathan D. Cahill; Eli Saber; David W. Messinger

doi:10.1117/12.2195178

15 October 2015 A game-theoretic tree matching approach for object detection in high-resolution remotely sensed images

Yilong Liang, Nathan D. Cahill, Eli Saber, David W. Messinger

Proceedings Volume 9643, Image and Signal Processing for Remote Sensing XXI; 964315 (2015) https://doi.org/10.1117/12.2195178
Event: SPIE Remote Sensing, 2015, Toulouse, France

Abstract

In this paper, we propose a game-theoretic tree matching algorithm for object detection in high resolution (HR) remotely sensed images, where, given a scene image and an object image, the goal is to determine whether or not the object exists in the scene image. To that effect, tree based representations of the images are obtained using a hierarchical scale space approach. The nodes of the tree denote regions in the image and edges represent the relative containment between different regions. Once we have the tree representations of each image, the task of object detection is reformulated as a tree matching problem. We propose a game-theoretic technique to search for the node correspondences between a pair of trees. This method involves defining a non-cooperative matching game, where strategies denote the possible pairs of matching regions and payoffs determine the compatibilities between these strategies. Trees are matched by finding the evolutionary stable states (ESS) of the game. To validate the effectiveness of the proposed algorithm, we perform experiments on both synthetic and HR remotely sensed images. Our results demonstrate the robustness of the tree representation with respect to different spatial variations of the images, as well as the effectiveness of the proposed game-theoretic tree matching algorithm.

Citation Download Citation

Yilong Liang, Nathan D. Cahill, Eli Saber, and David W. Messinger "A game-theoretic tree matching approach for object detection in high-resolution remotely sensed images", Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 964315 (15 October 2015); https://doi.org/10.1117/12.2195178

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Detection and tracking algorithms

Algorithms

Image processing algorithms and systems

Sensors

Image resolution

Image processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years