Spatiotemporal imagery selection for full coverage image generation over a large area with HFA-Net based quality grading

• Published in: Geo-spatial information science 2024-09, Vol.27 (5), p.1524-1541
• Main Authors: Pan, Jun, Chen, Liangyu, Shu, Qidi, Zhao, Qiang, Yang, Jin, Jin, Shuying
• Format: Article
• Language: English
• Subjects: Algorithms; Computer vision; Constraints; full coverage image generation; High-Frequency-Aware (HFA)-Net; Image processing; Image quality; Image selection; Methods; Mosaics; Pixels; regional quality grading; Remote sensing; Retrieval; Semantics; spatiotemporal constraints; Spatiotemporal data
• ISSN: 1009-5020; 1993-5153
• DOI: 10.1080/10095020.2023.2270641

Remote sensing images often need to be merged into a larger mosaic image to support analysis on large areas in many applications. However, the performance of the mosaic imagery may be severely restricted if there are many areas with cloud coverage or if these images used for merging have a long-time span. Therefore, this paper proposes a method of image selection for full coverage image (i.e. a mosaic image with no cloud-contaminated pixels) generation. Specifically, a novel High-Frequency-Aware (HFA)-Net based on Swin-Transformer for region quality grading is presented to provide a data basis for image selection. Spatiotemporal constraints are presented to optimize the image selection. In the temporal dimension, the shortest-time-span constraint shortens the time span of the selected images, obviously improving the timeliness of the image selection results (i.e. with a shorter time span). In the spatial dimension, a spatial continuity constraint is proposed to select data with better quality and larger area, thus improving the radiometric continuity of the results. Experiments on the GF-1 images indicate that the proposed method reduces the averages by 76.1% and 38.7% in terms of the shortest time span compared to the Improved Coverage-oriented Retrieval algorithm (MICR) and Retrieval Method based on Grid Compensation (RMGC) methods, respectively. Moreover, the proposed method also reduces the residual cloud amount by an average of 91.2%, 89.8%, and 83.4% when compared to the MICR, RMGC, and Pixel-based Time-series Synthesis Method (PTSM) methods, respectively.