The Research on Forest Resources Change Detection Based on C5.0 Algorithm and Neighborhood Correlation Image Analysis

Abstract:

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.

• Periodical: Key Engineering Materials (Volume 500)
• Pages: 701-708
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.500.701
• Online since: January 2012
• Authors: Zhi Hui Wang, Xi Min Cui, De Bao Yuan, Huan Liu, Jia Feng Wang
• Keywords: C5.0, Change Detection, Decision Tree, Neighborhood Correlation Analysis

References

[1] Jungho Im , John R. Jensen, A change detection model based on neighborhood correlation image analysis and decision tree classification[J], Remote Sensing of Environment 99 (2005) 326 – 340.

DOI: 10.1016/j.rse.2005.09.008

[2] R.R. Okhandiar, P.L.N. Raju, W. Bijker, NEIGHBORHOOD CORRELATION IMAGE ANALYSIS TECHNIQUE FOR CHANGE DETECTION IN FOREST LANDSCAPE, ISPRS Proceeding, 2008, Beijing.

[3] Jungho Im, Neighborhood Correlation Image Analysis for Change Detection Using Different Spatial Resolution Imagery[J], Korean Journal of Remote Sensing, 2006, 22(5)： 337-350.

[4] Muchoney D, Borak J, Borak H C, et al. Application of the MODIS Global Supervised Classification to Vegetation and Land CoverMapping of Central America[J]. International Journal of Remote Sensing, 2000, 21: 1115-1138.

DOI: 10.1080/014311600210100

[5] Joy S M, Reich R M, Reynolds R T. A Non-parametric Supervised Classification of Vegetation Types on the Kaibab National Forest using decision trees[J]. International Journal of Remote Sensing, 2003, 24(9)：1835-1852.

DOI: 10.1080/01431160210154948

[6] HAN Tao, XU Xiaotao, XIE Yaowen. Automated Construction and Classification of Decision Tree Classifier Based on Single-Temporal MODIS Data[J]. Geomatics and Information Science of Wuhan University, 2008, 33(2): 116-119.

[7] LI Mingshi, PENG Shikui, ZHOU Lin, MA Yixiu. A STUDY OF AUTOMATED CONSTRUCTION AND CLASSIFICATION OF DECISION TREE CLASSIFIERS BASED ON ASTER REMOTELY SENSED DATASETS[J]. Remote Sensing for Land & Resources, 2006 (3): 33-42.

[8] WEN Xing-ping, HU Guang-dao, YANG Xiao-feng. Extracting Information from ETM+Image Using C5. 0Decision Tree Algorithm[J]. Geography and Geo-Information Science, 2007, 23(6): 26-29.