由于探测器之间对接收的地物辐射信号的响应特征不同,导致遥感数据含有条带噪声,严重影响了图像质量及后续的定量计算。针对探测器响应函数在图像低值区及高值区呈非线性的特点,在着重分析矩匹配方法的基础上,提出分段线性动态矩匹配条带去除方法。方法设定阈值分割高中低值域统计区间,对探测器响应函数进行分段线性拟合,并对探测器每一分图像动态采用其领域内均值和标准差作为参考值进行条带纠正。应用TM数据第4波段及环境一号卫星高光谱数据进行去条带实验,并定性和定量地比较了该方法与动态矩匹配、傅里叶变换、自动均衡化曲线方法的去条带效果。结果表明该方法能够在保留图像基本信息的前提下,获得最佳的去条带效果,尤其能够提高非均匀地物分布区域内水体的条带去除效果。

Due to sensor-to-sensor variation within instruments,stripe noise,which affects image quality and subsequent quantitative calculation,is often detected in remote sensing data. Most previous destriping methods are based on the assumption that photomulipliers are linear. In fact,the nonlinearity is stronger in the low and high signal regions. Moment matching is emphasized in detail and a piece-wise linear dynamic moment matching algorithm is suggested which thresholds the image into low-median-high regions,and destripes each subscene separately by dynamically using its neighborhood average value and standard deviation as reference values. This is equivalent to modeling the relationship between sensors as piece-wise linear rather than simple linear. Tests on band 4 of a TM image and on HJ-1A HSI data show that piece-wise linear dynamic moment matching algorithm reduces stripes to a greater degree while retaining the basic information of image than dynamic moment matching method,Fourier transformation method and automatic equalization curves method. The visual and quantitative assessments make sure that this method is reliable and improves destriping effect of huge water body in heterogeneous area.