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.