信息熵-低通滤波联合掩模云层干扰去除方法

李静; 李颖

doi:10.37188/CO.2024-0067

信息熵-低通滤波联合掩模云层干扰去除方法

doi: 10.37188/CO.2024-0067

李静^,,
李颖

北京跟踪与通信技术研究所, 北京 100094

详细信息

作者简介:
李　静（1977—），女，山东荣成人，博士，助理研究员，2008年于海军航空工程大学获得博士学位，现为北京跟踪与通信技术研究所助理研究员，主要从事航天工程总体研究。E-mail：zuobin97117@163.com

李　颖（1977—），女，河北唐山人，博士，副研究员，主要从事通信与信息系统、电磁兼容、光学系统等方面的研究。E-mail：sunshinegirlly@163.com

中图分类号: TP394.1;TH691.9
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出版历程
- 收稿日期: 2024-04-10
- 修回日期: 2024-05-27
- 网络出版日期: 2024-08-10

Cloud interference removal using information entropy-low-pass-filtering combined mask

LI Jing^,,
LI Ying

Beijing Institute of Tracking and Telecommunications Technology, Beijing 100094, China

More Information

Corresponding author: zuobin97117@163.com

摘要

摘要:
为了降低海洋光学遥感图像中云区对海面表面纹理分析的影响，本文开展了信息熵-低通滤波融合掩模的云层干扰去除研究。首先，分析了现有遥感图像去云算法的基本原理及其局限性。在此基础上，提出一种基于信息熵-低通滤波联合掩模的云层干扰去除方法。其中包括对遥感图像的改进矩匹配的去条带预处理、局部信息熵滤波，以及联合低频滤波作为遥感图像中各像元的校正参数。该算法具有复杂度低、处理速度快的特点。实验结果表明：与现有算法相比，本文提出的算法可在低计算复杂度的前提下，大幅增强各区域的纹理细节信息，其平均信息熵可达到7.8以上，对比度可达到60以上，平均梯度可达到200以上；图像细节方面，本文算法能够在不引入伪边缘、非均匀性的前提下，充分展现受云区影响的海表纹理细节，进一步满足高保真度的遥感应用需求。
- 云层检测 /
- 干扰去除 /
- 信息熵滤波 /
- 低通滤波
Abstract:
To mitigate the impact of clouds on sea surface texture analysis in marine remote sensing images, this paper studies the removal of cloud interference using an information entropy-low-pass-filter combined mask. Initially, we analyze the fundamental principles and limitations of the existing remote sensing image declouding algorithms, highlighting their unsuitability for applications requiring high fidelity. Subsequently, we propose a cloud interference removal technology based on information entropy-low-pass filtering combined mask. This technology encompasses destriping procedures with improved moment matching for remote sensing images, local information entropy filtering, and joint low-frequency filtering as correction parameters for each pixel in the images. The algorithm is characterized by low complexity and high time efficiency. Experimental results demonstrate that, compared to existing algorithms, the proposed method significantly enhances texture detail information in thin cloud areas and cloud edges while maintaining low computational complexity. It achieves an image information entropy over 7.8, a contrast ratio exceeding 60, and a mean gradient above 200. In comparisons of image details, the proposed algorithm enhances texture details without introducing artifacts or non-uniformities, thereby meeting the high-fidelity requirements for remote sensing applications.
- cloud detection /
- interference removal /
- information-entropy filter /
- jointed mask

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图 1 两幅原始推扫海洋遥感图像

Figure 1. Original push-broom maritine remote sensing images

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图 2 经矩匹配去条带噪声后的图像

Figure 2. Images after stripe noise removal by moment matching

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图 3 经改进的矩匹配去条带噪声后的图像

Figure 3. Images after stripe noise removal by improved moment matching

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图 4 信息熵滤波图

Figure 4. Informational entropy filtering image

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图 5 信息熵值为零的指示图

Figure 5. Indicator map for zero local entropy

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图 6 本文算法处理效果

Figure 6. Processed by the proposed algorithms

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图 7 原始图像（部分）及各算法处理后图像。从上到下分别是 4 幅不同图像；从左到右分别是原图、基于暗通道先验去雾、局部直方图均衡、低频特征消减、本文算法结果。黄框表示框取示例，其中 A、B、D 为近云区；C、F、G 为非云区；E、H 为薄云区

Figure 7. Comparison between original images (Part) and corresponding images processed results obtained by four algorithms. From top to bottom are 4 different images, from left to right: original images, and processed by dark channel prior, local histogram equalization, low-frequency diminishing amd proposed algorithm. Yellow Squares are examples of selected areas for evaluations. A,B,D are near cloud areas; C,F,G are non-cloud areas, E,H are thin-cloud areas.

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图 8 局部区域的细节比较

Figure 8. Local area details comparison

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表 1 各类算法在局部区域中的信息熵比较

Table 1. Comparison of image entropies in local area of images processed by different algorithms

	薄云区	近云区	非云区	平均值
原图	5.202	3.191	3.391	3.928
基于暗通道先验算法	6.282	2.835	4.296	4.471
局部直方图均衡算法	7.350	2.967	5.016	5.111
低频特征消减算法	4.175	2.976	2.936	3.363
本文算法	7.847	7.976	7.826	7.883

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表 2 各类算法在局部区域中的对比度比较

Table 2. Comparison of image contrasts in local area of images processed by different algorithms

	薄云区	近云区	非云区	平均值
原图	12.088	2.226	2.582	5.632
基于暗通道先验算法	55.377	8.322	5.479	23.059
局部直方图均衡算法	55.402	19.079	9.107	27.863
低频特征消减算法	104.881	66.954	54.744	75.526
本文算法	60.306	72.438	58.656	63.800

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表 3 各类算法在局部区域中的平均梯度比较

Table 3. Comparison of image mean gradients in local area of images processed by different algorithms

	薄云区	近云区	非云区	平均值
原图	6.678	5.970	4.612	5.753
基于暗通道先验算法	24.486	31.244	10.053	21.928
局部直方图均衡算法	72.733	76.943	21.358	57.011
低频特征消减算法	94.857	269.980	189.559	184.799
本文算法	74.298	315.702	259.858	216.619

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