基于改进的矩匹配方法高光谱影像条带噪声滤波技术

Destriping Hyperspectral Image Based on an Improved Moment Matching Method

高光谱遥感数据蕴含着丰富的地物反射光谱信息,其原始反射数据中含有大量的噪声,这些噪声严重影响地物反射光谱中的吸收特征,大大降低数据的分析精度,研究有效的高光谱遥感数据噪声滤波算法是改善高光谱数据分析效果的关键环节。研究了推扫高光谱图像(PHI)影像中条带噪声的高频特性,针对目前常用的矩匹配方法及几种改进的矩匹配方法都存在一定的缺点,提出一种改进的行平滑条带滤波方案,对含有条带噪声波段行均值曲线进行平滑处理,并调整图像中各像元的灰度值,以减小行间灰度差异,所得图像的峰值信噪比有所提高,取得了比按波段的矩匹配方法更好的去条带效果,在较好地削弱图像中条带噪声的同时,保留了原图像的辐射特征。

The hyperspectral remote sensing data contains rich information of reflective spectrum of surface feature, however, the original reflection data includes the massive noises, which affects the absorption feature of reflective spectrum and degrades the data analysis precision greatly. The study of hyperspectral remote sensing data noise filtering algorithm is the key to improve data analysis. The hyperspectral image noise filtering technology is studied. Deep research in high-frequency characteristics of pushbroom hyperspectral imager （PHI） stripe noise, in view of the disadvantages of currently common and several improved moment matching methods, an improved smooth filtering algorithm by row is proposed. Tbe line-average curve of the bands contained strip noise is smoothed and gray value of each pixel in the image is also adjusted to reduce the gray differences between the lines. The peak signal-to-noiseratio of the gained image has been improved and better effect is got comparing with moment matching method by bands. While strip noise is weakened well, the radiative feature of original image is retained.