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A SVM-based change detection method from bi-temporal remote sensing images in forest area

A SVM-based change detection method from bi-temporal remote sensing images in forest area. Source ： International Workshop on Knowledge Discovery and Data Mining 2008, Jan. 2008, pp. 209-212 Author ： Dengkui Mo, Hui Lin, Jiping Li, Hua Sun, Zhuo Zhang, and Yujiu Xiong

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A SVM-based change detection method from bi-temporal remote sensing images in forest area

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  1. A SVM-based change detection method from bi-temporal remote sensing images in forest area Source：International Workshop on Knowledge Discovery and Data Mining 2008, Jan. 2008, pp. 209-212 Author：Dengkui Mo, Hui Lin, Jiping Li, Hua Sun, Zhuo Zhang, and Yujiu Xiong Reporter：Ching-Chih Cheng

  2. Outline • Introduction • Purposed scheme • Experimental result • Conclusions

  3. Introduction (1/3) • SVM (Support Vector Machines)

  4. Introduction (2/3) • kernel function

  5. Introduction (3/3) • choice of the kernel function

  6. Purposed scheme (1/2) • change detection method • change class considered positive • no change class considered negative gray level of pixels

  7. Purposed scheme (2/2) • extracting change information from forest area • NDVI (Normalized Difference Vegetation Index) • for water and vegetation • besides visible bands (RED, BLUE, GREEN) • NIR • Near-infrared • RED • Red reflectance

  8. Experimental result (1/2) • Pingjiang county,northeast Hunan province, China 1993.10.12 2001.09.24

  9. Experimental result (2/2) Kappa coefficient : 0~1

  10. Conclusions • SVM-based change detection method proposed • very efficient to identify forest land cover changes • detection accuracy higher than 95% • kappa coefficient higher than 0.89

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