The Ground Objects Identification for Digital Remote Sensing Image Based on the BP Neural Network

Spectral information of ground objects target in remote sensing image is complex, more noise, and highly nonlinear. It makes traditional data processing method no longer significant, effective, and efficient. The BP neural network classification-recognition method provides a more ideal solution. Using the TM remote sensing images as the example, this paper experimented the application of the BP neural network to the remote sensing image classification and recognition. Results showed that the classification precision of cultivated land was very low for both the BP neural network and traditional maximum likelihood methods because the spectrum difference between the new cultivated land and the bare land having low plant covered in this area was not significant. Maximum likelihood method wrongly regarded the bare land which had higher soil moisture content by lakeshore as water body. Except the grassland, the classification effect of the BP neural network was superior to maximum likelihood method. The overall classification accuracy by the BP neural network reached 81.79 %; however, the one by the maximum likelihood method was 79.08 %, indicating that the BP neural network classification and recognition was superior to the traditional maximum likelihood method.