The Dynamic Extraction of Classification Rules for Land Consolidation through Object-oriented Features Space Optimization

The high resolution remote sensing image is an important data sources for the accurate extraction of land consolidation area surface information.In this paper,a new object-based method,combining with genetic algorithm and artificial immune algorithm,is used to extract classification rules based on the characteristics of the sample image.After fuzzy classification,the results show that overall accuracy is increasing from 40% by traditional method to 86% corresponding to the genetic algorithm and 90% corresponding to the artificial immune algorithm,and the Kappa coefficient is increasing from 0.3 by traditional methods to 0.82 corresponding to the genetic algorithm and 0.89 corresponding to the artificial immune algorithm.All in all,not only this method can improve the convenience and versatility,changing the previous situation that the rule extraction requires users a large amount of priori knowledge and testing,but also the test results show the significant improvement in classification accuracy.Therefore,it has an important significance for land consolidation,especially using the high-remote sensing images for feature identifying and monitoring.