As an extension of hesitant fuzzy set, the probabilistic hesitant fuzzy set (PHFS) can more accurately express the initial decision information given by experts, thus the decision method based on PHFS is more true and reliable. In this paper, multi-attribute decision-making (MADM) method is proposed under probabilistic hesitant fuzzy environment, which is based the new distance measures of probabilistic hesitant fuzzy elements (PHFEs) and the COmplex PRoportional ASsessment (COPRAS) method. Firstly, the existing problems of some distances are analyzed and we propose some new distance measures including new Hamming distance, new Euclidean distance and new generalized distance under probabilistic hesitant fuzzy environment. Secondly, a maximizing deviation method based on the new Hamming distance measure is proposed to obtain the attribute weights in probabilistic hesitant fuzzy information. Then, the COPRAS method is extended to solve MADM problems under probabilistic hesitant fuzzy environment. Finally, compared other methods, an example is given to demonstrate the effectiveness of the proposed method.

The linear array CCD camera is the main sensor on the push-broom satellite. Because of the difference response among the CCD detectors, the striping noise is an obvious phenomenon in the remote sensing image along the scanning direction, which can seriously affect the image quality and quantitative application. The object of relative radiometric calibration is to eliminate them.As the state of satellite electronics varies from orbit to orbit, an automatic de-striping algorithm is needed that depends only on information that can be attained from the image data. There are many published techniques that are used to remove striping from images such as the histogram matching, histogram equalization, and Fourier transform filter methods.In order to decrease the effect we try to remove these stripes in CCD images using a relative radiometric correction algorithm based on the adaptive filtering pattern in this paper. Firstly, aiming at the characteristics of strip noise in push-broom scanner, the cause of strip noise formation is described. The suitable 1-D nonlinear filter is chosen to remove the obvious stripping based on the stripping distribution. Then, 1-D smoothing filtering is used to calculate the gain and offset coefficients. At last, the thin stripping is de-striped with the obtained coefficients.The final results indicate that the proposed method can effectively remove the stripping noise along the scanning direction effectively. Comparison of mean value and standard deviations obtained from the strip noise removed image by the proposed method and histogram equalization method suggested that the proposed method is evidently superior to the traditional histogram equalization method in preserving the image detail very well. The result of this study is applicable in striping removal of push-broom satellite's remote sensing data.