Image classification based on multi-dimensions texture features during monitoring mining subsidence

To improve the accuracy of monitoring mining subsidence by remote sensing image,the image classifi-cation based on multi-dimensions texture features was proposed.In this classification process,the multi-dimen-sions texture features including local square difference,local average grades,local energy and local information entropy were extracted,and then along with spectrum were used to compose eigenvector in the artificial immune algorithm.Through the selection operator,clone operator and mutation operator,the global optimum cluster center was obtained,so the accuracy of image classification was improved.This method was applied to monitor mining subsidence in Huainan based on TM image classification.The results show that this method is superior to the Par-allelepiped and Maximum likelihood methods,and its overall accuracy and Kappa coefficient reaches to 88.26% and 0.853 respectively.