The application value of mathematics classification methods would be decreased because of the different result from those different classification methods been cholice.Some mathematics classification methods which wer...The application value of mathematics classification methods would be decreased because of the different result from those different classification methods been cholice.Some mathematics classification methods which were common used had been selected,such as Multivariate Weight Method、Cluster Analysis、Primary Component Analysis、Fuzzy Comprehensive Evaluate Method and Discriminate analysis between multiple groups and a integration model of mathematics classification which based on the Bayes role had been pointed out in this paper.the problem of the divergence samples attributed would be resolved by using the integration model of mathematics classification and the applied efficiency had been compared which between the different classification methods and integration model,The result by using integration had showed much more reasonable than that by using single classification method above.展开更多
With the development of researches on the classification quality of remote sensing images, researchers thought that uncertainty is the main factor that influences classification quality. This study puts forward an app...With the development of researches on the classification quality of remote sensing images, researchers thought that uncertainty is the main factor that influences classification quality. This study puts forward an approach to uncertainty repre- sentation, which is developed from two aspects： formalized description and comprehensive evaluation. First, we complete the classification using fuzzy surveillance approach, taking it as a formalized description of classification uncertainty. Then we in- troduce a hybrid entropy model for classification uncertainty evaluation, which can meet the requirement of comprehensive reflection of several uncertainties, while constructing the evaluation index from pixel scale with the full consideration of the different contribution to the error rate of each pixel. Finally, an application example will be studied to examine the new method. The result shows that the evaluation results fully reflect the classification quality, when compared with the conventional evaluation method which constructs models from unitary uncertainty and category scale.展开更多
文摘The application value of mathematics classification methods would be decreased because of the different result from those different classification methods been cholice.Some mathematics classification methods which were common used had been selected,such as Multivariate Weight Method、Cluster Analysis、Primary Component Analysis、Fuzzy Comprehensive Evaluate Method and Discriminate analysis between multiple groups and a integration model of mathematics classification which based on the Bayes role had been pointed out in this paper.the problem of the divergence samples attributed would be resolved by using the integration model of mathematics classification and the applied efficiency had been compared which between the different classification methods and integration model,The result by using integration had showed much more reasonable than that by using single classification method above.
基金Supported by the Provincial Science Research Program in Hubei Province of China (No. ETZ2007A03)
文摘With the development of researches on the classification quality of remote sensing images, researchers thought that uncertainty is the main factor that influences classification quality. This study puts forward an approach to uncertainty repre- sentation, which is developed from two aspects： formalized description and comprehensive evaluation. First, we complete the classification using fuzzy surveillance approach, taking it as a formalized description of classification uncertainty. Then we in- troduce a hybrid entropy model for classification uncertainty evaluation, which can meet the requirement of comprehensive reflection of several uncertainties, while constructing the evaluation index from pixel scale with the full consideration of the different contribution to the error rate of each pixel. Finally, an application example will be studied to examine the new method. The result shows that the evaluation results fully reflect the classification quality, when compared with the conventional evaluation method which constructs models from unitary uncertainty and category scale.
