Abstract
Aim to striping noise brought by non-uniform response of remote sensing TDI CCD, a novel de-striping method based on statistical features of image histogram is put forward. By analysing the distribution of histograms,the centroid of histogram is selected to be an eigenvalue representing uniformity of ground objects,histogrammic centroid of whole image and each pixels are calculated first,the differences between them are regard as rough correction coefficients, then in order to avoid the sensitivity caused by single parameter and considering the strong continuity and pertinence of ground objects between two adjacent pixels,correlation coefficient of the histograms is introduces to reflect the similarities between them,fine correction coefficient is obtained by searching around the rough correction coefficient,additionally,in view of the influence of bright cloud on histogram,an automatic cloud detection based on multi-feature including grey level,texture,fractal dimension and edge is used to pre-process image.Two 0-level panchromatic images of SJ-9A satellite with obvious strip noise are processed by proposed method to evaluate the performance, results show that the visual quality of images are improved because the strip noise is entirely removed,we quantitatively analyse the result by calculating the non-uniformity ,which has reached about 1% and is better than histogram matching method.
Highlights
INTRODUCTIONStripe noises of TDI-CCD image is the phenomenon of gray value stochastic change along the array when illuminated by the same uniform light source,the essence of this phenomenon is caused by response non-uniformity of sensors during imaging.Stripe noises influence the quality and quantitative application of image products.Removing stripe noise is accomplished by relative radiometric correction
Stripe noises of TDI-CCD image is the phenomenon of gray value stochastic change along the array when illuminated by the same uniform light source,the essence of this phenomenon is caused by response non-uniformity of sensors during imaging.Stripe noises influence the quality and quantitative application of image products.Removing stripe noise is accomplished by relative radiometric correction.At present, there are two kinds of de-striping methods: the radiometric calibration method and the scene statistic method (Cao Juliang,2004a)
The scene statistic method includes histogram matching based on a mass of raw images and moment matching method based on scenery each
Summary
Stripe noises of TDI-CCD image is the phenomenon of gray value stochastic change along the array when illuminated by the same uniform light source,the essence of this phenomenon is caused by response non-uniformity of sensors during imaging.Stripe noises influence the quality and quantitative application of image products.Removing stripe noise is accomplished by relative radiometric correction. 2. PRINCIPLE AND MODEL 2.1 Histogram statistical principle The gray level is continuous for symmetrical ground objects(Figure 1(a)),so the shape of each pixel and whole pixels is alike if the amount of row is big enough(Figure 1(b)、Figure 1(c)、Figure 1(d)).It has been recognized that the content of highest grey level is bring by objects with high reflectance such as cloud and snow,on the contrary, the content of lowest grey level with low reflectance such as water. PRINCIPLE AND MODEL 2.1 Histogram statistical principle The gray level is continuous for symmetrical ground objects(Figure 1(a)),so the shape of each pixel and whole pixels is alike if the amount of row is big enough(Figure 1(b)、Figure 1(c)、Figure 1(d)).It has been recognized that the content of highest grey level is bring by objects with high reflectance such as cloud and snow,on the contrary, the content of lowest grey level with low reflectance such as water To the whole image,histogram of pixel one is regard as a first standard,histogram of pixel two matchs with it, standard changes to histogram of pixel two, histogram of pixel three matchs with it,the rest pixels are processed in the same manner one by one
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