ABSTRACT
We propose computer vision based approach for effectively computing the vegetation coverage of the image to determine the structure of the vegetation and to understand wildlife habitat. To deal with the variation of lighting condition, two-stage segmentation strategy is applied. Firstly, texture information is used to roughly classify the vegetation and the reference blackboard at each position using Support Vector Machine. And then a K-means based adaptive color model is used to refine the segmentation result in pixel level. We evaluate our approach on our dataset, and the results demonstrate that the proposed method is robust to environment changing, and color instability. For blackboard localization, we tested 200 images and the accuracy is approximately 93%. For grass detection and coverage computation, the error rate is approximately 3%.
- Jorgensen, C.F., Stutzman, R.J., Anderson, L.C., E Decker, S., Powell, L.A., Schacht, W.H. and Fontaine, J.J. 2013. Choosing a DIVA: a comparison of emerging digital imagery vegetation analysis techniques. Applied Vegetation Science, 16(4), pp. 552--560.Google ScholarCross Ref
- Limb, R.F., Hickman, K.R., Engle, D.M., Norland, J.E. and Fuhlendorf, S.D. 2007. Digital photography: reduced investigator variation in visual obstruction measurements for southern tallgrass prairie. Rangeland Ecology & Management, 60(5), pp. 548--552.Google ScholarCross Ref
- Leis, S.A. and Morrison, L.W. 2011. Field test of digital photography biomass estimation technique in tallgrass prairie. Rangeland ecology & management, 64(1), pp. 99--103.Google Scholar
- Morrison, L.W. 2015. Observer error in vegetation surveys: a review. Journal of Plant Ecology, 9(4), pp. 367--379.Google ScholarCross Ref
- Robel, R.J., Briggs, J.N., Dayton, A.D. and Hulbert, L.C. 1970. Relationships between visual obstruction measurements and weight of grassland vegetation. Journal of Range Management, pp. 295--297.Google ScholarCross Ref
- Nudds, T.D. 1977. Quantifying the vegetative structure of wildlife cover. Wildlife Society Bulletin, pp. 113--117.Google Scholar
- Cagney, J., Cox, S.E. and Booth, D.T. 2011. Comparison of point intercept and image analysis for monitoring rangeland transects. Rangeland Ecology & Management, 64(3), pp. 309--315.Google ScholarCross Ref
- Booth, D.T., Cox, S.E. and Johnson, D.E. 2005. Detection-threshold calibration and other factors influencing digital measurements of ground cover. Rangeland Ecology & Management, 58(6), pp. 598--604.Google ScholarCross Ref
- Dalal, N. and Triggs, B. 2005, June. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 1, pp. 886--893). IEEE. Google ScholarDigital Library
- He, D.C. and Wang, L. 1990. Texture unit, texture spectrum, and texture analysis. IEEE transactions on Geoscience and Remote Sensing, 28(4), pp. 509--512.Google Scholar
Index Terms
- Digital Image Vegetation Analysis with Machine Learning
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