DEBLURRING STUDY OF DMSP/OLS NIGHTTIME LIGHT DATA BY RTSVD

Abstract. DMSP/OLS, as the earliest Nighttime light remote sensing data, has great application value and can greatly improve the data quality by solving the blurring problem existing in the data. The blur reason is analyzed, and a new algorithm of regularization truncated singular value decomposition (RTSVD) combining with Pct image luminescence frequency filtering is proposed, which can effectively eliminate the blurring phenomenon and retain the real information of the image. Firstly, considering that the luminescence frequency of the light source pixel must be higher than that of the non-light source pixel, the luminescence frequency of the pixel in the Pct image is used to exclude the non-light source pixel in the average light image, and then the truncation parameter of the regularized truncation singular value decomposition (RTSVD) is obtained by using the L curve, so as to decompose and recombine the image. The experiments show that the regularized truncation singular value decomposition method combined with Pct image luminescence frequency filtering can remove the blurring phenomenon on the basis of preserving the image information.