Utility of Neural Net Classification for Remote Sensing Data Based on an Improved Image Fusion Algorithm

摘要

There are many different advantages and disadvantages in traditional subpixel classification methods such as uncertain classification accuracy, etc. which bring limitations for commonly application. In recent years, many algorithms have been used to resolve these problems. In this paper, based on an optimized image fusion algorithm, a comparison experiment on traditional maximum likelihood classification and neural net classification is performed. According to the classification accuracy data, the overall accuracy of classification increased from 81.67% to 89.67%.