小波分析在灰度遥感图像去云中的应用

针对薄云覆盖的灰度遥感图像其清晰度低、难于判读地物信息等问题, 提出了小波分析方法用于灰度遥感图像去云。以256 pixel×256 pixel的灰度遥感图像为例, 进行1～8级分解, 重构时对细节系数放大1～10倍, 先选定小波函数确定图像信息熵取得最高时的分解级数为7和相应细节系数的放大倍数, 再确定双正交2.2型小波和离散近似Mery小波, 以0.1为步长的细节放大倍数作进一步分析。结果表明, 分解的级数较低时, 图像信息熵对细节系数放大倍数的依赖性很强; 放大倍数为1.1时得到的图像其信息熵取得极大值; 采用离散近似Mery小波, 当放大倍数为4.8和5.7时还出现2个峰值。

Application of Removing Cloud in Gray Remote Sensing Images with Wavelet Analysis

Because of cloud in remote sensing images, the images were fuzzy and the information of earth objects could not been read. A new method for removing cloud had been projected based on wavelet analysis. The 1～8 orders decomposition for several gray remote sensing images 256 pixel×256 pixel was implemented with wavelet analysis and the detail coefficients were multiplied with 1～10 times in the process of reconstruction. Firstly, the order 7 of decomposition and the times of detail coefficiency had been selected by maxium entropy with selected wavelet function. Secondly, the functions of bi-orthogonal 2.2 wavelet and discrete approximation of Mery wavelet had been found by maxium entropy with selected order number and times. Finally, the exact order number and times had been shown in the curves of 1～8 orders decomposition and the times from 1 to 10 with 0.1 step length. The results show that the times 1.1 was best times for removing cloud from remote sensing images with thin cloud, and for discrete approximation of Mery wavelet there were two peaks in the times as 4.8 and 5.7.