Wang Shu-min, Zhang Ai-wu, Hu Shao-xing, Wang Jing-meng, Meng Xian-gang, Duan Yi-hao, Sun Wei-dong
Guang Pu Xue Yu Guang Pu Fen Xi. 2015 Feb;35(2):557-62.
As the rotation speed of ground based hyperspectral imaging system is too fast in the image collection process, which exceeds the speed limitation, there is data missed in the rectified image, it shows as the_black lines. At the same time, there is serious distortion in the collected raw images, which effects the feature information classification and identification. To solve these problems, in this paper, we introduce the each component of the ground based hyperspectral imaging system at first, and give the general process of data collection. The rotation speed is controlled in data collection process, according to the image cover area of each frame and the image collection speed of the ground based hyperspectral imaging system, And then the spatial orientation model is deduced in detail combining with the star scanning angle, stop scanning angle and the minimum distance between the sensor and the scanned object etc. The oriented image is divided into grids and resampled with new spectral. The general flow of distortion image corrected is presented in this paper. Since the image spatial resolution is different between the adjacent frames, and in order to keep the highest image resolution of corrected image, the minimum ground sampling distance is employed as the grid unit to divide the geo-referenced image. Taking the spectral distortion into account caused by direct sampling method when the new uniform grids and the old uneven grids are superimposed to take the pixel value, the precise spectral sampling method based on the position distribution is proposed. The distortion image collected in Lao Si Cheng ruin which is in the Zhang Jiajie town Hunan province is corrected through the algorithm proposed on above. The features keep the original geometric characteristics. It verifies the validity of the algorithm. And we extract the spectral of different features to compute the correlation coefficient. The results show that the improved spectral sampling method is better than the direct sampling method. It provides the reference for the similar product used on the ground.
由于地面高光谱成像系统在图像采集过程中的旋转速度过快，超过了速度限制，导致校正后的图像中出现数据缺失，表现为黑色线条。同时，采集到的原始图像存在严重失真，影响了特征信息的分类和识别。为了解决这些问题，本文首先介绍了地面高光谱成像系统的各个组成部分，并给出了数据采集的一般过程。在数据采集过程中，根据每一帧图像的覆盖面积和地面高光谱成像系统的图像采集速度来控制旋转速度。然后，结合星扫描角度、停止扫描角度以及传感器与被扫描物体之间的最小距离等详细推导了空间定向模型。对定向后的图像进行网格划分并重新采样新的光谱。本文给出了失真图像校正的一般流程。由于相邻帧之间的图像空间分辨率不同，为了保持校正后图像的最高分辨率，采用最小地面采样距离作为网格单元来划分地理参考图像。考虑到新的均匀网格和旧的不均匀网格叠加获取像素值时直接采样方法引起的光谱失真，提出了基于位置分布的精确光谱采样方法。利用上述算法对采集于湖南省张家界市老司城遗址的失真图像进行了校正。校正后的特征保持了原始几何特征。验证了算法的有效性。并提取不同特征的光谱计算相关系数。结果表明，改进后的光谱采样方法优于直接采样方法。为地面同类产品的使用提供了参考。
