Application of ant colony optimization (ACO) algorithm to remote sensing image classification

Qin Dai; Jianbo Liu

doi:10.1117/12.749344

15 November 2007 Application of ant colony optimization (ACO) algorithm to remote sensing image classification

Qin Dai, Jianbo Liu

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67881A (2007) https://doi.org/10.1117/12.749344
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

Ant Colony Optimization (ACO) algorithm takes inspiration from the coordinated behavior of ant swarms, which has been applied in many study fields as a novel evolutionary technology to solve optimization problems. But it has rarely been used to process remote sensing data. Using the ACO algorithm to remote sensing image classification does not assume an underlying statistical distribution for the pixel data, the contextual information can be taken into account, and it has strong robustness. In this paper, taking Landsat TM data as an example, the process of ACO method in remote sensing data classification is introduced in detail, and has achieved a good result. The study results suggest that ACO become a new effective method for remote sensing data processing.

Citation Download Citation

Qin Dai and Jianbo Liu "Application of ant colony optimization (ACO) algorithm to remote sensing image classification", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67881A (15 November 2007); https://doi.org/10.1117/12.749344

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