{"id":"W4362683587","doi":"10.1109/jstars.2023.3264452","title":"Local and Global Spatial Information for Land Cover Semisupervised Classification of Complex Polarimetric SAR Data","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Land cover; Synthetic aperture radar; Computer science; Pixel; Remote sensing; Contextual image classification; Polarimetry; Pattern recognition (psychology); Artificial intelligence; Spatial analysis; Dependency (UML); Graph; Geography; Land use; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004985841,0.0004543309,0.0003960761,0.001294859,0.0002744466,0.0004431975,0.0006280204,0.0004372813,0.0004963211],"category_scores_gemma":[0.001076369,0.0001678288,0.0003722126,0.0008593185,0.0004728123,0.0008014575,0.0004498589,0.0004318993,0.0002770276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004659616,"about_ca_system_score_gemma":0.0004759024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003595326,"about_ca_topic_score_gemma":0.007042827,"domain_scores_codex":[0.9997284,0.00007528546,0.00001406729,0.00008968268,0.00005820651,0.0000344316],"domain_scores_gemma":[0.999217,0.0003245584,0.0001670986,0.0001216211,0.0001316852,0.00003793127],"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":[0.0005233388,0.0003749019,0.01641473,0.0001739143,0.0001391736,0.0002221038,0.0002677036,0.3493925,0.0405058,0.007000079,0.003845167,0.5811406],"study_design_scores_gemma":[0.000005890897,0.00002564545,0.002764265,0.000004758607,0.00001128984,0.00002609779,0.0000380294,0.9905102,0.003929234,0.002269311,0.0004096988,0.000005627704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3056464,0.000520961,0.6901702,0.0002744159,0.0000305863,0.00007665875,0.0004912858,0.001029577,0.001760038],"genre_scores_gemma":[0.863344,0.000155092,0.1334306,0.00008380217,0.00005248765,0.00009484165,0.001301836,0.00007606626,0.00146113],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003595326,"threshold_uncertainty_score":0.007148802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04417509823531299,"score_gpt":0.2587872421172209,"score_spread":0.2146121438819079,"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."}}