Hard and Soft Classification Method of Multi-Spectral Remote Sensing Image Based on Adaptive Thresholds
HU Tan-gao, XU Jun-feng*, ZHANG Deng-rong, WANG Jie, ZHANG Yu-zhou
Institute of Remote Sensing and Earth Sciences, College of Science, and Hangzhou Normal University, Zhejiang Provincial Key Laboratory of Urban Wetlands and Regional Change, Hangzhou Normal University, Hangzhou 311121, China
Abstract：Hard and soft classification techniques are the conventional methods of image classification for satellite data, but they have their own advantages and drawbacks. In order to obtain accurate classification results, we took advantages of both traditional hard classification methods (HCM) and soft classification models (SCM), and developed a new method called the hard and soft classification model (HSCM) based on adaptive threshold calculation. The authors tested the new method in land cover mapping applications. According to the results of confusion matrix, the overall accuracy of HCM, SCM, and HSCM is 71.06%, 67.86%, and 71.10%, respectively. And the kappa coefficient is 60.03%, 56.12%, and 60.07%, respectively. Therefore, the HSCM is better than HCM and SCM. Experimental results proved that the new method can obviously improve the land cover and land use classification accuracy.
[1] Foody G M. Remote Sensing of Environment, 2002, 80(1): 185. [2] Soria-Ruiz J, Fernandez-Ordonez Y, Woodhouse I H. International Journal of Remote Sensing, 2010, 31(12): 3291. [3] Kavzoglu T, Colkesen I. International Journal of Applied Earth Observation and Geoinformation, 2009, 11(5): 352. [4] Jones D A, Hansen A J, Bly K, et al. Remote Sensing of Environment, 2009, 113(7Sp. Iss. SI): 1346. [5] Das S K, Singh R. IEEE Transactions on Geoscience and Remote Sensing Letters, 2009, 6(3): 453. [6] Wu T F, Lin C J, Weng R C. Journal of Machine Learning Research, 2004, 5: 975. [7] Pal M, Mather P M. International Journal of Remote Sensing, 2005, 26(5): 1007. [8] Gong B L, Im J, Mountrakis G. Remote Sensing of Environment, 2011, 115(2): 600. [9] Tang J, Wang L, Myint S W. International Journal of Remote Sensing, 2007, 28(18): 4047. [10] Foody G M. Remote Sensing of Environment, 2010, 114(10): 2271. [11] Shi W Z, Liu K F, Zhang H. International Journal of Applied Earth Observation and Geoinformation, 2011, 13(1): 89. [12] Silvan-Cardenas J L, Wang L. Remote Sensing of Environment, 2008, 112(3): 1081.
