Shadow detection in very high spatial resolution aerial images: A comparative study
Shadow detection in very high spatial resolution aerial images: A comparative study
复制标题
DOI：
10.1016/j.isprsjprs.2013.02.003
复制
发表时间：
2013-06-01
影响因子：
12.7
通讯作者：
Paparoditis, N.
中科院分区：
文献类型：
--
作者：
Adeline, K. R. M.;Chen, M.;Paparoditis, N.
自动阴影检测是遥感应用中非常重要的预处理步骤，特别是对于高空间分辨率的遥感图像。在复杂的城市环境中，阴影可能占据图像的很大一部分。忽略这些区域将导致各种应用中的错误，例如大气校正和分类。为了更好地理解阴影的辐射影响，通过模拟合成的城市峡谷场景进行了物理研究。它的结果有助于从物理的角度解释文献中关于阴影的最常见的假设。在此基础上，对阴影检测的最新方法进行了调查，并将其分为六类：直方图阈值，不变颜色模型，对象分割，几何方法，基于物理的方法，无监督和有监督的机器学习方法。其中，选择了一些方法，并在具有高空间分辨率的多光谱和高光谱机载图像的大数据集上进行了测试。所选数据集包含大量典型的西方城市场景。基于精确的参考荫罩比较结果。在这些实验中，RGB和NIR通道上的直方图阈值处理表现最好，平均准确率为92.5%，其次是基于物理的方法，例如Richter方法，平均准确率为90.0%。最后，本文分析和讨论了这些算法的局限性，得出了一些建议的阴影检测。(C)2013年国际摄影测量与遥感学会（ISRS）由Elsevier B.V.发布保留所有权利。
Automatic shadow detection is a very important pre-processing step for many remote sensing applications, particularly for images acquired with high spatial resolution. In complex urban environments, shadows may occupy a significant portion of the image. Ignoring these regions would lead to errors in various applications, such as atmospheric correction and classification. To better understand the radiative impact of shadows, a physical study was conducted through the simulation of a synthetic urban canyon scene. Its results helped to explain the most common assumptions made on shadows from a physical point of view in the literature. With this understanding, state-of-the-art methods on shadow detection were surveyed and categorized into six classes: histogram thresholding, invariant color models, object segmentation, geometrical methods, physics-based methods, unsupervised and supervised machine learning methods. Among them, some methods were selected and tested on a large dataset of multispectral and hyperspectral airborne images with high spatial resolution. The dataset chosen contains a large variety of typical occidental urban scenes. The results were compared based on accurate reference shadow masks. In these experiments, histogram thresholding on RGB and NIR channels performed the best with an average accuracy of 92.5%, followed by physics-based methods, such as Richter's method with 90.0%. Finally, this paper analyzes and discusses the limits of these algorithms, concluding with some recommendations for shadow detection. (C) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.