The French National Centre for Scientific Research (CNRS), Lille University, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France.
Chambre d'Agriculture de la Somme, F-80090 Amiens, France.
Sensors (Basel). 2021 May 21;21(11):3601. doi: 10.3390/s21113601.
To reduce the amount of herbicides used to eradicate weeds and ensure crop yields, precision spraying can effectively detect and locate weeds in the field thanks to imaging systems. Because weeds are visually similar to crops, color information is not sufficient for effectively detecting them. Multispectral cameras provide radiance images with a high spectral resolution, thus the ability to investigate vegetated surfaces in several narrow spectral bands. Spectral reflectance has to be estimated in order to make weed detection robust against illumination variation. However, this is a challenge when the image is assembled from successive frames that are acquired under varying illumination conditions. In this study, we present an original image formation model that considers illumination variation during radiance image acquisition with a linescan camera. From this model, we deduce a new reflectance estimation method that takes illumination at the frame level into account. We experimentally show that our method is more robust against illumination variation than state-of-the-art methods. We also show that the reflectance features based on our method are more discriminant for outdoor weed detection and identification.
为了减少除草剂的使用量以根除杂草并确保作物产量，可以使用成像系统的精准喷雾技术有效检测和定位田间的杂草。由于杂草在视觉上与作物相似，因此颜色信息不足以有效检测它们。多光谱相机提供具有高光谱分辨率的辐亮度图像，从而能够在几个狭窄的光谱波段中研究植被表面。为了使杂草检测不受光照变化的影响，必须估计光谱反射率。然而，当图像是由在不同光照条件下获取的连续帧组装而成时，这是一个挑战。在这项研究中，我们提出了一种原始的图像形成模型，该模型考虑了线扫描相机在获取辐亮度图像时的光照变化。从这个模型中，我们推导出了一种新的反射率估计方法，该方法考虑了帧级别的光照。我们通过实验表明，与最先进的方法相比，我们的方法对光照变化更具有鲁棒性。我们还表明，基于我们的方法的反射率特征对于户外杂草检测和识别更具判别力。
