遥感图像薄云的小波自适应阈值去除

可见光遥感图像最常见的薄云噪声严重地影响其解译的准确性，因此根据薄云噪声主要影响图像的低频信号，提出单波段遥感图像小波变换自适应阈值去云，图像经小波分解后，薄云噪声与地物信息在低频小波系数的阈值使用遗传算法以广义交叉验证GCV准则作为目标函数自动寻找，然后对小波系数进行阈值化去云。结果表明，该方法可有效去除薄云噪声并保留地物信息，使原来模糊的地物细节信息变清晰，信息熵最高，去云效果优于小波同态滤波，且明显优于同态滤波；不同尺度低频小波系数中薄云噪声与地物信息间的阈值，可用遗传算法和GCV准则有效地自动确定。

Thin Cloud Removal from Remote Sensing Images with Adaptive Thresholds of Wavelet Transforms

Thin cloud is one type of the most common noises in optical remote sensing images. It will severely affect the recognition precision of images. As thin cloud mainly exists in low frequency domain, the algorithm with adaptive thresholds of wavelet transform is presented to remove thin cloud in the single band image. The thresholds between thin cloud and land surface objects in the wavelet coefficients can be adaptively searched with general crossing validation (GCV) and genetic algorithm (GA). Then thin cloud is removed by shrinking wavelet coefficients. Results indicate that thin cloud can be effectively removed with the proposed algorithm while land surface objects are maintained, and blurry details of land surface objects become clear. With the highest entropy, the cloud-removed image from the algorithm shows better effect than those from homomorphic wavelet-based filter and homomorphic filter. The different scale thresholds of wavelet coefficients between thin cloud and land surface objects can be picked out with GA and GCV automatically.