Performance of a fast iterative algorithm for unsupervised bayesian classification of multispectral and hyperspectral images

Alessandro Barducci; Alessandro Mecocci; Alessandro Paperini

doi:10.1117/12.463089

13 March 2003 Performance of a fast iterative algorithm for unsupervised Bayesian classification of multispectral and hyperspectral images

Alessandro Barducci, Alessandro Mecocci, Alessandro Paperini

Author Affiliations +

Proceedings Volume 4885, Image and Signal Processing for Remote Sensing VIII; (2003) https://doi.org/10.1117/12.463089
Event: International Symposium on Remote Sensing, 2002, Crete, Greece

Abstract

Image classification based on radiometric and spectral information is connected with histogram labelling, therefore the required computing time exponentially grows with increasing pixel depth (m) and the number of spectral bands (M). In fact, histogram labelling requires the estimation of a cost function summed over every state of the input image, for a total number of states N = 2mM. Various techniques have been exploited in order to overcome this difficulty, but no general and satisfactory solution has been pointed out yet. We developed an iterative fitting algorithm in which the image histogram is analysed in the neighbourhood of its peaks. Each peak is fitted independently from other, using only local histogram data that feed an iterative fast procedure. Once the current peak has been processed, the input histogram is cleared from its contributions, and the residual histogram maximum is processed. The performance of the algorithm has been investigated by processing several TM images and photogrammetry. Executed tests have shown a good ability of the algorithm to accurately recognise different image classes, performing the entire classification in a very short time.

Citation Download Citation

Alessandro Barducci, Alessandro Mecocci, and Alessandro Paperini "Performance of a fast iterative algorithm for unsupervised Bayesian classification of multispectral and hyperspectral images", Proc. SPIE 4885, Image and Signal Processing for Remote Sensing VIII, (13 March 2003); https://doi.org/10.1117/12.463089

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