Several Different Remote Sensing Image Classification Technology Analysis

Xiangwei Liu

Abstract


Remote sensing (RS) image classification is an important content of RS research area. How to solve the classification of the multi-category image, avoid the existence of “same objects with different spectrums” and “different spectrums with same objects” phenomemon, and achieve certain accuracy requirement is a key question in RS application research, which has the very important practical significance especially in the military uffairs scout, geological survey and civil fields. By using the LANDSAT-7 ETM + RS image with the support of the project which investigated geology and mineral resources in the around 1:50 thousand regional Sepikou, Mulei County of Xinjiang in 2008, the improved BP neural network model is designed in this paper by the adaptive learning rate and the additional momentum, and compared with the traditional classification methods. Through the precision analysis by the error matrix, the result shows that the total classification accuracy which uses improved BP NN classification of RS image is 89.8%, and the classification accuracy of RS image is improved obviously, and is a kind of effective classification method.


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Journal of Sustainable Development   ISSN 1913-9063 (Print)   ISSN 1913-9071 (Online)

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