Multisource Data Fusion and Fisher Criterion Based Nearest Feature Space Approach to Landslide Classification

• Main Authors: Yi Chun (Benny) Wang, 王怡鈞
• Other Authors: 張陽郎
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/rtw5uf

博士 === 國立臺北科技大學 === 電機工程系博士班 === 103 === In this dissertation, a novel technique known as the Fisher criterion based nearest feature space (FCNFS) approach is proposed for supervised classification of multisource images for the purpose of landslide hazard assessment. The method is developed for land cover classification based upon the fusion of remotely sensed images of the same scene collected from multiple sources. This dissertation presents a framework for data fusion of multisource remotely sensed images, consisting of two approaches: the band generation process (BGP) and the FCNFS classifier. The multiple adaptive BGP is introduced to create an additional set of bands that are specifically accommodated to the landslide class and are extracted from the original multisource images. In comparison to the original nearest feature space (NFS) method, the proposed FCNFS classifier uses the Fisher criterion of between-class and within-class discrimination to enhance the classifier. In the training phase, the labeled samples are discriminated by the Fisher criterion, which can be treated as a pre-processing step of the NFS method. After completion of the training, the classification results can be obtained from the NFS algorithm. Experimental results show that the proposed BGP/FCNFS framework is suitable for land cover classification in Earth remote sensing and improves the classification accuracy compared to conventional classifiers.