S1, judging original elevation data to obtain wrong point data and normal data;

wherein judging the original elevation data comprises:

judging the original elevation data according to orders of from a first to a last and from the last to the first of a survey line by using a judging index method based on a Mahalanobis distance, and respectively obtaining wrong point data sets A^forward and A^inverse;

wherein a judging index based on the Mahalanobis distance is:

wherein y_k is the judging index, y_k is an actual measured value of the elevation, ŷ_k⁻ is a predicted measured value of the elevation, and P_ŷk⁻ is a covariance of ŷ_k⁻;

fusing the wrong point data sets A^forard and A^inverse, taking an intersection of the two data sets as the wrong point data, and taking data except the wrong point data in the original elevation data as the normal data;

S2, filtering the wrong point data by an improved Kalman filtering method, and filtering the normal data by a conventional Kalman filtering method;

wherein the improved Kalman filtering method is:

wherein P_ŷk⁻ is a covariance of an adjusted observation and prediction value, k_k is an adjusting coefficient, P_k⁺ is a covariance of a posterior estimation value, K_k is a Kalman filtering coefficient, H_k is an observation matrix, P_k⁻ is a covariance between a real value and a prediction value, R_k is a calculated auxiliary parameter of P_k⁺, K_k^T is a transposition of the Kalman filtering coefficient, y_k is the judging index, χ is a preset threshold, H_k^T is a transposition of the observation matrix, and R_k is a covariance of an observation noise matrix;

S3, fusing a filtering result of the wrong point data and a filtering result of the normal data, and taking a fused result as a measured value sequence used in a next working process; and

S4, repeating the S1-S3 until the fused result meets preset requirements, and completing an effective estimation of a surface elevation.