Integrating recursive Bayesian estimation with support vector machine to map probability of flooding from Multispectral Landsat Data

Sarker, Chandrama, Mejias Alvarez, Luis, & Woodley, Alan (2016) Integrating recursive Bayesian estimation with support vector machine to map probability of flooding from Multispectral Landsat Data. In Digital Image Computing: Techniques and Applications (DICTA 2016), 30 November - 2 December 2016, Gold Coast, Qld.

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This paper addresses the challenge of introducing a Bayes rules to measure flood probability from multispectral data. Machine learning classifiers were applied to map the extent of flood inundation from multispectral remote sensing imagery. The paper applies Extended Support Vector Machine classifier along with linear spectral unmixing to obtain the classification output. K-means clustering is applied on pre and post flood images to select SVM training samples from clustering outcome of the most informative spectral band. Experiments were conducted by dividing training and testing samples into two groups. The efficiency of classifier was enhanced by introducing the Bayesian probability measure and performance was assessed by using precision and recall metrics on the pre and post Bayesian flood probability estimation. It has been observed that for some test cases in this study there was a substantial improvement in precision-recall curve with high precision values and low recall rate. The optimal flood probability threshold value has also been easily calculated by calculating and analising iteratively precision and recall.

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ID Code: 98658
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Remote Sensing, K-means Clustering, Support Vector Machine Classifier, Bayesian Estimation, Flood Probability Mapping
DOI: 10.1109/DICTA.2016.7797054
Divisions: Current > Research Centres > Australian Research Centre for Aerospace Automation
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Facilities: HPC – QUT Supercomputer
Copyright Owner: Copyright 2016 IEEE
Copyright Statement: Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Deposited On: 06 Sep 2016 01:55
Last Modified: 16 Jan 2017 10:54

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