光谱学与光谱分析, 2014, 34 (7): 1983, 网络出版: 2014-07-22   

顾及光谱混叠的高光谱相机任意方向侧扫几何成像

The Hyperspectral Camera Side-Scan Geometric Imaging in Any Direction Considering the Spectral Mixing
作者单位
1 首都师范大学三维信息获取与应用教育部重点实验室, 北京 100048
2 中国地震局地震预测研究所, 北京 100036
3 北京航空航天大学机械及自动化学院, 北京 100083
4 清华大学电子工程系, 北京 100084
摘要
以移动平台的线推扫式高光谱相机横向推扫成像不同于以卫星平台或飞机平台的竖直摄影成像方式, 其属于水平方向推扫竖直方向成像, 存在投影面选择、 空间方位确定以及光顾混叠等问题。 针对上述情况, 详细推导了适合于线阵高光谱相机地面推扫的影像像素地理参考模型, 可以进行任意方向的地面横向推扫成像; 结合影像的地面采样间隔大小及推扫成像的区域范围, 给出了地理参考后影像格网划分的一般过程; 同时, 考虑到在影像格网划分过程中存在的舍入误差, 以及在影像校正过程中采用传统直接光谱采样可能造成的光谱掺杂问题, 提出了基于地理参考后相邻像素重叠面积作为权重系数进行加权融合的改进光谱采样方法。 最后进行了大量地面横向推扫成像实验, 并依据上述算法进行了影像畸变校正, 验证了线阵影像几何畸变校正算法的有效性及鲁棒性, 同时, 对校正好后的影像选用多个样本点进行了光谱数据验证, 实验表明改进后的光谱采样方法明显优于直接光谱采样算法, 为同类产品的地面应用提供参考。
Abstract
In order to correct the image distortion in the hyperspectral camera side-scan geometric Imaging, the image pixel geo-referenced algorithm was deduced in detail in the present paper, which is suitable to the linear push-broom camera side-scan imaging on the ground in any direction. It takes the orientation of objects in the navigation coordinates system into account. Combined with the ground sampling distance of geo-referenced image and the area of push broom imaging, the general process of geo-referenced image divided into grids is also presented. The new image rows and columns will be got through the geo-referenced image area dividing the ground sampling distance. Considering the error produced by round rule in the pixel grids generated progress, and the spectral mixing problem caused by traditional direct spectral sampling method in the process of image correction, the improved spectral sampling method based on the weighted fusion method was proposed. It takes the area proportion of adjacent pixels in the new generated pixel as coefficient and then the coefficients are normalized to avoid the spectral overflow. So the new generated pixel is combined with the geo-referenced adjacent pixels spectral. Finally the amounts of push-broom imaging experiments were taken on the ground, and the distortion images were corrected according to the algorithm proposed above. The results show that the linear image distortion correction algorithm is valid and robust. At the same time, multiple samples were selected in the corrected images to verify the spectral data. The results indicate that the improved spectral sampling method is better than the direct spectral sampling algorithm. It provides reference for the application of similar productions on the ground.

王书民, 张爱武, 胡少兴, 孙卫东. 顾及光谱混叠的高光谱相机任意方向侧扫几何成像[J]. 光谱学与光谱分析, 2014, 34(7): 1983. WANG Shu-min, ZHANG Ai-wu, HU Shao-xing, SUN Wei-dong. The Hyperspectral Camera Side-Scan Geometric Imaging in Any Direction Considering the Spectral Mixing[J]. Spectroscopy and Spectral Analysis, 2014, 34(7): 1983.

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