A Study of Applying Hyper-Rectangle Learning Model to Interpret the Classification of Remote Sensing Image

• Main Authors: Wern Horn Lu, 盧文鴻
• Other Authors: Li Chen
• Format: Others
• Language: zh-TW
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/95933853781797954300

碩士 === 中華大學 === 土木工程學系碩士班 === 88 === Abstract Recently, many nations comprehensively apply remote sensing method, to serve as the important data resource of analysis and decision planning. The application of this in this aspect has also been more and more popular. The remote sensing data coverage is provided with both comprehensive and up-to-date characteristics, able to serve as a kind of effective survey tool for build environmental resource database with convenient service. This research attempts to engage in image classification with the Hyper- Rectangles Learning Model in artificial intellectual field. This model belongs to case base method. That is, through experience to attain the feedback revision objective, but starting from super space geometric concept, to store the past data in hyper rectangles structure, not only saving memory but also with systematic significance; in particular it can attain perfect accuracy in training stage which can hardly be attained in general traditional classification mode. The research, in view of the characteristics of classification, innovates the Hyper-Rectangles Learning Model, able to considerably upgrade accuracy ratio. In order to verify the classification ability of the model, theoretical math function is applied as a test example, then the SPOT satellite multispectral image applied to Tzengwen Reservoir water collection area is applied to perform land cover interpretation, with input variables in adopting the 6 classification features provided by specialists and scholars. The result shows that either five model test examples or the applied example , The accuracy of applying Hyper-Rectangles Learning Model is better than Artificial Neural Network of BPN .