Hyperspectral Image Classification Based on the Weighted Probabilistic Fusion of Multiple Spectral-spatial Features

• Main Authors: ZHANG Chunsen, ZHENG Yiwei, HUANG Xiaobing, CUI Weihong
• Format: Article
• Language: zho
• Published: Surveying and Mapping Press 2015-08-01
• Series: Acta Geodaetica et Cartographica Sinica
• Subjects: spectral-spatial feature; probabilistic fusion; SVM; hyperspectral; classification
• Online Access: http://html.rhhz.net/CHXB/html/2015-8-909.htm

A hyperspectral images classification method based on the weighted probabilistic fusion of multiple spectral-spatial features was proposed in this paper. First, the minimum noise fraction (MNF) approach was employed to reduce the dimension of hyperspectral image and extract the spectral feature from the image, then combined the spectral feature with the texture feature extracted based on gray level co-occurrence matrix (GLCM), the multi-scale morphological feature extracted based on OFC operator and the end member feature extracted based on sequential maximum angle convex cone (SMACC) method to form three spectral-spatial features. Afterwards, support vector machine (SVM) classifier was used for the classification of each spectral-spatial feature separately. Finally, we established the weighted probabilistic fusion model and applied the model to fuse the SVM outputs for the final classification result. In order to verify the proposed method, the ROSIS and AVIRIS image were used in our experiment and the overall accuracy reached 97.65% and 96.62% separately. The results indicate that the proposed method can not only overcome the limitations of traditional single-feature based hyperspectral image classification, but also be superior to conventional VS-SVM method and probabilistic fusion method. The classification accuracy of hyperspectral images was improved effectively.