A Modified Nonlinear Two-Filter Smoothing for High-Precision Airborne Integrated GPS and Inertial Navigation Airborne remote sensing imaging depends on the integrated system of strapdown inertial navigation system (SINS) and Global Positioning System (GPS) to obtain high-accuracy motion parameters. In this paper, a modified nonlinear two-filter smoother (TFS) is proposed for an offline SINS/GPSintegrated system suitable for remote sensing imaging. The proposed smoother has a two-filter structure, which includes a forward filter based on central difference Kalman filter, a backward filter with modified propagation and update equation, and a smoothing algorithm. The smoothing algorithm with the modified backward filter is conducted by the simulation and the data processing of SINS/GPSintegrated system flight test. Furthermore, a digital camera is used to verify the precision of practical applications in a check field with numerous reference points. In these tests, the performance of the proposed smoother is compared with the central difference Rauch-Tung-Striebel smoother (RTSS), extended TFS, and extended RTSS.