Enhancing the Swin Transformer With Spatiotemporal Feature Correction for Time-Series Image Segmentation

Time-series images with rich spatiotemporal features contain comprehensive and accurate context information for image segmentation. Due to the variability of time-series images, a random offset phenomenon may occur in targets, interfering with the continuity of temporal features. Although windowed attention mechanisms are adopted to capture the complete image information, they are prone to triggering the edge-jagged phenomenon. To address the above issues, this article presents a Swin transformer with spatiotemporal feature correction (SwinTSFC) for the semantic segmentation of time-series images. A convolutional long-short-term memory (ConvLSTM) module with dynamic correction is proposed to adjust the target deviation of temporal data by capturing the offset relationship among sequences. It learns image semantic association and maintains object alignment among dynamic data. A global-to-local learning strategy is adopted to extract spatial features. Swin transformer blocks are adopted to capture the long-range dependencies of images by strengthening interaction capabilities among windows and to improve the overall recognition ability of SwinTSFC. Self-calibrated convolution (SCConv) adaptively extracts fine-grained information to optimize edge continuity features and overcome the phenomenon of edge-jagged. The superiority of the SwinTSFC to state-of-the-art algorithms is demonstrated via experimentation. The code is available at: https://github.com/fjc1575/Marine-Aquaculture/tree/main/SwinTSFC © 2025 IEEE.