CN107274441A - The wave band calibration method and system of a kind of high spectrum image - Google Patents
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Abstract
本发明提供了一种高光谱图像的波段配准方法，包括：选取某一个波段作为基准波段，生成新的图像作为基准图像；在待配准波段图像上提取特征点，以特征点为中心提取模板窗口；在基准图像上选择与特征点相同的坐标作为中心，提取搜索窗口，使用模板匹配方法得到粗匹配点；建立待配准波段图像和基准图像的粗变换关系，计算出所述特征点在基准图像上的匹配点，以该匹配点为中心提取搜索窗口，使用模板匹配方法得到精匹配点；建立待配准波段图像和基准图像的精变换关系，完成待配准波段图像的校正。采用本发明的技术方案，可以自动完成无人机高光谱图像的波段配准，使无人机高光谱图像所有波段具有空间一致性，处理速度快，配准精度高。
The invention provides a band registration method of a hyperspectral image, comprising: selecting a certain band as a reference band, generating a new image as a reference image; Template window; select the same coordinates as the feature points on the reference image as the center, extract the search window, and use the template matching method to obtain rough matching points; establish a rough transformation relationship between the band image to be registered and the reference image, and calculate the feature points For the matching point on the reference image, extract the search window centered on the matching point, and use the template matching method to obtain the fine matching point; establish the precise transformation relationship between the band image to be registered and the reference image, and complete the correction of the band image to be registered. By adopting the technical scheme of the present invention, the band registration of the hyperspectral image of the UAV can be automatically completed, so that all bands of the hyperspectral image of the UAV have spatial consistency, the processing speed is fast, and the registration accuracy is high.
Description
技术领域technical field
本发明属于高光谱图像处理技术领域，涉及一种无人机高光谱图像波段配准方法和系统。The invention belongs to the technical field of hyperspectral image processing, and relates to a method and system for band registration of hyperspectral images of unmanned aerial vehicles.
背景技术Background technique
无人机具有机动性高、成本低的优点，近年来在小范围大比例尺制图、应急救灾、资源环境调查、精细农业等方面有广泛的应用，成为卫星遥感和传统航空遥感的一个重要补充方式。无人机高光谱遥感跟卫星高光谱遥感相比有其得天独厚的优势，由于无人机的飞行高度低，获取的高光谱图像既有丰富的光谱信息，也有着很高的空间分辨率，空间分辨率可达到厘米级别，可以应用于环境监测、精准农业(作物长势判断、作物估产、病虫害预警)等方面。UAV has the advantages of high mobility and low cost. In recent years, it has been widely used in small-scale and large-scale mapping, emergency relief, resource and environment investigation, precision agriculture, etc., and has become an important supplementary method for satellite remote sensing and traditional aerial remote sensing. . Compared with satellite hyperspectral remote sensing, UAV hyperspectral remote sensing has its unique advantages. Due to the low flying altitude of UAV, the acquired hyperspectral images have both rich spectral information and high spatial resolution. The resolution can reach the centimeter level, and can be applied to environmental monitoring, precision agriculture (judging crop growth, crop yield estimation, early warning of pests and diseases), etc.
由于无人机本身的特点以及搭载在无人机上的高光谱成像仪的成像方式，无人机高光谱图像的处理是一个不小的挑战。搭载在卫星上的传统高光谱成像仪有分光器，波段之间都是对齐的，不需要进行波段之间的配准。无人机特别是旋翼型无人机由于载荷有限，一般为几千克，不能搭载传统的高光谱成像仪。目前无人机上搭载的高光谱成像仪多为框幅式成像仪，采用连续曝光的成像方式，获取多波段的高光谱图像。由于曝光期间无人机在运动，因此同一景图像不同波段之间不是对齐的，需要进行波段之间的配准。Due to the characteristics of the drone itself and the imaging method of the hyperspectral imager mounted on the drone, the processing of the hyperspectral image of the drone is not a small challenge. Traditional hyperspectral imagers mounted on satellites have beam splitters, and the bands are aligned, so there is no need for registration between bands. Due to the limited load of UAVs, especially rotor UAVs, which are generally several kilograms, they cannot be equipped with traditional hyperspectral imagers. At present, most of the hyperspectral imagers mounted on UAVs are frame-type imagers, which adopt continuous exposure imaging to obtain multi-band hyperspectral images. Since the drone is moving during the exposure, the different bands of the same scene image are not aligned, and registration between the bands is required.
发明内容Contents of the invention
有鉴于此，本发明提供了一种高光谱图像的波段配准方法，包括：In view of this, the present invention provides a method for band registration of hyperspectral images, including:
步骤S100，选取某一个波段作为基准波段，然后将该波段的图像上下左右各扩充若干个像素，生成新的图像作为基准图像；Step S100, selecting a certain band as the reference band, and then expanding the image of the band by several pixels up, down, left, and right to generate a new image as the reference image;
步骤S200，在待配准波段图像上提取特征点，以特征点为中心提取每个特征点的模板窗口；Step S200, extracting feature points on the band image to be registered, and extracting a template window of each feature point centered on the feature point;
步骤S300，对每一个特征点，在基准图像上选择与所述特征点相同的坐标作为中心，提取搜索窗口，使用模板匹配方法得到粗匹配点；Step S300, for each feature point, select the same coordinates as the feature point on the reference image as the center, extract a search window, and use a template matching method to obtain a rough matching point;
步骤S400，基于所述粗匹配点建立待配准波段图像和基准图像的粗变换关系，对每一个特征点，根据所述粗变换关系计算出所述特征点在基准图像上的匹配点，以所述匹配点为中心提取搜索窗口，使用模板匹配方法得到精匹配点；Step S400, establish a rough transformation relationship between the band image to be registered and the reference image based on the rough matching points, and for each feature point, calculate the matching point of the feature point on the reference image according to the rough transformation relationship, so as to The matching point is centered to extract a search window, and a template matching method is used to obtain a fine matching point;
步骤S500，基于所述精匹配点建立待配准波段图像和基准图像的精变换关系，然后使用该精变换关系完成待配准波段图像的校正；Step S500, establishing a fine transformation relationship between the band image to be registered and the reference image based on the fine matching points, and then using the fine transformation relationship to complete the correction of the band image to be registered;
步骤S600，重复步骤S200～S500，完成下一波段的配准。In step S600, steps S200-S500 are repeated to complete the registration of the next band.
进一步的，在提取特征点之前，对待配准波段图像进行突出对比度的增强操作。Further, before extracting the feature points, the image of the band to be registered is enhanced to highlight the contrast.
进一步的，步骤S400中所述的粗匹配点为使用随机采样一致性方法和最小二乘法剔除误匹配点以后的粗匹配点，步骤S500中所述的精匹配点为使用随机采样一致性方法和最小二乘法剔除误匹配点以后的精匹配点。Further, the rough matching point described in step S400 is the rough matching point after using the random sampling consistency method and the least squares method to eliminate the wrong matching point, and the fine matching point described in the step S500 is using the random sampling consistency method and The least squares method eliminates the fine matching points after the wrong matching points.
进一步的，每配准一个波段之后，计算该波段相对于基准波段的偏移量，在步骤S300中对下一波段使用特征点的坐标加上偏移量之后的坐标为中心提取搜索窗口。Further, after each band is registered, the offset of the band relative to the reference band is calculated, and in step S300, the coordinates of the feature points plus the offset are used as the center to extract the search window for the next band.
进一步的，在步骤S200中，使用或SIFT或Harris或SURF作为提取特征点的算子。Further, in step S200, use Or SIFT or Harris or SURF as the operator for extracting feature points.
本发明还提供一种高光谱图像的波段配准系统，包括：The present invention also provides a band registration system for hyperspectral images, including:
基准图像生成模块，用于选取某一个波段作为基准波段，然后将该波段的图像上下左右各扩充若干个像素，生成新的图像作为基准图像；The reference image generation module is used to select a certain band as the reference band, and then expand the image of the band by several pixels up, down, left, and right to generate a new image as the reference image;
模板窗口提取模块，用于在待配准波段图像上提取特征点，以特征点为中心提取每个特征点的模板窗口；The template window extraction module is used to extract feature points on the band image to be registered, and extract the template window of each feature point centered on the feature points;
粗匹配模块，用于对每一个特征点，在基准图像上选择与所述特征点相同的坐标作为中心，提取搜索窗口，使用模板匹配方法得到粗匹配点；A rough matching module, for each feature point, select the same coordinate as the feature point on the reference image as the center, extract the search window, and use the template matching method to obtain the rough matching point;
精匹配模块，用于基于所述粗匹配点建立待配准波段图像和基准图像的粗变换关系，对每一个特征点，根据所述粗变换关系计算出所述特征点在基准图像上的匹配点，以所述匹配点为中心提取搜索窗口，使用模板匹配方法得到精匹配点The fine matching module is used to establish a rough transformation relationship between the band image to be registered and the reference image based on the rough matching point, and for each feature point, calculate the matching of the feature point on the reference image according to the rough transformation relationship point, extract the search window centered on the matching point, and use the template matching method to obtain the exact matching point
几何校正模块，用于基于所述精匹配点建立待配准波段图像和基准图像的精变换关系，然后使用该精变换关系完成待配准波段图像的校正；A geometric correction module, configured to establish a fine transformation relationship between the band image to be registered and the reference image based on the fine matching points, and then use the fine transformation relationship to complete the correction of the band image to be registered;
进一步的，上述波段配准系统还包括图像增强模块，用于在提取特征点之前，对待配准波段图像进行突出对比度的增强操作。Further, the above-mentioned band registration system further includes an image enhancement module, which is used to perform an enhancement operation of highlighting contrast on the image of the band to be registered before extracting the feature points.
进一步的，上述波段配准系统还包括误匹配点剔除模块，用于使用随机采样一致性方法和最小二乘法剔除误匹配点。Further, the above-mentioned band registration system also includes a mismatch point elimination module, which is used to eliminate mismatch points by using the random sampling consistency method and the least square method.
进一步的，上述波段配准系统还包括偏移量计算模块，用于在配准一个波段之后，计算该波段相对于基准波段的偏移量。Further, the above-mentioned band registration system further includes an offset calculation module, configured to calculate the offset of a band relative to the reference band after the band is registered.
采用上述技术方案，可以自动完成无人机高光谱图像的波段配准，使无人机高光谱图像所有波段具有空间一致性，处理速度快，配准精度高。By adopting the above technical solution, the band registration of the hyperspectral image of the UAV can be automatically completed, so that all bands of the hyperspectral image of the UAV have spatial consistency, the processing speed is fast, and the registration accuracy is high.
上述概述仅仅是为了说明书的目的，并不意图以任何方式进行限制。除上述描述的示意性的方面、实施方式和特征之外，通过参考附图和以下的详细描述，本发明进一步的方面、实施方式和特征将会是容易明白的。The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the drawings and the following detailed description.
附图说明Description of drawings
在附图中，除非另外规定，否则贯穿多个附图相同的附图标记表示相同或相似的部件或元素。这些附图不一定是按照比例绘制的。应该理解，这些附图仅描绘了根据本发明公开的一些实施方式，而不应将其视为是对本发明范围的限制。In the drawings, unless otherwise specified, the same reference numerals designate the same or similar parts or elements throughout the several drawings. The drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in accordance with the present invention and should not be taken as limiting the scope of the present invention.
图1为本发明实施例的高光谱图像的波段配准方法流程图。FIG. 1 is a flow chart of a method for band registration of a hyperspectral image according to an embodiment of the present invention.
图2为本发明实施例的高光谱图像的波段配准系统结构图。FIG. 2 is a structural diagram of a band registration system for a hyperspectral image according to an embodiment of the present invention.
具体实施方式detailed description
在下文中，仅简单地描述了某些示例性实施例。正如本领域技术人员可认识到的那样，在不脱离本发明的精神或范围的情况下，可通过各种不同方式修改所描述的实施例。因此，附图和描述被认为本质上是示例性的而非限制性的。In the following, only some exemplary embodiments are briefly described. As those skilled in the art would realize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. Accordingly, the drawings and descriptions are to be regarded as illustrative in nature and not restrictive.
如图1所示，本实施例的高光谱图像的波段配准方法，包括以下步骤：As shown in Figure 1, the band registration method of the hyperspectral image of the present embodiment includes the following steps:
步骤S100，选取某一个波段作为基准波段，然后将该波段的图像上下左右各扩充M个像素，生成新的图像作为基准图像。In step S100, a certain band is selected as a reference band, and then the image of the band is expanded by M pixels up, down, left, and right respectively to generate a new image as the reference image.
波段配准是以高光谱图像的某一个波段为基准，进行配准。因此，在配准前需要先选择合适的波段作为基准波段，选取的原则是其它波段和该波段的配准成功率和准确率更高，选取方法可以通过各个波谱范围分析、实际进行配准实验等确定，此为公知常识，不再赘述。Band registration is based on a certain band of the hyperspectral image for registration. Therefore, before registration, it is necessary to select a suitable band as the reference band. The principle of selection is that other bands and this band have a higher registration success rate and accuracy. The selection method can be analyzed by each spectral range and the actual registration experiment Wait for confirmation, this is common knowledge and will not be repeated.
原始高光谱图像在每个波段的大小一样，根据实际应用需要，配准后，每个波段的图像大小也必须一样。但是，由于成像时间不一致，导致基准波段与其它波段的成像范围不一致，如果直接配准，并保证配准后图像大小不变，会导致配准后的图像丢失部分数据，因此，选择好基准波段以后，要将基准波段的图像上下左右各扩充M个像素，形成新的图像作为基准图像。M的取值通过其它波段和基准波段之间的最大偏移量来确定，M取值大于最大偏移量。The size of each band of the original hyperspectral image is the same, according to the actual application needs, after registration, the size of the image of each band must also be the same. However, due to the inconsistency of the imaging time, the imaging ranges of the reference band and other bands are inconsistent. If you directly register and ensure that the image size remains unchanged after registration, some data will be lost in the registered image. Therefore, choose a good reference band In the future, the image of the reference band will be expanded by M pixels up, down, left, and right respectively to form a new image as the reference image. The value of M is determined by the maximum offset between other bands and the reference band, and the value of M is greater than the maximum offset.
步骤S200，在待配准波段图像上提取特征点，以特征点为中心提取每个特征点的模板窗口。Step S200, extracting feature points on the band image to be registered, and extracting a template window of each feature point centered on the feature point.
在提取特征点之前，要对待配准波段图像进行增强操作，突出对比度，使提取的特征点数量更多。Before extracting the feature points, it is necessary to enhance the image of the band to be registered to highlight the contrast and make the number of extracted feature points more.
本步骤中，可使用或SIFT(Scale-invariant feature transform，尺度不变特征转换)或Harris或SURF(Speeded-Up Robust Features，加速稳健特征)作为提取特征点的算子，本实施例优选算子，通过计算待配准波段图像各像素的Robert梯度和以像素(c,r)为中心的一个窗口的灰度协方差矩阵，在待配准波段图像中寻找尽可能小而接近圆的误差椭圆的点作为特征点。In this step, you can use Or SIFT (Scale-invariant feature transform, scale-invariant feature transformation) or Harris or SURF (Speeded-Up Robust Features, accelerated robust features) as the operator for extracting feature points, preferred in this embodiment Operator, by calculating the Robert gradient of each pixel of the band image to be registered and the gray level covariance matrix of a window centered on the pixel (c, r), looking for the smallest possible and close to the circle in the band image to be registered The points of the error ellipse are used as feature points.
步骤S300，对每一个特征点，在基准图像上选择与所述特征点相同的坐标作为中心，提取搜索窗口，然后使用归一化相关系数完成粗匹配得到粗匹配点。Step S300, for each feature point, select the same coordinates as the feature point on the reference image as the center, extract a search window, and then use the normalized correlation coefficient to complete rough matching to obtain a rough matching point.
粗匹配完成之后，可能会存在一些误匹配点，使用随机采样一致性方法(RandomSample Consensus,RANSAC)和最小二乘法剔除误匹配点，得到正确的粗匹配点。After the rough matching is completed, there may be some false matching points. Use the Random Sample Consensus (RANSAC) and the least squares method to eliminate the false matching points to obtain the correct rough matching points.
步骤S400，基于上述正确的粗匹配点建立待配准波段图像和基准图像的粗变换关系，对每一个特征点，根据所述粗变换关系计算出所述特征点在基准图像上的匹配点，以所述匹配点为中心提取搜索窗口，使用归一化相关系数完成精匹配得到精匹配点。Step S400, based on the above correct rough matching points, establish a rough transformation relationship between the band image to be registered and the reference image, and for each feature point, calculate the matching point of the feature point on the reference image according to the rough transformation relationship, A search window is extracted centering on the matching point, and a normalized correlation coefficient is used to complete fine matching to obtain a fine matching point.
相比粗匹配，精匹配使用更小的搜索窗口，更高的归一化相关系数阈值，找到更精确的精匹配点。精确匹配完成后，使用RANSAC和最小二乘法剔除误匹配点，得到正确的精匹配点。Compared with rough matching, fine matching uses a smaller search window and a higher normalized correlation coefficient threshold to find more accurate fine matching points. After the exact matching is completed, RANSAC and the least square method are used to eliminate the wrong matching points to obtain the correct fine matching points.
步骤S500，基于上述正确的精匹配点建立待配准波段图像和基准图像的精变换关系，然后使用该精变换关系完成待配准波段图像的校正。Step S500, based on the correct fine matching points, establish a fine transformation relationship between the band image to be registered and the reference image, and then use the fine transformation relationship to complete the correction of the band image to be registered.
校正模型使用多项式模型，多项式模型的阶数根据匹配点数量和图像变形情况来确定。多项式模型需要匹配点分布比较均匀。在校正之前对匹配点进行均匀化，具体方法为：对图像按网格划分，把匹配点分配到不同的网格，对于有匹配点的网格，保留匹配度最大的一个控制点，然后利用均匀化后的匹配点构建模型。The correction model uses a polynomial model, and the order of the polynomial model is determined according to the number of matching points and the image deformation. The polynomial model requires a relatively uniform distribution of matching points. Homogenize the matching points before correction. The specific method is: divide the image into grids, assign the matching points to different grids, and keep the control point with the largest matching degree for the grid with matching points, and then use The homogenized matching points build a model.
步骤S600，重复步骤S200～S500，完成下一波段的配准。In step S600, steps S200-S500 are repeated to complete the registration of the next band.
为了提高配准效率，每配准一个波段之后，计算该波段相对于基准波段的偏移量，在步骤S300中对下一波段使用特征点的坐标加上偏移量之后的坐标为中心提取搜索窗口。In order to improve registration efficiency, after each band is registered, calculate the offset of the band relative to the reference band, and use the coordinates of the feature points plus the offset as the center to extract and search the next band in step S300 window.
如图2所示，本实施例还提供一种用于上述高光谱图像的波段配准方法的系统，包括：As shown in FIG. 2, this embodiment also provides a system for the band registration method of the above-mentioned hyperspectral image, including:
基准图像生成模块100，用于选取某一个波段作为基准波段，然后将该波段的图像上下左右各扩充若干个像素，生成新的图像作为基准图像；The reference image generating module 100 is used to select a certain band as the reference band, and then expand the image of the band by several pixels up, down, left, and right to generate a new image as the reference image;
模板窗口提取模块200，用于在待配准波段图像上提取特征点，以特征点为中心提取每个特征点的模板窗口；The template window extraction module 200 is used to extract feature points on the band image to be registered, and extract the template window of each feature point centered on the feature points;
粗匹配模块300，用于对每一个特征点，在基准图像上选择与所述特征点相同的坐标作为中心，提取搜索窗口，使用模板匹配方法得到粗匹配点；The rough matching module 300 is used to select the same coordinates as the center on the reference image for each feature point, extract a search window, and use a template matching method to obtain a rough matching point;
精匹配模块400，用于基于所述粗匹配点建立待配准波段图像和基准图像的粗变换关系，对每一个特征点，根据所述粗变换关系计算出所述特征点在基准图像上的匹配点，以所述匹配点为中心提取搜索窗口，使用模板匹配方法得到精匹配点The fine matching module 400 is used to establish a rough transformation relationship between the band image to be registered and the reference image based on the rough matching points, and for each feature point, calculate the position of the feature point on the reference image according to the rough transformation relationship. Matching points, the search window is extracted centering on the matching points, and the template matching method is used to obtain fine matching points
几何校正模块500，用于基于所述精匹配点建立待配准波段图像和基准图像的精变换关系，然后使用该精变换关系完成待配准波段图像的校正。The geometric correction module 500 is configured to establish a fine transformation relationship between the band image to be registered and the reference image based on the fine matching points, and then use the fine transformation relationship to complete the correction of the band image to be registered.
本实施例的波段配准系统，还包括图像增强模块，用于在提取特征点之前，对待配准波段图像进行突出对比度的增强操作。The band registration system of this embodiment further includes an image enhancement module, which is used to enhance the contrast of the band image to be registered before extracting the feature points.
本实施例的波段配准系统，还包括误匹配点剔除模块，用于使用随机采样一致性方法和最小二乘法剔除误匹配点。The band registration system of this embodiment further includes a mismatch point elimination module, which is used to eliminate mismatch points by using the random sampling consistency method and the least square method.
本实施例的波段配准系统，还包括偏移量计算模块，用于在配准一个波段之后，计算该波段相对于基准波段的偏移量。The band registration system of this embodiment further includes an offset calculation module, configured to calculate an offset of a band relative to a reference band after the band is registered.
本实施例提供的高光谱图像的波段校准方法和系统可以自动完成无人机高光谱图像的波段配准，使无人机高光谱图像所有波段具有空间一致性，处理速度快，配准精度高。The hyperspectral image band calibration method and system provided in this embodiment can automatically complete the band registration of the UAV hyperspectral image, so that all bands of the UAV hyperspectral image have spatial consistency, fast processing speed, and high registration accuracy .
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到其各种变化或替换，这些都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以所述权利要求的保护范围为准。The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person familiar with the technical field can easily think of its various changes or modifications within the technical scope disclosed in the present invention. Replacement, these should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the protection scope of the claims.
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