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.
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.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||Remote Sensing, K-means Clustering, Support Vector Machine Classifier, Bayesian Estimation, Flood Probability Mapping|
|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|
Repository Staff Only: item control page