{"id":"W4381735870","doi":"10.1109/jstars.2023.3288143","title":"An Effective Multimodel Fusion Method for SAR and Optical Remote Sensing Images","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"State Key Laboratory of Remote Sensing Science; National Natural Science Foundation of China; China Postdoctoral Science Foundation; Ministry of Natural Resources of the People's Republic of China; Natural Science Foundation of Jiangsu Province; Government of Jiangsu Province; Ministry of Natural Resources","keywords":"Synthetic aperture radar; Remote sensing; Computer science; Image fusion; Artificial intelligence; Fusion; Computer vision; Sensor fusion; Image resolution; Pattern recognition (psychology); Image (mathematics); Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008954019,0.0008556475,0.0007119736,0.0008210243,0.0004419041,0.0005446388,0.0008696313,0.001011668,0.001349912],"category_scores_gemma":[0.001086577,0.0004154793,0.001471554,0.0006569902,0.0003169006,0.001207513,0.0009317056,0.0008627056,0.0003238003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005622579,"about_ca_system_score_gemma":0.0008431487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005570804,"about_ca_topic_score_gemma":0.004972524,"domain_scores_codex":[0.9995121,0.0000789969,0.00003206579,0.0001395081,0.0001944088,0.0000428854],"domain_scores_gemma":[0.9997925,0.0000529125,0.00002924001,0.00002117112,0.0000906697,0.00001350153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001495086,0.0000805255,0.001076721,0.000137884,0.0001703043,0.0001177919,0.0001522775,0.4405829,0.02905871,0.008065136,0.00163848,0.5187697],"study_design_scores_gemma":[0.000003121935,0.00002007766,0.0001901235,0.000003505122,0.00001046177,0.00001920607,0.000006370619,0.9963426,0.001809122,0.001035884,0.0005529269,0.000006554103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008119013,0.0001685344,0.9907119,0.00005862466,0.00003196,0.00002351565,0.00002547305,0.0002909376,0.0005700834],"genre_scores_gemma":[0.3770576,0.0003323408,0.618425,0.0001487096,0.00007572959,0.0001380042,0.0002922917,0.0001128234,0.003417459],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005570804,"threshold_uncertainty_score":0.01107675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01920676378796429,"score_gpt":0.2884392038431857,"score_spread":0.2692324400552214,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}