Multisource data fusion for landslide classification using generalized positive Boolean functions

作者：Chang Yang Lang^*; Liang Long Shin; Han Chin Chuan; Fang Jyh Perng; Liang Wen Yew; Chen Kun Shan

来源：IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(6): 1697-1708.

DOI：10.1109/TGRS.2007.895832

• band generation process (BGP)
• data fusion
• digital elevation model (DEM)
• generalized positive Boolean GPBF)
• normalized difference vegetation index (NDVI)
• positive Boolean PBF)
• stack filter

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 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 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 la 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.

• 出版日期2007-6
• 单位中央民族大学