If you are interested in some methods which I developed for data fusion, cloud removal, and ETM+ image gap filling and others, please download the following codes and test data. These codes are subject to update.
(1) the code (IDL and Python versions) and test data for ESTARFM algorithm (enhanced spatial and temporal adaptive reflectance fusion model, includes a fast version)
(1) the code (IDL and Python versions) and test data for ESTARFM algorithm (enhanced spatial and temporal adaptive reflectance fusion model, includes a fast version)
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(2) the IDL code and test data for NSPI algorithm (a Neighborhood Similar Pixel Interpolator approach for filling gaps in Landsat ETM+ SLC-off images)
| NSPI_update_20100824.zip |
(3) the IDL code and test data for GNSPI algorithm (a geostatistical approach for filling gaps in Landsat ETM+ SLC-off images)
| GNSPI_update_20130317.zip |
(4) the IDL code and test data for Cloud Remove algorithm (a modified NSPI approach for removing thick clouds in satellite images)
| MNSPI_cloud_remove_update_20210507.zip |
(5) the code (IDL and Python versions) and test data for fusing satellite images with different spatial and temporal resolutions (a Flexible Spatiotemporal DAta Fusion (FSDAF) method)
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(6) the code (IDL and Python versions) and test data for interpolating missing pixels in all images of a time series (NSPI time series)
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_files/output-22rdxl_1.gif)
Left: original Landsat time series; Right: reconstructed Landsat time series
(7) the code (IDL version and Python version) and test data for ATSA algorithm (automatic screening of cloud and cloud shadow in optical image time series): download both test data zip files before unzip
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(8) the Matlab code and test data for SEAM algorithm (A self-adjusting model for correcting the blooming effects in DMSP-OLS nighttime light images)
| SEAM_update_20190417.zip |
(9) A package including all codes (ATSA, buffer and recode ATSA mask, and NSPI) needed for automatically reconstructing high-quality time-series images (IDL and Python versions)
| Package_for_time_series_reconstruction_ATSA_buffer_NSPI_.zip |
(10) Code for computing all-round performance assessment (APA) metrics of fused images (IDL and Python codes) and R code for drawing the APA diagram
| idl_code_and_sample_data_for_computing_accuracy_metrics.zip |
| python_code_and_sample_data_for_computing_accuracy_metrics.zip |
| r_code_and_sample_data_for_drawing_apa_diagram.zip |
(11) Code of computing SAR-based Paddy Rice Index-SPRI (R and Python versions)
| python_code_rice_mapping_spri_20221202.zip |
| r_code_rice_mapping_spri_20220520.zip |
(12) the Python code and test data for CRYSTAL algorithm (a method to remove clouds in daily nighttime light images by synergizing spatiotemporal information)
| CRYSTAL_20230626.rar |