Python code of our paper "OBSUM: An object-based spatial unmixing model for spatiotemporal fusion of remote sensing images" (https://www.sciencedirect.com/science/article/pii/S0034425724000579#ab0010)
OBSUM can fuse images with different spatial and temporal resolutions, e.g., Sentinel-2 and Sentinel-3, to generate images with both high spatial and temporal resolutions, i.e., daily Sentinel-2-like images.
If you found our paper useful, please kindly cite: H. Guo, D. Ye, H. Xu, and L. Bruzzone, "OBSUM: An object-based spatial unmixing model for spatiotemporal fusion of remote sensing images," Remote Sensing of Environment, vol. 304, p. 114046, 2024, doi: https://doi.org/10.1016/j.rse.2024.114046.
Please feel free to contact with me if you have any trouble in running the OBSUM code: houcai.guo@unitn.it
Please visit https://github.com/facebookresearch/segment-anything for detailed guidance on installing the Segment Anything Model (SAM) and downloading the model checkpoint.
If you prefer the Multiresolution Segmentation in the eCognition software, please use the raw image instead of the surface reflectance for segmentation.
Dataset (Fig. 4 and 5 in the paper, 2.17 GB): https://pan.baidu.com/s/1GZ9t628ncmFGz3D4aVfvWg?pwd=0416
All fused images (Fig. 11 and 12 in the paper, 4.61 GB): https://pan.baidu.com/s/1_CNq-xOxwdmTW3CNssHffw?pwd=0416
Note: Please visit https://www.lfd.uci.edu/~gohlke/pythonlibs/ to download the .whl file for GDAL and Rasterio (if needed).