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Multisource data fusion for landslide classification using generalized positive Boolean functions
- Source :
- IEEE Transactions on Geoscience and Remote Sensing. June, 2007, Vol. 45 Issue 6, p1697, 12 p.
- Publication Year :
- 2007
-
Abstract
- In this paper, a novel technique is proposed for a supervised classification of multisource images for the purpose of landslide hazard assessment. The method, known as the generalized positive Boolean function (GPBF), is developed for land cover classification based on the fusion of remotely sensed images of the same scene collected from multiple sources. It presents a framework for data fusion of multisource remotely sensed images, which consists of two approaches, referred to as the band generation process (BGP) and the positive Boolean function (PBF) classifier. The PBF classifier developed from a stack filter has been successfully applied in hyperspectral image classification. For the PBF to be effective for multispectral images, a multiple adaptation BGP is introduced to create a new set of additional bands especially accommodated to landslide classes. These bands include nonlinear normalized difference vegetation index data and morphological information in the form of digital elevation model (DEM)-derived slope values that originate from multiple sources. The performance of the proposed method is evaluated by fusing Systeme Pour l'Observation de ht Terre images and DEM information for land cover classification during the post 921 Earthquake period in Taiwan. Experimental results demonstrate the proposed GPBF multiclassification approach is suitable for land cover classification in Earth remote sensing and improves the precision of image classification compared to conventional classifiers. Index Terms--Band generation process (BGP), data fusion, digital elevation model (DEM), generalized positive Boolean function (GPBF), normalized difference vegetation index (NDVI), positive Boolean function (PBF), stack filter.
- Subjects :
- Landslides -- Forecasts and trends
Remote sensing -- Usage
Algebra, Boolean -- Usage
Emergency management -- Research
Emergency management -- Technology application
Satellite imaging -- Usage
Disaster planning
Market trend/market analysis
Technology application
Business
Earth sciences
Electronics and electrical industries
Subjects
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 45
- Issue :
- 6
- Database :
- Gale General OneFile
- Journal :
- IEEE Transactions on Geoscience and Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- edsgcl.164829052