Abstract:

The automatically classifying and identifying the TM remote sensing images of forest plays an important role in the monitoring and management of forest resources. In order to improve the performance of BP artificial neural network(BP ANN), many measures, such as standardizing input vectors, increasing verifying set volume, promoting training study algorithm, expanding layers of input-output and main factor analysis, were applied in the TM image data processing. Taking Wangqing Forestry Bureau of Jilin Province as the example study area, the authors studied the automatically classifying and identifying the TM remote sensing images of forest mixed with conifer and broadleaves using the improved BP ANN. The results show that accuracy of automatically classification and identification has been increased significantly, 19.14% higher than that of the traditional ANN method and 8.55% higher than that of traditional unsupervised classifying method respectively. The research also indicates that the classification and identification accuracy rate can be increased further with expanding the BP ANN network volume.

Key words: BP} artificial neural network(BP ANN), mixed forest, TM images, automatically classifying, geographic information