Auto-registration and Orthorecification Algorithm for the Time Series HJ-1A/B CCD Images

How to deal with geometric distortion is an open problem when using the massive amount of satellite images at a national or global scale, especially for multi-temporal image analysis. In this paper, an algorithm is proposed to automatically rectify the geometric distortion of time-series CCD multi- spectral data of small constellation for environmental and disaster mitigation （HJ-1A/B） which was launched by China in 2008. In this algorithm, the area-based matching method was used to automatically search tie points firstly, and then the polynomial function was introduced to correct the systematic errors caused by the satellite motion along the roll, pitch and yaw direction. The improved orthorectification method was finally used to correct pixel displacement caused by off-nadir viewing of topography, which are random errors in the images and cannot be corrected by the polynomial equation. Nine scenes of level 2 HJ CCD images from one path/row were taken as the warp images to test the algorithm. The test result showed that the overall accuracy of the proposed algorithm was within 2 pixels （the average residuals were 37.8 m, and standard deviations were 19.8 m）. The accuracies of 45.96% validation points （VPs） were within 1 pixel and 90.33% VPs were within 2 pixels. The discussion showed that three main factors including the distortion patterns of HJ CCD images, pereent of cloud cover and the varying altitude of the satellite orbit may affect the search of tie points and the accuracy of results. Although the influence of varying altitude of the satellite orbits is less than the other factors, it is noted that detailed satellite altitude information should be given in the future to get a more precise result. The proposed algorithm should be an efficient tool for the geo-correction of HJ CCD multi-spectral images.

Validation of Global Land Surface Satellite (GLASS) Downward Shortwave Radiation Product in the Rugged Surface

The downward shortwave radiation(DSR) is an essential parameter of land surface radiation budget and many land surface models that characterize hydrological,ecological and biogeochemical processes.The new Global LAnd Surface Satellite(GLASS) DSR datasets have been generated recently using multiple satellite data in China.This study investigates the performances of direct comparison approach,which is mostly used for validation of surface insolation retrieved from satellite data over the plain area,and indirect comparison approach,which needs a fine resolution map of DSR as reference,for validation of GLASS DSR product in time-steps of 1 and 3 hours over three Chinese Ecosystem Research Network sites located in the rugged surface.Results suggest that it probably has a large uncertainty to assess GLASS DSR product using the direct comparison method between GLASS surface insolation and field measurements over complex terrain,especially at Mt.Gongga 3,000 m station with root mean square error of 279.04 and 229.06 W/m2in time-steps of 1 and 3 hours,respectively.Further improvement for validation of GLASS DSR product in the rugged surface is suggested by generation of a fine resolution map of surface insolation and comparison of the aggregated fine resolution map with GLASS product in the rugged surface.The validation experience demonstrates that the GLASS DSR algorithm is satisfactory with determination coefficient of 0.83 and root mean square error of 81.91W/m2over three Chinese Ecosystem Research Network sites,although GLASS product overestimates DSR compared to the aggregated fine resolution map of surface insolation.

自适应空间强度约束和KL信息的模糊C均值彩色噪声图像分割

为了增强传统模糊C均值聚类算法的抗噪性能,保持任意像素与相邻像素间的隶属度相似性,提出一种自适应空间强度约束和KL信息的模糊C均值彩色噪声图像分割算法.首先,通过快速双边滤波器获取局部空间强度信息,用于平滑噪声像素;然后,将局部加权平均隶属度作为先验概率,并通过KL信息将其嵌入目标函数,从而优化隶属度的划分矩阵;最后,计算原始图像与双边滤波图像间的绝对强度差,用指数形式的绝对强度差作为双边滤波图像的自适应权值,并将其倒数作为原始图像的自适应权值.当混合噪声密度为30%时,所提出算法在彩色合成图像上的划分系数和划分熵分别为99.66%和0.58%,在彩色真实图像上的划分系数和划分熵分别为98.77%和2.03%.实验结果表明,与其他相关算法相比,所提出算法的抗噪性能更强、分割精度更高、稳定性更好.