WO2018076137A1 - 一种高光谱图像特征描述子的获取方法及装置 - Google Patents

一种高光谱图像特征描述子的获取方法及装置 Download PDF

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WO2018076137A1
WO2018076137A1 PCT/CN2016/103069 CN2016103069W WO2018076137A1 WO 2018076137 A1 WO2018076137 A1 WO 2018076137A1 CN 2016103069 W CN2016103069 W CN 2016103069W WO 2018076137 A1 WO2018076137 A1 WO 2018076137A1
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angle
direction angle
sub
hyperspectral image
pixel
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French (fr)
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李岩山
范雷东
刘鹏
夏荣杰
谢维信
黄庆华
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Shenzhen University
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Shenzhen University
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    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition

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  • the invention belongs to the field of computers, and in particular relates to a method and a device for acquiring descriptors of interest points of a hyperspectral image in a hyperspectral image based on a spectral angle.
  • hyperspectral images have been widely used in civil and military fields such as remote sensing, atmospheric, agricultural monitoring, medical spectral imaging diagnosis, food detection, and safety.
  • Hyperspectral image processing technology based on spectral analysis has been successfully applied to target detection, fine feature classification, mineral mapping and other fields.
  • hyperspectral image data has the characteristics of high spatial resolution, large number of bands and large amount of data.
  • Traditional spectral curve-based hyperspectral image classification technology has been difficult to process high spatial resolution hyperspectral images. . Therefore, in recent years, there have been many methods for studying hyperspectral images using points of interest.
  • Mukherjee combines the SIFT algorithm (Scale-invariant feature transform) with PCA technology (principal components analysis) and applies it to hyperspectral images. The detection of points of interest. Adding PCA dimensionality reduction technology to SIFT algorithm greatly reduces the computational complexity of interest point search, but also loses spectral information. Later generations improved it.
  • the spectral curve of the point of interest is directly used as its descriptor. This method cannot guarantee the rotation transformation, illumination transformation and noise interference of the hyperspectral image.
  • the descriptor has certain robustness and uniqueness.
  • the technical problem to be solved by the present invention is to provide a method and a device for acquiring a hyperspectral image feature descriptor, which are intended to solve the problem in the prior art that in the case of a hyperspectral image rotation transformation, illumination transformation, and noise interference, the description cannot be guaranteed.
  • the child has certain problems of robustness and uniqueness.
  • the present invention is implemented in such a manner that a method for acquiring a hyperspectral image feature descriptor includes:
  • the obtaining the direction angle of each pixel in the preset neighborhood of the hyperspectral image null spectrum domain interest point comprises:
  • the establishing a direction angle statistical histogram according to the direction angle of each of the pixels includes:
  • the direction angle of the sampling point is the horizontal axis
  • the amplitude accumulated value corresponding to the direction angle of the sampling point after the weight processing is the vertical axis, and the direction angle statistical histogram is established.
  • the acquiring the feature vector of the rotated hyperspectral image includes:
  • the feature vectors of the sub-regions are spliced to obtain feature vectors of the rotated hyperspectral image.
  • the acquiring feature vectors of each of the sub-regions includes:
  • the component of the feature vector of the sub-region is determined by the direction angle of the sub-region, and the component of the feature vector of the sub-region is determined.
  • the invention also provides a device for acquiring a hyperspectral image feature descriptor, comprising:
  • a direction angle acquiring unit configured to acquire a direction angle of each pixel in a preset neighborhood of the high-spectrum image null spectrum domain interest point
  • a main direction angle acquiring unit configured to establish a direction angle statistical histogram according to a direction angle of each of the pixels, and obtain an angle corresponding to a peak in the direction angle statistical histogram;
  • An image rotation unit configured to calculate an angle corresponding to a peak in the histogram in the direction direction as a main direction, and rotate the hyperspectral image according to the main direction to obtain a rotated hyperspectral image
  • a descriptor acquisition unit is configured to acquire a feature vector of the rotated hyperspectral image, and save the feature vector as a descriptor of the hyperspectral image.
  • the direction angle acquiring unit includes:
  • a circular neighborhood determining module configured to obtain a circular neighborhood of the point of interest by using the hyperspectral image spatial domain point of interest as a circle and using a preset distance as a radius;
  • a square neighborhood determining module configured to determine, by using each pixel in the circular neighborhood as a sampling point, a square neighborhood of the sampling point according to a preset side length
  • a spectral angle calculation module configured to calculate a spectral angle of each pixel in the square neighborhood and the sampling point, and determine a pixel with the largest spectral angle in the square neighborhood;
  • An angle calculation module is configured to calculate an angle between a pixel having the largest spectral angle in the square neighborhood and the sampling point, and the angle is used as a direction angle of the sampling point.
  • main direction angle acquiring unit is specifically configured to:
  • the direction angle of the sampling point is the horizontal axis
  • the amplitude accumulated value corresponding to the direction angle of the sampling point after the weight processing is the vertical axis, and the direction angle statistical histogram is established.
  • the descriptor obtaining unit includes:
  • An image dividing module configured to divide the rotated hyperspectral image into several sub-regions at equal intervals
  • a vector acquiring module configured to acquire a feature vector of each of the sub-regions, and splicing the feature vectors of the sub-regions to obtain a feature vector of the rotated hyperspectral image.
  • the vector acquisition module includes:
  • a direction angle calculation sub-module configured to calculate a direction angle of each picture element in the sub-area, and establish a direction angle statistical histogram of the sub-area according to a direction angle of each picture element in the sub-area;
  • the vector determining sub-module is configured to calculate, according to a direction angle of the sub-region, a component of a feature vector corresponding to each column in the histogram, and a feature vector of the sub-region.
  • the present invention has the beneficial effects that the embodiment of the present invention calculates the rotated hyperspectral image by acquiring the main direction of the neighborhood of the interest point of the hyperspectral image in the optical spectrum domain and rotating according to the main direction.
  • the feature vector is represented by the feature vector as a descriptor of the hyperspectral image.
  • This embodiment generates a description using the spatial information and spectral information of the point of interest.
  • Sub- with certain robustness and uniqueness, while using the main direction of the neighborhood of the point of interest to perform hyperspectral image rotation, obtain the rotated feature vector, and use the feature vector as a descriptor to ensure that the descriptor does not rotate. Denaturation and robustness, the descriptors still have good robustness and uniqueness in the case of hyperspectral image rotation transformation, illumination transformation and noise interference.
  • FIG. 1 is a flowchart of a method for acquiring a hyperspectral image feature descriptor according to a first embodiment of the present invention.
  • FIG. 2 is a flowchart of a method for acquiring a hyperspectral image feature descriptor according to a second embodiment of the present invention.
  • FIG. 3 is a schematic diagram of a neighborhood of a point of interest provided by a second embodiment of the present invention.
  • 4a is a schematic diagram showing a direction angle of each pixel in a circular neighborhood of a point of interest according to a second embodiment of the present invention.
  • 4b is a histogram of the direction angle statistics of the circular neighborhood provided by the second embodiment of the present invention.
  • FIG. 5a is a schematic diagram of a sub-area of a circular neighborhood of a point of interest of a rotated hyperspectral image according to a second embodiment of the present invention.
  • FIG. 5b is a directional statistical histogram of a sub-area provided by a second embodiment of the present invention.
  • FIG. 6 is a schematic structural diagram of an apparatus for acquiring a hyperspectral image feature descriptor according to a third embodiment of the present invention.
  • FIG. 7 is a schematic structural diagram of a direction angle acquiring unit according to a fourth embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of a description sub-acquisition unit according to a fifth embodiment of the present invention.
  • an ideal descriptor should have high robustness and uniqueness.
  • Robustness mainly refers to the ability of the descriptor to work stably under the conditions of image affine transformation, density transformation and noise interference.
  • Uniqueness means description
  • the sub-capture has the ability to capture and reflect this change as the local image structure of the point of interest changes.
  • the invention relates to determining a direction reference according to a partial image structure of a point of interest, a main direction, thereby realizing rotation invariance of the image, and generating a descriptor using the pixel direction angle information of the rotated image.
  • the SIFT algorithm uses the gradient information of the two-dimensional image to obtain the main direction of the local structure. After completing the image gradient calculation of the neighborhood of the interest point, the histogram is used to calculate the gradient direction and amplitude of the pixels in the neighborhood. Different from the method of using image gradient in two-dimensional image, the method uses the distribution feature of the spectral angle of the neighborhood pixel of interest point to determine the main direction.
  • the present invention provides a first embodiment, as shown in FIG. 1, a method for acquiring a hyperspectral image feature descriptor, including:
  • the acquiring device acquires the direction angle of each pixel in the preset neighborhood of the high-spectral image interest point, which is convenient for calculation.
  • the preset neighborhood is a circular neighborhood.
  • the acquiring device establishes a direction angle statistical histogram according to the direction angle of each pixel in the preset neighborhood, where the horizontal axis of the histogram is the angle of the direction angle, and the vertical axis is the direction angle of the pixel. Amplitude cumulative value. After establishing the direction angle statistical histogram, the acquiring device will acquire the angle of the direction angle corresponding to the peak in the direction angle statistical histogram.
  • S103 Calculate an angle corresponding to a peak in the histogram in the direction direction as a main direction, and rotate the hyperspectral image according to the main direction to obtain a rotated hyperspectral image.
  • the acquisition means rotates the hyperspectral image in the main direction by the angle of the direction angle corresponding to the peak in the histogram in the direction angle of the step S102, and obtains the rotated hyperspectral image.
  • the acquisition device rotates the hyperspectral image clockwise according to the main direction, centering on the point of interest.
  • the acquiring device acquires the feature vector of the rotated hyperspectral image, and uses the feature vector as a descriptor of the hyperspectral image.
  • the embodiment provides a method for acquiring a descriptor of a hyperspectral image spatial domain interest point, and the obtaining method first calculates a direction angle of each pixel in a preset neighborhood of the hyperspectral image spatial domain interest point, and A main direction is determined by using the direction angle of each pixel of the preset neighborhood of the point of interest, and then the hyperspectral image is rotated by one main direction and the descriptor is created by using the direction information of the neighborhood pixel of the point of interest.
  • the descriptor obtained by the obtaining method of the descriptor provided in this embodiment has certain rotation invariance and robustness.
  • the present invention further provides a second embodiment as shown in FIG. 2, a method for obtaining a hyperspectral image feature descriptor, comprising:
  • S201 The circular neighborhood of the interest point is obtained by taking the interest point of the hyperspectral image in the hyperspectral image as a circle and using a preset distance as a radius.
  • the direction angle of the sampling point is the horizontal axis
  • the amplitude accumulated value corresponding to the direction angle of the sampling point after the weight processing is the vertical axis
  • the direction angle statistical histogram is established, and the direction angle is obtained. Count the angles corresponding to the peaks in the histogram.
  • S206 Calculate an angle corresponding to the peak in the histogram in the direction direction as a main direction, and rotate the hyperspectral image according to the main direction to obtain a rotated hyperspectral image.
  • the rotated hyperspectral image is equally divided into a plurality of sub-regions
  • the amplitude value of each column in the histogram is calculated by using the direction angle of the sub-region, and the component of the feature vector of the sub-region is determined, and the feature vector of the sub-region is determined;
  • S210 splicing feature vectors of the sub-regions, acquiring feature vectors of the rotated hyperspectral image, and saving the feature vectors as descriptors of the hyperspectral images.
  • the acquisition method of the descriptor is mainly divided into calculating a direction angle of each pixel in a preset neighborhood of the interest point, establishing a statistical histogram of the direction angle, obtaining a peak of the statistical histogram of the direction angle, and rotating
  • the post-hyperspectral image is counted on the direction angle and the descriptor is generated in four steps.
  • the specific implementation process is as follows:
  • the acquisition method in this embodiment describes the hyper-spectral image in the circular neighborhood where the interest points of the optical spectrum domain are located, and the selection of the radius of the circular neighborhood needs to be set in a specific application to achieve an optimal algorithm.
  • Efficiency and effect as shown in FIG. 3, before describing the point of interest I, first determine the size of the circular neighborhood where the point of interest I is located, that is, determine the radius R of the circular neighborhood.
  • the direction angle of each pixel in the circular neighborhood needs to be calculated, and the calculation of the direction angle of each pixel is performed in a square neighborhood, as shown in FIG.
  • the size of the square neighborhood of the pixel I i that is, the side length W of the square neighborhood must first be determined.
  • the radius R of the circular neighborhood and the length W of the square neighborhood in this embodiment affect the calculation performance of the descriptor acquisition method and the judgment accuracy of the main direction. If the radius R of the circular neighborhood is too small, the main direction will be inaccurate. If the radius R is too large, the calculation amount will be increased and the efficiency of the algorithm will be reduced.
  • the side length W of the square neighborhood will determine the resolution of the direction angle, for example, when calculating the image. when the angular direction of the element I i, square neighborhood like element I i is the size of 3 * 3, the image element I i direction angular resolution of approximately 45 °, like the square neighborhood if the element I i is 5 * 5 At a large hour, the angular resolution of the pixel I i is about 22.5°.
  • the direction angle of each pixel in the circular neighborhood of the point of interest is calculated.
  • the present embodiment to an angle of maximum spectral image element I i for example, first calculates the square neighborhood pixel image I j spectral angle ⁇ with the central element of I i (I i, I j), determination of embodiments Pixel I k , ie Then calculate the angle between the pixel I k and the pixel I i Angle As the direction angle of the pixel I i .
  • the square neighborhood size of the pixel I i is 3*3 as an example for specific description:
  • the central pixel I i and the other eight pixels I 1 , I 2 , I 3 , I 4 , I 5 , I 6 , I 7 are respectively calculated in the square neighborhood of 3*3.
  • the horizontal axis of the direction angle statistical histogram is the direction angle of the pixel
  • the vertical axis is the amplitude accumulated value corresponding to the pixel direction angle.
  • the number of columns of the direction angle statistical histogram is determined by the size of the square neighborhood, as shown in Figure 4a, the arrows in the figure indicate the direction angle of the pixel, and Figure 4b shows the square neighborhood when the size is 3*3.
  • the direction angle statistical histogram has a total of 8 columns, representing the direction angle of other pixels except the central pixel in the square neighborhood, and 8 columns from the left.
  • the angles to the right are 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°, respectively.
  • Each sampling point that adds a histogram of the direction angle statistics (the sampling point represents all the cells in the circular neighborhood of the point of interest that need to calculate the direction angle, and all the pixels include the prototype boundary that falls on the circular neighborhood of the point of interest.
  • the magnitude of the pixel needs to be weighted, and the weighting uses a circular Gaussian weighting function, so that the magnitude near the point of interest has a larger weight.
  • the angle corresponding to the peak in the direction angle statistical histogram is determined, and the angle is used as the main direction of the point of interest.
  • the main direction of the point of interest is 180°.
  • a rotated hyperspectral image is obtained. The rotated hyperspectral image is shown in Figure 5a.
  • the amplitude accumulated value ie, d 1 , d 2 , . . .
  • each sub-region has information of 8 directions, so the feature vector D of the points of interest has 2*2*8 data, and finally forms 32-dimensional. Feature vector.
  • the present invention also provides a third embodiment as shown in FIG. 6, a device for acquiring a hyperspectral image feature descriptor, comprising:
  • a direction angle obtaining unit 601 configured to acquire a direction angle of each pixel in a preset neighborhood of the high-spectral image spatial domain domain interest point;
  • a main direction angle obtaining unit 602 configured to establish a direction angle statistical histogram according to a direction angle of each of the pixels, and acquire an angle corresponding to a peak in the direction angle statistical histogram;
  • the image rotation unit 603 is configured to calculate, according to the direction angle, an angle corresponding to a peak in the histogram as a main direction, and rotate the hyperspectral image according to the main direction to obtain a rotated hyperspectral image;
  • the descriptor obtaining unit 604 is configured to acquire a feature vector of the rotated hyperspectral image, and save the feature vector as a descriptor of the hyperspectral image.
  • the direction angle acquiring unit 601 includes:
  • the circular neighborhood determining module 6011 is configured to obtain a circular neighborhood of the point of interest by using the hyperspectral image spatial domain point of interest as a dot and a preset distance as a radius;
  • the square neighborhood determining module 6012 is configured to determine, by using each pixel in the circular neighborhood as a sampling point, a square neighborhood of the sampling point according to a preset side length;
  • the spectral angle calculation module 6013 is configured to calculate a spectral angle of each pixel in the square neighborhood and the sampling point, and determine a pixel with the largest spectral angle in the square neighborhood;
  • the angle calculation module 6014 is configured to calculate an angle between the pixel with the largest spectral angle in the square neighborhood and the sampling point, and use the included angle as the direction angle of the sampling point.
  • the description sub-acquisition unit 604 includes:
  • the image dividing module 6041 is configured to divide the rotated hyperspectral image into equal intervals into a plurality of sub-regions;
  • the vector obtaining module 6042 is configured to acquire feature vectors of each of the sub-regions, and perform splicing of feature vectors of the sub-regions to acquire feature vectors of the rotated hyperspectral image.
  • the vector obtaining module 6042 includes:
  • a direction angle calculation sub-module configured to calculate a direction angle of each picture element in the sub-area, and establish a direction angle statistical histogram of the sub-area according to a direction angle of each picture element in the sub-area;
  • the vector determining sub-module is configured to calculate, according to a direction angle of the sub-region, a component of a feature vector corresponding to each column in the histogram, and a feature vector of the sub-region.

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Abstract

一种高光谱图像特征描述子的获取方法，包括：获取高光谱图像空谱域兴趣点的预置邻域内的每一像元的方向角(S101)，根据每一所述像元的方向角建立方向角统计直方图，并获取所述方向角统计直方图中峰值对应的角度(S102)，以所述方向角统计直方图中峰值对应的角度为主方向，将所述高光谱图像按照所述主方向进行旋转，获取旋转后的高光谱图像(S103)，获取所述旋转后的高光谱图像的特征矢量，将所述特征矢量保存为所述高光谱图像的描述子(S104)。根据上述方法获取的描述子在高光谱图像发生旋转变换、光照变换和噪声干扰的情况下，仍然具备很好的鲁棒性和独特性。

Description

一种高光谱图像特征描述子的获取方法及装置 技术领域
本发明属于计算机领域，尤其涉及基于光谱角的高光谱图像空谱域兴趣点的描述子的获取方法及装置。
背景技术
目前，高光谱图像在遥感、大气、农业监测、医疗光谱成像诊断、食品检测、安全等民用领域以及军事领域得到了广泛的应用。基于光谱分析的高光谱图像处理技术已成功运用于目标探测、地物精细分类、矿物填图等领域。
随着遥感器制造技术的提高，高光谱图像数据具有空间分辨率高、波段数多和数据量大的特点，传统的基于光谱曲线的高光谱图像分类技术已经难以处理高空间分辨率高光谱图像。所以近几年出现了很多利用兴趣点对高光谱图像进行研究的方法。Mukherjee在论文‘Interest Points for Hyperspectral Image Data’中将SIFT算法(尺度不变特征变换，Scale-invariant feature transform)与PCA技术(主成分分析技术，principal components analysis)相结合，并应用到高光谱图像兴趣点的检测中。在SIFT算法中加入PCA降维技术极大地降低了兴趣点搜索的计算量，但同时也会丢失光谱信息。后人对其进行改进，在论文‘A Vector SIFT Detector for Interest Point Detection in Hyperspectral Imagery’中，Leidy P.
Figure PCTCN2016103069-appb-000001
充分考虑到光谱图像像元的矢量特性，将其看作是一幅矢量图像进行研究，获得的兴趣点数量更多，鲁棒性、独特性更强。但是对兴趣点进行描述的时候，两种方法都是直接利用兴趣点所在的光谱曲线作为其描述子。而在图像识别过程中，使用一种紧凑而完整的特征描述是十分重要的。在二维图像的SIFT算法中，Lowe通过对兴趣点邻域内像元的梯度进行直方图 统计计算描述子，使得兴趣点具有一定的旋转不变性，光照不变性以及仿射不变性。
现有技术在对高光谱图像兴趣点进行描述的时候是直接利用兴趣点所在的光谱曲线作为其描述子，这种方法在高光谱图像发生旋转变换，光照变换，噪声干扰的情况下，无法保证描述子具有一定的鲁棒性和独特性。
发明内容
本发明所要解决的技术问题在于提供一种高光谱图像特征描述子的获取方法及装置，旨在解决现有技术中在高光谱图像发生旋转变换，光照变换，噪声干扰的情况下，无法保证描述子具有一定的鲁棒性和独特性的问题。
本发明是这样实现的，一种高光谱图像特征描述子的获取方法，包括：
获取高光谱图像空谱域兴趣点的预置邻域内的每一像元的方向角；
根据每一所述像元的方向角建立方向角统计直方图，并获取所述方向角统计直方图中峰值对应的角度；
以所述方向角统计直方图中峰值对应的角度为主方向，将所述高光谱图像按照所述主方向进行旋转，获取旋转后的高光谱图像；
获取所述旋转后的高光谱图像的特征矢量，将所述特征矢量保存为所述高光谱图像的描述子。
进一步地，所述获取高光谱图像空谱域兴趣点的预置邻域内的每一像元的方向角包括：
以所述高光谱图像空谱域兴趣点为圆点，以预置距离为半径，获取所述兴趣点的圆形邻域；
以所述圆形邻域内的每一像元为采样点，按照预置边长确定所述采样点的方形邻域；
计算所述方形邻域中的每一像元与所述采样点的光谱角，确定所述方形邻域内光谱角最大的像元；
计算所述方形邻域内光谱角最大的像元与所述采样点之间的夹角，以所述夹角作为所 述采样点的方向角。
进一步地，所述根据每一所述像元的方向角建立方向角统计直方图包括：
以所述采样点的方向角为横轴，以进行权重处理后的所述采样点的方向角对应的幅值累加值为纵轴，建立方向角统计直方图。
进一步地，所述获取所述旋转后的高光谱图像的特征矢量包括：
将所述旋转后的高光谱图像等间隔划分为若干子区域；
获取各个所述子区域的特征矢量；
将所述子区域的特征矢量进行拼接，得到所述旋转后的高光谱图像的特征矢量。
进一步地，所述获取各个所述子区域的特征矢量包括：
计算所述子区域内每一像元的方向角，并根据所述子区域内每一像元的方向角建立所述子区域的方向角统计直方图；
以所述子区域的方向角统计直方图中每个柱对应的幅值累加值为所述子区域的特征矢量的分量，确定所述子区域的特征矢量。
本发明还提供了一种高光谱图像特征描述子的获取装置，包括：
方向角获取单元，用于获取高光谱图像空谱域兴趣点的预置邻域内的每一像元的方向角；
主方向角获取单元，用于根据每一所述像元的方向角建立方向角统计直方图，并获取所述方向角统计直方图中峰值对应的角度；
图像旋转单元，用于以所述方向角统计直方图中峰值对应的角度为主方向，将所述高光谱图像按照所述主方向进行旋转，获取旋转后的高光谱图像；
描述子获取单元，用于获取所述旋转后的高光谱图像的特征矢量，将所述特征矢量保存为所述高光谱图像的描述子。
进一步地，所述方向角获取单元包括：
圆形邻域确定模块，用于以所述高光谱图像空谱域兴趣点为圆点，以预置距离为半径，获取所述兴趣点的圆形邻域；
方形邻域确定模块，用于以所述圆形邻域内的每一像元为采样点，按照预置边长确定所述采样点的方形邻域；
光谱角计算模块，用于计算所述方形邻域中的每一像元与所述采样点的光谱角，确定所述方形邻域内光谱角最大的像元；
夹角计算模块，用于计算所述方形邻域内光谱角最大的像元与所述采样点之间的夹角，以所述夹角作为所述采样点的方向角。
进一步地，所述主方向角获取单元具体用于：
以所述采样点的方向角为横轴，以进行权重处理后的所述采样点的方向角对应的幅值累加值为纵轴，建立方向角统计直方图。
进一步地，所述描述子获取单元包括：
图像划分模块，用于将所述旋转后的高光谱图像等间隔划分为若干子区域；
矢量获取模块，用于获取各个所述子区域的特征矢量，将所述子区域的特征矢量进行拼接，得到所述旋转后的高光谱图像的特征矢量。
进一步地，所述矢量获取模块包括：
方向角计算子模块，用于计算所述子区域内每一像元的方向角，并根据所述子区域内每一像元的方向角建立所述子区域的方向角统计直方图；
矢量确定子模块，用于以所述子区域的方向角统计直方图中每个柱对应的幅值累加值为所述子区域的特征矢量的分量，确定所述子区域的特征矢量。
本发明与现有技术相比，有益效果在于：本发明实施例通过获取高光谱图像空谱域兴趣点的邻域的主方向，并按照该主方向进行旋转后，计算旋转后的高光谱图像的特征矢量，以该特征矢量作为高光谱图像的描述子。本实施例利用兴趣点的空间信息和光谱信息生成描述 子，具备一定的鲁棒性和独特性，同时利用兴趣点的邻域的主方向进行高光谱图像旋转，获取旋转后的特征矢量，以该特征矢量为描述子，保证该描述子的旋转不变性和鲁棒性，在高光谱图像发生旋转变换、光照变换和噪声干扰的情况下，该描述子依旧具备很好的鲁棒性和独特性。
附图说明
图1是本发明第一实施例提供的一种高光谱图像特征描述子的获取方法的流程图。
图2是本发明第二实施例提供的一种高光谱图像特征描述子的获取方法的流程图。
图3是本发明第二实施例提供的兴趣点的邻域的示意图。
图4a是本发明第二实施例提供的兴趣点的圆形邻域中每一像元的方向角示意图。
图4b是本发明第二实施例提供的圆形邻域的方向角统计直方图。
图5a是本发明第二实施例提供的旋转后高光谱图像的兴趣点圆形邻域的子区域示意图。
图5b是本发明第二实施例提供的子区域的方向角统计直方图。
图6是本发明第三实施例提供的一种高光谱图像特征描述子的获取装置的结构示意图。
图7是本发明第四实施例提供的方向角获取单元的结构示意图。
图8是本发明第五实施例提供的描述子获取单元的结构示意图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
在图像处理中，一个理想的描述子应该具有高的鲁棒性、独特性。鲁棒性主要是指描述子能在图像仿射变换、密度变换和噪声干扰等条件下具有稳定工作的能力。独特性是指描述 子在兴趣点局部图像结构发生变化时，具有捕获和反映这一变化的能力。
本发明涉及根据兴趣点的局部图像结构确定一个方向基准——主方向，从而实现图像的旋转不变性，并利用旋转后图像的像元方向角信息生成描述子。SIFT算法使用二维图像的梯度信息求取局部结构的主方向，在完成兴趣点邻域的图像梯度计算后，使用直方图统计邻域内像元的梯度方向和幅值。不同于二维图像中利用图像梯度的方法，本方法利用兴趣点邻域像元光谱角的分布特征来确定主方向。
基于上述表述，本发明提供了如图1所示的第一实施例，一种高光谱图像特征描述子的获取方法，包括：
S101，获取高光谱图像空谱域兴趣点的预置邻域内的每一像元的方向角。
在本步骤中，获取装置获取高光谱图像兴趣点的预置邻域内的每一像元的方向角，为计算方便，在本步骤中，预置邻域为圆形邻域。
S102，根据每一所述像元的方向角建立方向角统计直方图，并获取所述方向角统计直方图中峰值对应的角度。
在本步骤中，获取装置根据预置邻域内每一像元的方向角建立方向角统计直方图，该方向角统计直方图横轴为方向角的角度，纵轴为像元的方向角对应的幅值累加值。在建立方向角统计直方图之后，获取装置将获取该方向角统计直方图中峰值对应的方向角的角度。
S103，以所述方向角统计直方图中峰值对应的角度为主方向，将所述高光谱图像按照所述主方向进行旋转，获取旋转后的高光谱图像。
在本步骤中，获取装置以步骤S102中方向角统计直方图中峰值对应的方向角的角度为主方向，将高光谱图像按照该主方向进行旋转，得到旋转后的高光谱图像。具体地，获取装置将根据主方向，以兴趣点为中心，将所述高光谱图像按照顺时针进行旋转。
S104，获取所述旋转后的高光谱图像的特征矢量，将所述特征矢量保存为所述高光谱图像的描述子。
在本步骤中，得到旋转后的高光谱图像后，获取装置将获取该旋转后的高光谱图像的特征矢量，并将该特征矢量作为该高光谱图像的描述子。
本实施例提供了一种高光谱图像空谱域兴趣点的描述子的获取方法，该获取方法首先计算高光谱图像空谱域兴趣点的预置邻域内的每一像元的方向角，并利用兴趣点的预置邻域的每一像元的方向角确定一个主方向，然后将高光谱图像旋转一个主方向并利用兴趣点邻域像元的方向信息建立描述子。本实施例提供的描述子的获取方法得到的描述子具有一定的旋转不变性和鲁棒性。
本发明还提供的如图2所示的第二实施例，一种高光谱图像特征描述子的获取方法，包括：
S201，以所述高光谱图像空谱域兴趣点为圆点，以预置距离为半径，获取所述兴趣点的圆形邻域。
S202，以所述圆形邻域内的每一像元为采样点，按照预置边长确定所述采样点的方形邻域。
S203，计算所述方形邻域中的每一像元与所述采样点的光谱角，确定所述方形邻域内光谱角最大的像元。
S204，计算所述方形邻域内光谱角最大的像元与所述采样点之间的夹角，以所述夹角作为所述采样点的方向角。
S205，以所述采样点的方向角为横轴，以进行权重处理后的所述采样点的方向角对应的幅值累加值为纵轴，建立方向角统计直方图，并获取所述方向角统计直方图中峰值对应的角度。
S206，以所述方向角统计直方图中峰值对应的角度为主方向，将所述高光谱图像按照所述主方向进行旋转，获取旋转后的高光谱图像。
S207，将所述旋转后的高光谱图像等间隔划分为若干子区域；
S208，计算所述子区域内每一像元的方向角，并根据所述子区域内每一像元的方向角建立所述子区域的方向角统计直方图；
S209，以所述子区域的方向角统计直方图中每个柱对应的幅值累加值为所述子区域的特征矢量的分量，确定所述子区域的特征矢量；
S210，将所述子区域的特征矢量进行拼接，获取所述旋转后的高光谱图像的特征矢量，将所述特征矢量保存为所述高光谱图像的描述子。
下面对本实施例进行详细的阐述：
在本实施例中，描述子的获取方法主要分为计算兴趣点的预置邻域内的每个像元的方向角、建立方向角统计直方图、求取方向角统计直方图的峰值和在旋转后的高光谱图像上对方向角进行统计并生成描述子四个步骤。具体的实现过程如下：
(1)确定邻域大小
本实施例中的获取方法是在高光谱图像空谱域兴趣点所在的圆形邻域内对其进行描述，圆形邻域的半径的选择需要在具体应用中进行设置，以达到最优的算法效率和效果，如图3所示，在对兴趣点I进行描述之前，首先要确定兴趣点I所在的圆形邻域的大小，即确定圆形邻域的半径R。
在兴趣点I的圆形邻域确定以后，需要计算圆形邻域内每个像元的方向角，每一像元的方向角的计算是在一个方形邻域内进行的，如图3所示，假设需要计算像元Ii的方向角，则首先必须确定像元Ii的方形邻域的大小，即方形邻域的边长W。
本实施例中的圆形邻域的半径R和方形邻域的边长W的大小会影响描述子的获取方法的计算性能和主方向的判断精度。圆形邻域的半径R过小会使主方向不准确，半径R过大则会增加计算量，降低算法效率，而方形邻域的边长W将决定方向角的分辨率，比如当计算像元Ii的方向角时，像元Ii的方形邻域为3*3大小时，像元Ii的方向角分辨率约为45°，若像元Ii的方形邻域为5*5大小时，像元Ii的方向角分辨率约为22.5°。
(2)方向角的计算
在圆形邻域和方形邻域确定后，开始计算兴趣点的圆形邻域内的每个像元的方向角。如图3所示，本实施例中以像元Ii为例，首先计算方形邻域内像元Ij与中心像元Ii的光谱角α(Ii，Ij)，确定光谱角最大的像元Ik，即
Figure PCTCN2016103069-appb-000002
然后计算像元Ik和像元Ii的夹角
Figure PCTCN2016103069-appb-000003
将夹角
Figure PCTCN2016103069-appb-000004
作为像元Ii的方向角。
下面，以像元Ii的方形邻域大小为3*3为例进行具体说明：
假设对Ii计算方向角，在3*3的方形邻域内，分别计算中心像元Ii与其他8个像元I1，I2，I3，I4，I5，I6，I7，I8光谱角，计算结果分别为θ1，θ2，θ3，θ4，θ5，θ6，θ7，θ8，从计算结果中选出最大的θ值，即θ＝max(θ1，θ2，θ3，θ4，θ5，θ6，θ7，θ8)，设最大的θ值对应的像元为Ik，则像元Ik与中心像元Ii的夹角即为像元Ii的方向角。
(3)建立方向角统计直方图
在对兴趣点的圆形邻域内所有像元的方向角计算完成后，需要使用至反复图统计圆形邻域内所有像元的方向角和幅值。在本实施例中，方向角统计直方图的横轴是像元的方向角，纵轴是像元方向角对应的幅值累加值。方向角统计直方图的柱数由方形邻域的大小决定，如图4a所示，图中的箭头表示该像元的方向角，而图4b示出了方形邻域为3*3大小时的兴趣点圆形邻域的方向角统计直方图，该方向角统计直方图中共有8个柱，分别代表该方形邻域中除了中心像元以外的其他像元的方向角，8个柱从左到右的角度分别为0°、45°、90°、135°、180°、225°、270°和315°。每个加入方向角统计直方图的采样点(采样点表示兴趣点的圆形邻域内所有需要计算方向角的像元，所有的像元包括落在兴趣点的圆形邻域的原型边界上的像元)的幅值都需要进行权重处理，加权采用圆形高斯加权函数，使得兴趣点附近的幅值有较大的权重。
(4)确定主方向并生成描述子
建立方向角统计直方图后，确定方向角统计直方图中的峰值对应的角度，以该角度作为兴趣点的主方向，如图4b中所示，兴趣点的主方向为180°。将高光谱图像旋转一个主方向后，得到旋转后的高光谱图像。旋转后的高光谱图像如图5a所示。
本实施例中，在获取如图5a所示的旋转后的该光谱图像之后，获取装置按照预置的划分规则，将兴趣点的圆形邻域等间距划分为n*n个子区域(图5a中，n＝2)，并在划分的子区域内计算每一像元的方向角，根据计算得出的子区域的每一像元的方向角建立该子区域的方向角统计直方图，该子区域的方向角统计直方图如图5b所示。以图5a中子区域501的方向角统计直方图的每个柱对应的幅值累加值(即d1，d2，...d8)作为子区域501的特征矢量D1的分量，生成D1，即D1＝(d1，d2，...d8)。图5a中存在2*2个子区域，因此共有2*2个特征矢量，D1，D2，D3，D4，将每个子区域的特征矢量拼接形成兴趣点的最终的特征矢量，即兴趣点的特征矢量D＝(D1，D2，D3，D4)。因为在本实施例所示的图5a中存在2*2个子区域，每个子区域共有8个方向的信息，因此兴趣点的特征矢量D共有2*2*8个数据，最终形成了32维的特征矢量。
本发明还提供了如图6所示的第三实施例，一种高光谱图像特征描述子的获取装置，包括：
方向角获取单元601，用于获取高光谱图像空谱域兴趣点的预置邻域内的每一像元的方向角；
主方向角获取单元602，用于根据每一所述像元的方向角建立方向角统计直方图，并获取所述方向角统计直方图中峰值对应的角度；
图像旋转单元603，用于以所述方向角统计直方图中峰值对应的角度为主方向，将所述高光谱图像按照所述主方向进行旋转，获取旋转后的高光谱图像；
描述子获取单元604，用于获取所述旋转后的高光谱图像的特征矢量，将所述特征矢量保存为所述高光谱图像的描述子。
进一步地，如图7所示，方向角获取单元601包括：
圆形邻域确定模块6011，用于以所述高光谱图像空谱域兴趣点为圆点，以预置距离为半径，获取所述兴趣点的圆形邻域；
方形邻域确定模块6012，用于以所述圆形邻域内的每一像元为采样点，按照预置边长确定所述采样点的方形邻域；
光谱角计算模块6013，用于计算所述方形邻域中的每一像元与所述采样点的光谱角，确定所述方形邻域内光谱角最大的像元；
夹角计算模块6014，用于计算所述方形邻域内光谱角最大的像元与所述采样点之间的夹角，以所述夹角作为所述采样点的方向角。
进一步地，如图8所示，描述子获取单元604包括：
图像划分模块6041，用于将所述旋转后的高光谱图像等间隔划分为若干子区域；
矢量获取模块6042，用于获取各个所述子区域的特征矢量，将所述子区域的特征矢量进行拼接，获取所述旋转后的高光谱图像的特征矢量。
进一步地，矢量获取模块6042包括：
方向角计算子模块，用于计算所述子区域内每一像元的方向角，并根据所述子区域内每一像元的方向角建立所述子区域的方向角统计直方图；
矢量确定子模块，用于以所述子区域的方向角统计直方图中每个柱对应的幅值累加值为所述子区域的特征矢量的分量，确定所述子区域的特征矢量。
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种高光谱图像特征描述子的获取方法，其特征在于，所述获取方法包括：
    获取高光谱图像空谱域兴趣点的预置邻域内的每一像元的方向角；
    根据每一所述像元的方向角建立方向角统计直方图，并获取所述方向角统计直方图中峰值对应的角度；
    以所述方向角统计直方图中峰值对应的角度为主方向，将所述高光谱图像按照所述主方向进行旋转，获取旋转后的高光谱图像；
    获取所述旋转后的高光谱图像的特征矢量，将所述特征矢量保存为所述高光谱图像的描述子。
  2. 如权利要求1所述的获取方法，其特征在于，所述获取高光谱图像空谱域兴趣点的预置邻域内的每一像元的方向角包括：
    以所述高光谱图像空谱域兴趣点为圆点，以预置距离为半径，获取所述兴趣点的圆形邻域；
    以所述圆形邻域内的每一像元为采样点，按照预置边长确定所述采样点的方形邻域；
    计算所述方形邻域中的每一像元与所述采样点的光谱角，确定所述方形邻域内光谱角最大的像元；
    计算所述方形邻域内光谱角最大的像元与所述采样点之间的夹角，以所述夹角作为所述采样点的方向角。
  3. 如权利要求2所述的获取方法，其特征在于，所述根据每一所述像元的方向角建立方向角统计直方图包括：
    以所述采样点的方向角为横轴，以进行权重处理后的所述采样点的方向角对应的幅值累加值为纵轴，建立方向角统计直方图。
  4. 如权利要求1所述的获取方法，其特征在于，所述获取所述旋转后的高光谱图像的特征矢量包括：
    将所述旋转后的高光谱图像等间隔划分为若干子区域；
    获取各个所述子区域的特征矢量；
    将所述子区域的特征矢量进行拼接，得到所述旋转后的高光谱图像的特征矢量。
  5. 如权利要求4所述的获取方法，其特征在于，所述获取各个所述子区域的特征矢量包括：
    计算所述子区域内每一像元的方向角，并根据所述子区域内每一像元的方向角建立所述子区域的方向角统计直方图；
    以所述子区域的方向角统计直方图中每个柱对应的幅值累加值为所述子区域的特征矢量的分量，确定所述子区域的特征矢量。
  6. 一种高光谱图像特征描述子的获取装置，其特征在于，所述获取装置包括：
    方向角获取单元，用于获取高光谱图像空谱域兴趣点的预置邻域内的每一像元的方向角；
    主方向角获取单元，用于根据每一所述像元的方向角建立方向角统计直方图，并获取所述方向角统计直方图中峰值对应的角度；
    图像旋转单元，用于以所述方向角统计直方图中峰值对应的角度为主方向，将所述高光谱图像按照所述主方向进行旋转，获取旋转后的高光谱图像；
    描述子获取单元，用于获取所述旋转后的高光谱图像的特征矢量，将所述特征矢量保存为所述高光谱图像的描述子。
  7. 如权利要求6所述的获取装置，其特征在于，所述方向角获取单元包括：
    圆形邻域确定模块，用于以所述高光谱图像空谱域兴趣点为圆点，以预置距离为半径，获取所述兴趣点的圆形邻域；
    方形邻域确定模块，用于以所述圆形邻域内的每一像元为采样点，按照预置边长确定所述采样点的方形邻域；
    光谱角计算模块，用于计算所述方形邻域中的每一像元与所述采样点的光谱角，确定所述方形邻域内光谱角最大的像元；
    夹角计算模块，用于计算所述方形邻域内光谱角最大的像元与所述采样点之间的夹角，以所述夹角作为所述采样点的方向角。
  8. 如权利要求7所述的获取装置，其特征在于，所述主方向角获取单元具体用于：
    以所述采样点的方向角为横轴，以进行权重处理后的所述采样点的方向角对应的幅值累加值为纵轴，建立方向角统计直方图。
  9. 如权利要求6所述的获取装置，其特征在于，所述描述子获取单元包括：
    图像划分模块，用于将所述旋转后的高光谱图像等间隔划分为若干子区域；
    矢量获取模块，用于获取各个所述子区域的特征矢量，将所述子区域的特征矢量进行拼接，得到所述旋转后的高光谱图像的特征矢量。
  10. 如权利要求9所述的获取装置，其特征在于，所述矢量获取模块包括：
    方向角计算子模块，用于计算所述子区域内每一像元的方向角，并根据所述子区域内每一像元的方向角建立所述子区域的方向角统计直方图；
    矢量确定子模块，用于以所述子区域的方向角统计直方图中每个柱对应的幅值累加值为所述子区域的特征矢量的分量，确定所述子区域的特征矢量。
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