杨元维, 王明威, 高贤君, 李熙, 张佳华. 改进Wallis模型的高分辨率遥感影像阴影自动补偿方法[J]. 武汉大学学报 ( 信息科学版), 2021, 46(3): 318-325. DOI: 10.13203/j.whugis20190032
引用本文: 杨元维, 王明威, 高贤君, 李熙, 张佳华. 改进Wallis模型的高分辨率遥感影像阴影自动补偿方法[J]. 武汉大学学报 ( 信息科学版), 2021, 46(3): 318-325. DOI: 10.13203/j.whugis20190032
YANG Yuanwei, WANG Mingwei, GAO Xianjun, LI Xi, ZHANG Jiahua. Automatic Shadow Compensation Based on Improved Wallis Model for High Resolution Remote Sensing Images[J]. Geomatics and Information Science of Wuhan University, 2021, 46(3): 318-325. DOI: 10.13203/j.whugis20190032
Citation: YANG Yuanwei, WANG Mingwei, GAO Xianjun, LI Xi, ZHANG Jiahua. Automatic Shadow Compensation Based on Improved Wallis Model for High Resolution Remote Sensing Images[J]. Geomatics and Information Science of Wuhan University, 2021, 46(3): 318-325. DOI: 10.13203/j.whugis20190032

改进Wallis模型的高分辨率遥感影像阴影自动补偿方法

Automatic Shadow Compensation Based on Improved Wallis Model for High Resolution Remote Sensing Images

  • 摘要: 目前的阴影自动补偿方法仍然存在对比度提升的自适应能力不足等问题。Wallis滤波原理常用于影像色彩匀光,但应用于阴影补偿时会存在影像反差系数对对比度提升效果不理想的问题。首先,通过增加补偿强度系数及拉伸系数,设计了改进的Wallis阴影补偿模型;然后,获取阴影周边的非阴影区域信息作为补偿目标,利用阴影边界在局部范围内寻找同类特征点,自动解算该模型中的补偿系数值,再根据各个阴影区域自身的特点定制相应的补偿模型,对阴影区域内各像素亮度进行合理补偿,恢复被遮挡地物信息;最后,选取多幅具有云阴影及地物阴影的影像进行检测以及阴影补偿的实验,并与局部补偿方法进行对比。实验结果表明,该方法能够有效地补偿阴影,使其亮度和对比度共同提升到与非阴影区域相一致的水平,最佳还原被阴影遮挡的地物信息。

     

    Abstract:
      Objectives  Shadows in high-resolution remote sensing images will cause objects information loss and image quality decline, which is not beneficial for relative applications. Current shadow compensation methods often take advantage of non-shadow information around the shadow area to increase the brightness, whereas there is an unsolved problem that the contrast cannot be enhanced well enough and self-adaptively. Wallis filter principle has been used in image dodging. However, when it is used in shadow compensation, contrast improvement is not as good as other methods, leading to poor compensation results. Therefore, an improved Wallis model compensation method is proposed in this paper to enhance the brightness and contrast better to restore the shaded information.
      Methods  First, by adding compensation strength and stretch parameters, an improved Wallis model is designed. The strength parameter is positive to the brightness and contrast, and the stretch parameter is sensitive to the contrast. Therefore, the improved Wallis model is more efficient to adjust brightness and contrast. Moreover, an automatic parameters calculation strategy is further explored to customize a suitable compensation model for each shadow area. On one hand, the brightness average and deviation of the adjacent non-shadow region are calculated and used as the compensation target values. On the other hand, based on searching the same kinds of points around shadow boundaries, a series of non-shadow and shadow feature points are matched. Assumed the feature value of the non-shadow point is the approximate value of its responding shadow point, they can be used to calculate strength and stretch parameters automatically. Lastly, the brightness of each pixel in different shadow regions will be compensated by customized models to cover the shaded information self-adaptively.
      Results  In this paper, three sets of comparative experiments are set up to analyze and compare the part compensation methods algorithm, original Wallis method in three images with ordinary object shadows and cloud shadows, respectively. The differences in brightness average and gradient average between the compensated value and the non-shadow target value are used to evaluate the compensation quality. The experimental results show that: (1) Original Wallis model is useful to enhance brightness to some extent, while it is insufficient to improve contrast. As a consequence, the visual compensated results are not acceptable as the other two methods. (2) Part compensation method can improve brightness and contrast effectively. However, it cannot adjust self-adaptively according to each shadow regions condition, leading to some over-compensated and some insufficient compensation results in the same image. (3) The proposed method results indicate the best compensation quality in different shadow regions because the improved Wallis model is more pointed to improve contrast and the automatically calculated parameters values are suitable to customize the compensation model for each shadow region.
      Conclusions  Aiming at the contrast improvement of the current algorithm of automatic shadow compensation, an automatic compensation method based on an improved Wallis model is proposed in this paper. The experimental results show that the newly designed model is more effective to enhance brightness and contrast, which is useful for finding suitable parameters values. Combining with the strategy of automatic parameters calculation, its customized model can adaptively compensate each shadow. However, it should be pointed out that there are still some limitations about the shadow border compensation and the internal difference in one shadow area, which need to be further studied.

     

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