The Research on Forest Resources Change Detection Based on C5.0 Algorithm and Neighborhood Correlation Image Analysis
With double-temporal Landsat TM and ETM+ datasets, the change information of forest resources of Culai Mountain in Shandong Province was explored. This paper applies decision tree classification based on C5.0 algorithm and neighborhood correlation image analysis to detect forest change information，and compares the three different detection methods：1)C5.0 classifies single-temporal data respectively，and extract change information after comparing classification results；2) create C5.0 train rules through double-temporal raw data，then generate change detection map；3)In addition to double-temporal remote sensing data，neighborhood correlation analysis images are also added as one of the data sources of C5.0，and generate change detection map. The experimental result shows that decision tree classification based on C5.0 algorithm can detect change information effectively，and after adding neighborhood correlation analysis images the classification accuracy of change detection was improved.
Z. H. Wang et al., "The Research on Forest Resources Change Detection Based on C5.0 Algorithm and Neighborhood Correlation Image Analysis", Key Engineering Materials, Vol. 500, pp. 701-708, 2012