Object Classification Using Multispectral Sensor Data Fusion

Michael P. Cain; Susan A. Stewart; Jeffrey B. Morse

doi:10.1117/12.960481

14 September 1989 Object Classification Using Multispectral Sensor Data Fusion

Michael P. Cain, Susan A. Stewart, Jeffrey B. Morse

Proceedings Volume 1100, Sensor Fusion II; (1989) https://doi.org/10.1117/12.960481
Event: SPIE 1989 Technical Symposium on Aerospace Sensing, 1989, Orlando, FL, United States

Abstract

In this paper, the potential benefits in applying sensor fusion to object classification are discussed. A specific example is presented that involves the fusion of multiple band IR and visible light data collected from co-located sensors. Pattern vectors describing the objects were based on features extracted form the simulated target signatures observed within the sensor wavebands individually and also by 'fusing' the multispectral data. The pattern vectors were then subjected to feature analysis using a variety of statistical pattern recognition techniques to determine the relative contribution of each feature to classification performance. Features selected through this process were then used in subsequent classification algorithms which established class boundaries, classified the objects, determined confidence levels, and calculated error probabilities. A neural network paradigm was also applied to the same data set to determine the relative merit of the features and to classify the objects. In particular, a competitive learning algorithm was used. Analysis methods and performance comparisons are presented.

Citation Download Citation

Michael P. Cain, Susan A. Stewart, and Jeffrey B. Morse "Object Classification Using Multispectral Sensor Data Fusion", Proc. SPIE 1100, Sensor Fusion II, (14 September 1989); https://doi.org/10.1117/12.960481

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