Hyperspectral Images Fusion Classification Based on the DS Evidence Theory - LI Hao; YU Hong; RAO Tong; ZHOU Shuai; SHEN Feng (Journal of Harbin University of Science and Technology)
Hyperspectral images contain rich information of ground objects and the technology is widely used in many occasions. Since each classification model has different classification performance, effectively utilizing the difference in the performance of each classification model is an essential step to achieve fusion classification. Therefore, a hyperspectral image classification method based on DS evidence theory is proposed. Different prediction results are generated since the existing classification models extract different features from the spatial and spectral domains of HSI data. In this fusion method, a multi-layer perceptron network and random forest network are used for fusion classification experiments. The network improves the accuracy of classification results by taking advantage of the difference in extracted features of each classification network. The experimental results show that the DS fusion model can improve the classification accuracy and is superior to the linear average weithted fusion model when there are some differences in the classification accuracy of different networks.