多源遥感影像自动配准与镶嵌的方法研究
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摘要
多源遥感影像可以从不同的电磁波段、不同的时相、不同的成像机理、不同的空间分辨率等方面提供关于地物不同方面的信息，这些信息可以相互补充。近些年来，多源遥感影像被越来越广泛的应用到许多领域。在许多应用中，遥感影像之间高精度的配准和镶嵌是必须的。目前，多源遥感影像的自动配准技术越来越受到人们的关注，成为国内外研究的热点。
本文对多源遥感影像自动配准和镶嵌技术进行了系统的研究。首先，从多源光谱遥感影像应用的业务化要求出发，本文提出并实现了一套基于匹配技术的多源卫星遥感影像快速自动配准的业务化处理流程。主要包括特征点提取、基于特征点引导的小波金字塔影像逐层交叉匹配、误差消除以及分片纠正等步骤。大量实验结果表明，本文中提出的方案高效、稳定，能够满足多源光谱遥感影像应用业务化运行的需要。
在此基础上，研究了SAR影像与光谱影像之间的自动配准算法，提出了一种基于区域特征匹配的影像配准方法。首先利用改进的滤波器对SAR影像相干斑噪声进行抑制，然后采用基于边缘的区域生长法提取出封闭区域的边界作为特征，与从可见光影像中提取出的边界特征利用代价函数进行交叉匹配，采用配对区域的重心作为配准控制点。利用ENVISAT影像与TM影像进行试验，结果表明，该方法可以实现完全自动配准，配准精度较高。
在上述工作的基础上，本文研究了基于配准的遥感影像镶嵌方法。基于上述的影像配准，自动的确定镶嵌实施方案，然后采用方差均值、直方图匹配和加权平滑对重叠区进行灰度镶嵌，以达到重叠区灰度平滑过渡，提高了镶嵌影像的效果。
本文的主要成果如下：
1．基于Harris特征点提取算子提出并实现了一种在遥感影像上自适应提取大量均匀分布特征点地算法；
2．针对手工选点进行遥感影像配准的速度慢、精度低和强度大等缺点，提出了基于图像灰度和特征结合、小波金字塔相关匹配的方法自动提取配准控制点的方法；
3．针对遥感影像存在局部的高频畸变情况，采用区域畸变模型，通过构建TIN实现了影像的精纠正；
4．研究了常用的SAR影像滤波方法，并进行了改进；
5．利用基于边缘的区域生长法提取出封闭区域的边界作为特征，实现了SAR影像与多光谱影像的自动配准；
6．基于上述的研究，编程实现了多源光谱遥感影像自动配准和镶嵌实用系统以及文中提到的其他算法。
Multi-source remote sensing images from different angles of incidence with different imaging mechanism provide different and complementary information in different space resolution, different electromagnetic wave bands and different time, so they are often employed comprehensively for many applications. The registration and mosaic of multi-source remote sensing images make the results more suitable to further applications. Therefore, the technique of multi-source remote sensing images registration had become a hotspot of research fields recently.
In this paper, the automatic registration and mosaic methods between multi-source remote sensing images were expatiated. To meet the requirement of multi-source spectral image application, an automatic imagery registration algorithm based on image matching was proposed and and implemented firstly in this paper. There are five main steps in the workflow: 1) coarse registration, 2) extracting feature points by modified Harris detector, 3) getting registration control points (RCPs) based on image matching with pyramid image and feature points guiding strategy, 4) expurgating big error pairs by using absolute range detector and polynomial error checker, and 5) image rectifying by using TIN affine transformation. It could be clearly indicated by the results of many experimentations that the algorithm and workflow designed here are precise, prompt, and practical for geometry registration.
Based on the studies above, an automatic registration method between SAR and optical images was discussed, and a new registration method based on region features matching was presented in this paper. First of all, the speckle noises in SAR image was suppressed by some modified filter, and then image segmentation based on region growing limited by the edge was used to extract close regions as invariable features. And meanwhile the close contours in optical image were extracted by edge extraction. Next, the precise feature matching is made by cost function and cross correlation, and then the center points of the matched region were used as RCPs. The images from EnviSat and TM were employed to testify accuracy of the proposed algorithm, and the results showed that the method in this paper could extract RCPs with high accuracy and accomplish the automatic registration for multi-sensor images.
At last, a mosaic method was discussed. Mosaic implementation program was automatically established firstly after image registration and the methods including square error, histogram matching and weighted average were employed to reduce the radiation difference between the images, and desired results were shown by the experimentation.
The paper presented several aspects in the research of remote sensing registration as following.
1. A new auto-adapted feature points extraction method based on Harris operator was proposed in this paper.
2. Towards the faults of slow speed, low precision and high manpower in RCPs manual selection, a mixed auto-selection RCPs algorithm was proposed, which was based on image intensity and feature.
3. Towards the local high-frequency distortions, a local deformation model was employed to rectify the input image accurately.
4. Some common filters were modified by taking into account the advantage of median filter.
5. The contours were extracted automatically based on region growing limited by edges, and the automatic registration between SAR and TM images was achieved.
6. Based on the studies mentioned above, the multi-source spectral image automatism registration and mosaic practical system were developed on the platform C++ Builder 6.
本文对多源遥感影像自动配准和镶嵌技术进行了系统的研究。首先，从多源光谱遥感影像应用的业务化要求出发，本文提出并实现了一套基于匹配技术的多源卫星遥感影像快速自动配准的业务化处理流程。主要包括特征点提取、基于特征点引导的小波金字塔影像逐层交叉匹配、误差消除以及分片纠正等步骤。大量实验结果表明，本文中提出的方案高效、稳定，能够满足多源光谱遥感影像应用业务化运行的需要。
在此基础上，研究了SAR影像与光谱影像之间的自动配准算法，提出了一种基于区域特征匹配的影像配准方法。首先利用改进的滤波器对SAR影像相干斑噪声进行抑制，然后采用基于边缘的区域生长法提取出封闭区域的边界作为特征，与从可见光影像中提取出的边界特征利用代价函数进行交叉匹配，采用配对区域的重心作为配准控制点。利用ENVISAT影像与TM影像进行试验，结果表明，该方法可以实现完全自动配准，配准精度较高。
在上述工作的基础上，本文研究了基于配准的遥感影像镶嵌方法。基于上述的影像配准，自动的确定镶嵌实施方案，然后采用方差均值、直方图匹配和加权平滑对重叠区进行灰度镶嵌，以达到重叠区灰度平滑过渡，提高了镶嵌影像的效果。
本文的主要成果如下：
1．基于Harris特征点提取算子提出并实现了一种在遥感影像上自适应提取大量均匀分布特征点地算法；
2．针对手工选点进行遥感影像配准的速度慢、精度低和强度大等缺点，提出了基于图像灰度和特征结合、小波金字塔相关匹配的方法自动提取配准控制点的方法；
3．针对遥感影像存在局部的高频畸变情况，采用区域畸变模型，通过构建TIN实现了影像的精纠正；
4．研究了常用的SAR影像滤波方法，并进行了改进；
5．利用基于边缘的区域生长法提取出封闭区域的边界作为特征，实现了SAR影像与多光谱影像的自动配准；
6．基于上述的研究，编程实现了多源光谱遥感影像自动配准和镶嵌实用系统以及文中提到的其他算法。
Multi-source remote sensing images from different angles of incidence with different imaging mechanism provide different and complementary information in different space resolution, different electromagnetic wave bands and different time, so they are often employed comprehensively for many applications. The registration and mosaic of multi-source remote sensing images make the results more suitable to further applications. Therefore, the technique of multi-source remote sensing images registration had become a hotspot of research fields recently.
In this paper, the automatic registration and mosaic methods between multi-source remote sensing images were expatiated. To meet the requirement of multi-source spectral image application, an automatic imagery registration algorithm based on image matching was proposed and and implemented firstly in this paper. There are five main steps in the workflow: 1) coarse registration, 2) extracting feature points by modified Harris detector, 3) getting registration control points (RCPs) based on image matching with pyramid image and feature points guiding strategy, 4) expurgating big error pairs by using absolute range detector and polynomial error checker, and 5) image rectifying by using TIN affine transformation. It could be clearly indicated by the results of many experimentations that the algorithm and workflow designed here are precise, prompt, and practical for geometry registration.
Based on the studies above, an automatic registration method between SAR and optical images was discussed, and a new registration method based on region features matching was presented in this paper. First of all, the speckle noises in SAR image was suppressed by some modified filter, and then image segmentation based on region growing limited by the edge was used to extract close regions as invariable features. And meanwhile the close contours in optical image were extracted by edge extraction. Next, the precise feature matching is made by cost function and cross correlation, and then the center points of the matched region were used as RCPs. The images from EnviSat and TM were employed to testify accuracy of the proposed algorithm, and the results showed that the method in this paper could extract RCPs with high accuracy and accomplish the automatic registration for multi-sensor images.
At last, a mosaic method was discussed. Mosaic implementation program was automatically established firstly after image registration and the methods including square error, histogram matching and weighted average were employed to reduce the radiation difference between the images, and desired results were shown by the experimentation.
The paper presented several aspects in the research of remote sensing registration as following.
1. A new auto-adapted feature points extraction method based on Harris operator was proposed in this paper.
2. Towards the faults of slow speed, low precision and high manpower in RCPs manual selection, a mixed auto-selection RCPs algorithm was proposed, which was based on image intensity and feature.
3. Towards the local high-frequency distortions, a local deformation model was employed to rectify the input image accurately.
4. Some common filters were modified by taking into account the advantage of median filter.
5. The contours were extracted automatically based on region growing limited by edges, and the automatic registration between SAR and TM images was achieved.
6. Based on the studies mentioned above, the multi-source spectral image automatism registration and mosaic practical system were developed on the platform C++ Builder 6.
引文
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