Uncertainty modeling and measuring method for remote sensing image features

Abstract

The invention discloses an uncertainty modeling and measuring method for remote sensing image features. The method is mainly composed of two parts of uncertainty modeling of remote sensing image features in a geographic space domain and uncertainty modeling of a feature space domain. Then, weighted combination is performed on the geographical space uncertainty and the feature space uncertainty toobtain a comprehensive feature uncertainty index FUI (Feature Unity Index) so as to measure the feature uncertainty of the remote sensing image more accurately and comprehensively. Different expression characteristics of the image characteristic uncertainty under different visual angles are considered, a characteristic uncertainty quantification result of the whole image pixel by pixel can be provided, and manual intervention can be minimized. The method is high in accuracy and adaptive degree, high in calculation efficiency, high in operability, easy to implement and high in expandability ofthe whole model. The uncertainty quantification result has very high indication capability for classification errors, so that the method has very high practical value.

Description

technical field [0001] The invention belongs to the field of remote sensing image processing and statistical modeling, in particular to an uncertainty modeling and measurement method for remote sensing image features. Background technique [0002] Remote sensing image classification products have very important application value in many aspects such as natural disaster monitoring, environmental protection, urban planning and decision making. But current remote sensing image classification techniques still cannot achieve 100% accuracy or a level of accuracy that is reliable enough to be completely convincing. The root cause of errors in image classification results and low reliability of classification results is that there are uncertainties in every link of remote sensing image classification, and these uncertainties will continue to propagate and accumulate during the classification process, which will eventually affect the accuracy of classification results. precision and...

AI Technical Summary

Problems solved by technology

It only focuses on the uncertainty of the classification results, but ignores the uncertainty in the image classification process. The uncertainty in the classification process is the source of the uncertainty in the classification results. Therefore, the uncertainty of the existing methods The improvement effect of quantitative results on classification results is not obvious

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