Abstract
Estimation of the extent and spread of wildland fires is an important application of high spatial resolution multispectral images. This work addresses a fuzzy segmentation algorithm to map fire extent, active fire front, hot burn scar, and smoke regions based on a statistical model. The fuzzy results are useful data sources for integrated fire behavior and propagation models built using Dynamic Data Driven Applications Systems (DDDAS) concepts that use data assimilation techniques which require error estimates or probabilities for the data parameters. The Hidden Markov Random Field (HMRF) model has been used widely in image segmentation, but it is assumed that each pixel has a particular class label belonging to a prescribed finite set. The mixed pixel problem can be addressed by modeling the fuzzy membership process as a continuous Multivariate Gaussian Markov Random Field. Techniques for estimating the class membership and model parameters are discussed. Experimental results obtained by applying this technique to two Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images show that the proposed methodology is robust with regard to noise and variation in fire characteristics as well as background. The segmentation results of our algorithm are compared with the results of a K-means algorithm, an Expectation Maximization (EM) algorithm (which is very similar to the Fuzzy C-Means Clustering algorithm with entropy regularization), and an MRF-MAP algorithm. Our fuzzy algorithm achieves more consistent segmentation results than the comparison algorithms for these test images with the added advantage of simultaneously providing a proportion or error map needed for the data assimilation problem.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Publication Date
1-16-2007
Document Type
Article
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Recommended Citation
Y. Li, A. Vodacek, Y. Zhu An automatic statistical segmentation algorithm for extraction of fire and smoke region Remote Sensing of Environment, 108 (2007), pp. 171-178
Campus
RIT – Main Campus
Comments
Copyright © 2006 Elsevier Inc. All rights reserved. This is the pre-print of an article published by Elsevier. The final, published version is located here: https://doi.org/10.1016/j.rse.2006.10.023
This material is based upon work supported by the National Science Foundation under Grant No. CNS-0324989 and by the National Aeronautics and Space Administration under Grant No. NAG5-10051.
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