Attitude jitter is a crucial factor that limits the imaging quality and geo-positioning accuracy of high-resolution optical satellites, which has attracted significant research interests in recent years. However, few researchers have attempted to retrieve the dynamic characteristics and time-varying trends of a satellite attitude jitter. This paper presents a novel processing framework for detecting, estimating, and investigating time-varying attitude jitter in long strips based on a time-frequency analysis with the input from either an attitude sensor or an optical imaging sensor. Attitude angle signals containing attitude jitter information are detected from attitude data through generating the Euler angles relative to the orbit coordinate system, or from image data through high-accuracy dense matching between parallax observations, correction of integration time variation and frequency domain-based deconvolution. Variational mode decomposition is adopted to extract the separate band-limited periodic components, and Hilbert spectral analysis is integrated to estimate the instantaneous attributes for each time sample and the varying trends for the entire duration. Experiments with three sets of ZiYuan-3 long-strip datasets were carried out to test the novel processing framework of attitude jitter. The experimental results indicate that the processing framework could reveal the dynamic jitter characteristics, and the mutual validations of different data sources demonstrate the effectiveness of the proposed method.