Remote Sensing Image Classification of the Improved BP NN

Xiangwei Liu

Abstract


Remote sensing (RS) image classification plays the very important practical role in the geological survey and mineral exploration. The neural network (NN) technology is an important method for the RS image classification. But, the BP NN still has shortcomings, for example, the learning convergence rate is slow and the training process is easy to fall into the partial minimum. By taking the classification of 512×512 pixel experimental area of Mulei County of Xinjiang as the example, the improved BP NN model is designed in this article by the adaptive learning rate and the additional momentum, using the LANDSAT-7 ETM + RS images as the main data source. Through the precision analysis by the error matrix, the result shows that the total classification NNaccuracy which uses improved BP NN classification of RS image is 89.06%, and the Kappa coefficient is 85.53%, and the classification accuracy of RS image is improved obviously.


Full Text: PDF DOI: 10.5539/jsd.v3n4p220

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This work is licensed under a Creative Commons Attribution 3.0 License.

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

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