• Developed a hybrid optimization method to detect birds in cave.
  • Combined Taguchi's and spatial objective function for cave bird detection.
  • Proposed a new semi-automatic counting of birds in cave with greater efficiency.


This paper proposes an optimized Taguchi-objective function segmentation-based image analysis to detect bird nests in a cave from high resolution terrestrial laser scanning intensity images. First, the Taguchi orthogonal array was used to design 25 experiments with three segmentation parameters: scale, shape, and compactness, each having five variable factor levels. Then, a plateau objective function was computed for each experiment using their respective level combinations. A merger of the factor level combination in the orthogonal array and the computed plateau objective function values was used to generate main effects and interaction plots for signal-to-noise ratios, which provided a measure of robustness for scale, shape, and compactness factors. The optimized parameters were used in the segmentation process in eCognition. The image object was then classified into nest and cave-wall on the basis of laser return intensity and area index using knowledge-based rule sets, and the detection accuracy was evaluated. The result produced area under ROC curve of 0.93 with P2value of 5.10% at 95% confidence interval, respectively. This shows that the method is consistent with non-significant difference among the trials.



Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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