Abstract
Change detection techniques attempt to be used for remote sensing monitoring of invasive plants. A novel change detection method based on direction feature and RFLICM (an improved fuzzy C-means clustering) is proposed. Firstly, the difference image is acquired from multitemporal images. The pixel values of the difference image are updated using the template of directional neighborhood based on direction feature. The final change detection map is achieved by clustering the pixel values of the difference image using RFLICM algorithm into two disjoint classes: changed and unchanged. The results obtained by experiment are compared with some other existing state of the art methods. It is observed that the proposed method outperforms the other methods. Finally, the proposed change detection technique is applied to remote sensing monitoring of invasive plants.
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References
Li, X., Jin, H.: Study on Invasive Plant Species. Journal of Agriculture 3(03), 39–43 (2013)
Wan, H., Wang, C., Ya, L., et al.: Monitoring an invasive plant using hyperspectral remote sensing data. Transactions of the CSAE 26 (suppl. 2), 59–63 (2010)
Pan, W., Chen, J., Li, L., Jiayi, W.: Dynamical Monitoring of Spartina Alterniflora Invasion by Using Remote Sensing Data in Luoyuan Bay, Fujian. Chinese Agricultural Science Bulletin 25(13), 216–219 (2009)
Zhang, Y., Lu, J.: Progress on Monitoring of Two Invasive Species Smooth Cordgrass (Spatina alterniflora) and Water Hyacinth (Eichhornia crassipes) by Remote Sensing. Bulletin of Science and Technology 26(1), 130–137 (2010)
Pan, W., Chen, J., Zhang, C., et al.: Dynamic Monitoring Analysis of Expansion of Spartina alterniflora in Fujian. Chinese Journal of Agrometeorology 32 (supp.1), 174–177 (2011)
Mack, R.N., Von Holle, B., Meyerson, L.A.: Assessing invasive alien species across multiple spatial scales: Working globally and locally. Frontiers in Ecology and the Environment 5(4), 217–220 (2007)
Gong, P.: Remote sensing of environmental change over China: A review. Chinese Science Bulletin 57(16), 1379–1387 (2012)
Xu, R.: A few thoughts about biological invasions. Journal of Environmental Entomology 30(1), 80–82 (2008)
Huang, H., Zhang, L.: Remote sensing analysis of range expansion of spartina alterniflora at Jiuduansha shoals in Shanghai, China. Journal of Plant Ecology 31(1), 75–82 (2007)
Pu, R., Gong, P., Tian, Y., Miao, X., Carruthers, R.I., Anderson, G.L.: Invasive species change detection using artificial neural networks and CASI hyperspectral imagery. Environ. Monit. Assess. 140, 15–32 (2008)
Mishra, N.S., Ghosh, S., Ghosh, A.: Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images. Applied Soft Computing 12(8), 2683–2692 (2012)
Radke, R.J., Andra, S., Al-Kofahi, O., et al.: Image Chang Detection Algorithms: A Systematic Survey. IEEE Trans on Image Process 14(3), 294–307 (2005)
Li, S., Li, Y., An, Y.: Automatic Recognition of Landslides Based on Change Detection. Remote Sensing Information 1, 27–31 (2010)
Jiang, L., Liao, M., Zhang, L., et al.: Change Detection in Multitemporal SAR Images Using MRF Models. Geomatics and Information Science of Wuhan University 31(4), 312–315 (2006)
Zhang, X., Li, H., Jiao, L.: A change detection algorithm based on object feature for SAR image. In: The Second International Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009, Xian, China, pp. 693–696 (2009)
Krinidis, S., Chatzis, V.: A robust fuzzy local information C-means clustering algorithm. IEEE Trans. Image Process. 19(5), 1328–1337 (2010)
Ma, J., Gong, M., Zhou, Z.: Wavelet Fusion on Ratio Images for Change Detection in SAR Images. IEEE Geoscience and Remote Sensing Letters 9(6), 1122–1126 (2012)
Gong, M., Zhou, Z., Ma, J.: Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering. IEEE Trans. Image Process. 21(4), 2141–2151 (2012)
Celik, T.: Multiscale Change Detection in Multitemporal Satellite Images. IEEE Geoscience and Remote Sensing Letters 6(4), 820–824 (2009)
Wu, C., Wu, Y.: Multitemporal images Change Detection using nonsubsampled contourlet Transform and Kernel Fuzzy C-Mean Clustering. In: International Symposium on Intelligence Information Processing and Trusted Computing, TPTC 2011, Wuhan, China, pp. 96–99 (2011)
Xin, F.: Change Detection in Remote Sensing Imagery Based on Fisher Classifier and Computational Intelligence. Xidian University, Xian (2011)
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Li, Q., Qin, X., Jia, Z., Yang, J., Hu, R. (2013). A Novel Change Detection Method Based on Direction Feature and Fuzzy Clustering for Remote Sensing Images and Its Application in Biological Invasions. In: Bian, F., Xie, Y., Cui, X., Zeng, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. Communications in Computer and Information Science, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45025-9_49
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DOI: https://doi.org/10.1007/978-3-642-45025-9_49
Publisher Name: Springer, Berlin, Heidelberg
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