Thompson John R J
Department of Computer Science, Mathematics, Physics and Statistics, University of British Columbia, Kelowna, British Columbia, Canada.
J Appl Stat. 2024 May 21;51(16):3431-3455. doi: 10.1080/02664763.2024.2352759. eCollection 2024.
Understanding wildfire spread in Canada is critical to promoting forest health and protecting human life and infrastructure. Quantifying fire spread from noisy images, where change-point boundaries separate regions of fire, is critical to accurately estimating fire spread rates. The challenge lies in denoising the fire images and accurately identifying highly non-linear fire lines without smoothing over boundaries. In this paper, we develop an iterative smoothing algorithm for change-point data that utilizes oversmoothed estimates of the underlying data generating process to inform re-smoothing. We demonstrate its effectiveness on simulated one- and two-dimensional change-point data, and robustness to response outliers. Then, we apply the methodology to fire spread images from laboratory micro-fire experiments and show that the regions fuel, burning and burnt-out are smoothed while boundaries are preserved.
了解加拿大野火的蔓延情况对于促进森林健康以及保护人类生命和基础设施至关重要。从有噪声的图像中量化火灾蔓延情况（其中变化点边界分隔着火区域）对于准确估计火灾蔓延速度至关重要。挑战在于对火灾图像进行去噪，并准确识别高度非线性的火线，同时不模糊边界。在本文中，我们为变化点数据开发了一种迭代平滑算法，该算法利用基础数据生成过程的过度平滑估计来指导重新平滑。我们在模拟的一维和二维变化点数据上证明了其有效性，以及对响应异常值的鲁棒性。然后，我们将该方法应用于实验室微火实验的火灾蔓延图像，结果表明燃料、燃烧和燃尽区域得到了平滑处理，同时边界得以保留。
