BP Neural Network Classification of Remote Sensing Images based on DT\|CWT Decomposition

In order to solve the ambiguity and uncertainty of high resolution multi\|spectral remote sensing image classification and to better overcome the influence of noise，a new BPNN（Back Propagation Neural Network）classification method of multi\|spectral image，based on DT\|CWT decomposition，is presented in this paper.First，the NDVI and texture features of the image are extracted to reduce the classification uncertainty caused by the problem of different objects having the same spectrum and the same objects having different spectrum in the image，then，the original spectral band，NDVI and texture features of the image are decomposed by DT\|CWT to extract the Low\|frequency information of the image，as well as to reduce the image noise and the presence of “salt and pepper” in the classification.Finally，the extracted low\|frequency sub\|graphs are input to the BP neural network and classified according to the trained network to obtain the final classification result.The results of the comparison show that the proposed method with less miscellaneous points has stronger regional consistency，higher classification accuracy and better robustness.