单景高分辨率遥感影像薄云去除研究
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摘要
随着遥感技术的发展,高分辨率遥感的应用越来越广泛,但却容易受到大气云层的影响。在实际应用中,SPOT、IKONOS、QuickBird等高分辨率遥感影像都存在云层覆盖问题。云噪声不仅影响了遥感影像的解译精度和目标地物判读的准确性,而且对于时间分辨率较低的遥感平台来说,更是增加了获取有效遥感数据的难度,降低了遥感的时效性。因此,有效地减少或去除云层对遥感影像的影响,对提高遥感数据的有效利用有着重要的意义。
本文在研究薄云影像形成模型及相关理论的基础上,对常用的薄云去除方法进行了深入研究,并提出了基于静态小波变换的高分辨率遥感影像的薄云去除方法,同时以单景QuickBird影像为实验对象,取得很好的去云效果。主要研究内容包括：
1,对现有常用的遥感影像薄云去除方法进行了深入地比较与研究,包括同态滤波法、拉普拉斯空域增强法、小波变换系数加权法,并指出了这几种方法的优缺点。
2,探讨了对影像进行小波分解时如何选择小波算法、小波函数、小波分解层数的基本准则,分析了小波系数的特点,为方法的提出提供理论依据。
3,提出了基于静态小波变换的高分辨率遥感影像薄云去除的方法。由于静态小波变换具有时-频局部特性和时移不变特点,故首先利用低频系数建立云区厚度掩膜,然后根据掩膜厚度减小低频系数和增大高频系数,最后对处理后的低频系数和高频系数进行重构得到去云后的图像。
4,引入灰度均值、标准差、熵、平均梯度、相关系数、光谱扭曲度等统计指标,从统计和视觉两个方面对实验结果进行评价。结果表明,本文提出的基于静态小波变换的高分辨率遥感影像薄云去除方法较现有其他方法具有更好的去云效果。
With the development of remote sensing technology, high-resolution remote sensing has been widely used, but vulnerable to the influence of atmospheric clouds. In practical applications, the cloud cover problems also appear in the high-resolution remote sensing images, such as SPOT, IKONOS, QuickBird, etc. Cloud noise not only affects interpretation accuracy of the remote sensing image and reading accuracy of the target features, but also increases the difficulty of obtaining effective remote sensing data and reduces the timeliness of remote sensing when the data comes from temporal resolution lower remote sensing platform. Therefore, it is most significant to effectively reduce or eliminate the influence of cloud on remote sensing image for improving the utilization of remote sensing image.
This thesis presents a thorough study of the commonly used methods for removing thin cloud cover from remote sensing based on the study of cloud formation model and related theories, and proposes a new method for removing thin cloud cover from high-resolution remote sensing image based on static wavelet transform. In the end, it takes an experiment using QuickBird images and gets good results. The work of this thesis mainly embodies aspects as follows:
1. Studies the commonly used methods existing for removing thin cloud cover from remote sensing images, including cloud homomorphic filter method, Laplace airspace enhancement method, and wavelet transform coefficient weighting method, and points out the advantages and disadvantages of these methods.
2. Discusses how to choose the wavelet algorithm, wavelet function, wavelet decomposition level in the wavelet decomposition of image, and analyzes the characteristics of wavelet coefficients, in order to provide theoretical basis for proposing the new method.
3. Proposes a new method for removing thin cloud cover from high-resolution remote sensing image based on static wavelet transform. Due to the time-frequency local characteristic and the constant motion characteristic of static wavelet transform, the method firstly establish cloud area thickness mask using low-frequency coefficients, then decreases low-frequency coefficients and increase high-frequency coefficient according to the thickness of mask, finally reconstructs the low-frequency coefficients and high-frequency coefficients processed to get a cloud removal image.
4. Introduces some statistical indexes, such as gray mean, standard deviation, entropy, average gradient, correlation coefficient, spectral distortion degree, to evaluate the results of experiments from statistical and visual two aspects. The results show that the method proposed gets better effect than other methods.
本文在研究薄云影像形成模型及相关理论的基础上,对常用的薄云去除方法进行了深入研究,并提出了基于静态小波变换的高分辨率遥感影像的薄云去除方法,同时以单景QuickBird影像为实验对象,取得很好的去云效果。主要研究内容包括：
1,对现有常用的遥感影像薄云去除方法进行了深入地比较与研究,包括同态滤波法、拉普拉斯空域增强法、小波变换系数加权法,并指出了这几种方法的优缺点。
2,探讨了对影像进行小波分解时如何选择小波算法、小波函数、小波分解层数的基本准则,分析了小波系数的特点,为方法的提出提供理论依据。
3,提出了基于静态小波变换的高分辨率遥感影像薄云去除的方法。由于静态小波变换具有时-频局部特性和时移不变特点,故首先利用低频系数建立云区厚度掩膜,然后根据掩膜厚度减小低频系数和增大高频系数,最后对处理后的低频系数和高频系数进行重构得到去云后的图像。
4,引入灰度均值、标准差、熵、平均梯度、相关系数、光谱扭曲度等统计指标,从统计和视觉两个方面对实验结果进行评价。结果表明,本文提出的基于静态小波变换的高分辨率遥感影像薄云去除方法较现有其他方法具有更好的去云效果。
With the development of remote sensing technology, high-resolution remote sensing has been widely used, but vulnerable to the influence of atmospheric clouds. In practical applications, the cloud cover problems also appear in the high-resolution remote sensing images, such as SPOT, IKONOS, QuickBird, etc. Cloud noise not only affects interpretation accuracy of the remote sensing image and reading accuracy of the target features, but also increases the difficulty of obtaining effective remote sensing data and reduces the timeliness of remote sensing when the data comes from temporal resolution lower remote sensing platform. Therefore, it is most significant to effectively reduce or eliminate the influence of cloud on remote sensing image for improving the utilization of remote sensing image.
This thesis presents a thorough study of the commonly used methods for removing thin cloud cover from remote sensing based on the study of cloud formation model and related theories, and proposes a new method for removing thin cloud cover from high-resolution remote sensing image based on static wavelet transform. In the end, it takes an experiment using QuickBird images and gets good results. The work of this thesis mainly embodies aspects as follows:
1. Studies the commonly used methods existing for removing thin cloud cover from remote sensing images, including cloud homomorphic filter method, Laplace airspace enhancement method, and wavelet transform coefficient weighting method, and points out the advantages and disadvantages of these methods.
2. Discusses how to choose the wavelet algorithm, wavelet function, wavelet decomposition level in the wavelet decomposition of image, and analyzes the characteristics of wavelet coefficients, in order to provide theoretical basis for proposing the new method.
3. Proposes a new method for removing thin cloud cover from high-resolution remote sensing image based on static wavelet transform. Due to the time-frequency local characteristic and the constant motion characteristic of static wavelet transform, the method firstly establish cloud area thickness mask using low-frequency coefficients, then decreases low-frequency coefficients and increase high-frequency coefficient according to the thickness of mask, finally reconstructs the low-frequency coefficients and high-frequency coefficients processed to get a cloud removal image.
4. Introduces some statistical indexes, such as gray mean, standard deviation, entropy, average gradient, correlation coefficient, spectral distortion degree, to evaluate the results of experiments from statistical and visual two aspects. The results show that the method proposed gets better effect than other methods.
引文
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[17]吴为禄.遥感图像中的云层消除处理[J].铁路航测,2003,(1)：6～7
[18]刘星,胡光道.高光谱遥感图像频域自适应同态滤波薄云去除[J].物探化探 计算技术,2008,30(2)：163～167
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[20]李刚.卫星遥感图像薄云去除技术研究[硕士学位论文],成都：成都理工大学,2007
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[23]江兴方,潘国卫,陶纯堪.基于样条曲线的一种遥感图像去云方法[J].激光技术,2007,31(6)：581～583
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