Department of Geography, Texas A&M University, College Station, Texas 77843-3147, USA.
Opt Lett. 2013 May 15;38(10):1676-8. doi: 10.1364/OL.38.001676.
Shadows in remote-sensor images can yield marked errors in classification of riverine environments. We propose use of a modified shadow-removal algorithm as a preprocessing step for remote-sensing image classification of riverine landscapes. To accommodate characterization of spatially complex river features in the image, we investigate an illumination suppression-based shadow-removal algorithm, modified to include a user-defined tiling approach. We quantitatively evaluate the influence of shadow removal from aerial photography on classification accuracy as such studies are currently lacking. Experimental results demonstrate that this modified shadow-removal method significantly increases classification accuracy and improves detection of small river channels partially obscured by shadow.
遥感图像中的阴影会导致河流环境分类出现显著误差。我们提出使用改进的阴影去除算法作为遥感图像河流景观分类的预处理步骤。为了适应图像中空间复杂河流特征的描述，我们研究了一种基于光照抑制的阴影去除算法，并对其进行了修改，以包含用户定义的平铺方法。由于目前缺乏此类研究，我们定量评估了从航空摄影中去除阴影对分类精度的影响。实验结果表明，这种改进的阴影去除方法显著提高了分类精度，并提高了对部分被阴影遮挡的小河道的检测能力。
