摘要
去除遥感图像薄云/雾干扰是遥感图像处理的一个经常性任务。研究发现薄云干扰作为附加于图像信号上的低频干扰,通常表现为图像亮度增大和饱和度下降的信号变化。由于云雾厚度存在由中心向边缘的渐次变化,亮度和饱和度的附加增量也表现出相应的梯度变化。通过图像采样和云雾分布场的统计相关分析,给出距离D与亮度V和饱和度S之间的非线性关系,逐点计算V和S的改正数,来达到去除云雾、恢复景物波谱特征的目的。并提出加入方向角的加权距离算法及简化算法。实验结果表明:该方法具有云雾改正效果好、采样可操作和工作量小、运算开销小等特点。
Removing thin cloud/fog cover is one of regular tasks in remote sensing image processing. In this study,it is found that the effects from thin cloud cover to spectral signal usually have a performance of low- fFequency characteristic and lead to both increased brightness V and reduced saturation S of the image sig- nals. As the thickness of the cloud gradually changes from the center of cloud to the edge, the corrections for V and S also vary gradually. Therefore the relations between image distance D and S and between D and V divided by S were obtained by image sample and regression analysis. And then through a pixel-by-pixel correction in both S and V with the two relations,the spectral signals can be recovered well. When added a parameter of orientation angle, these relations can also be used to remove cloud in an asymmetric cloudy field. It has been demonstrated by several tests that the spectral signals can be recovered quite well with a bove mentioned method and has a low calculating costs for sampling,regression and correction.
出处
《遥感技术与应用》
CSCD
北大核心
2013年第4期640-646,共7页
Remote Sensing Technology and Application
基金
国家自然科学基金委员会国家基础科学人才培养基金项目(J1103412)
国家自然科学基金项目"城镇植物固碳模型的遥感驱动方法"(41071275)
关键词
遥感图像
薄云干扰
梯度改正
光谱信号恢复
Remote sensing image
Thin cloud cover
Pixel-by-pixel correction
Spectral signal recovery
