{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000256961,0.00009133782,0.0001897262,0.0001930063,0.00006206754,0.00003173192,0.00008449583,0.0001022019,0.000001046867],"category_scores_gemma":[0.00006318193,0.00008644423,0.00001668312,0.0007316588,0.00004147869,0.0001318886,0.00001944408,0.0001237035,3.025842e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003645617,"about_ca_system_score_gemma":0.00005192244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001209106,"about_ca_topic_score_gemma":0.00006680353,"domain_scores_codex":[0.9992076,0.00001159449,0.0004331182,0.00008404654,0.0001485075,0.0001151121],"domain_scores_gemma":[0.9993543,0.0001051308,0.0001314389,0.0001562752,0.000215209,0.00003766891],"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":[0.00001823218,0.000007696158,0.0005343245,0.00008742633,0.00003349909,4.00564e-7,0.00008015211,0.0004522921,0.004928418,0.0002842164,0.0003160197,0.9932573],"study_design_scores_gemma":[0.00048567,0.00002519614,0.08540133,0.00005082955,0.00003280103,0.00002277768,0.00004931381,0.8570127,0.001687331,0.0008602882,0.05427371,0.00009806973],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.148287,0.00007467836,0.8508751,0.0002287607,0.00005560947,0.0002072948,0.00006331372,0.00004301978,0.0001652141],"genre_scores_gemma":[0.6867889,0.0002223568,0.3127912,0.00002607987,0.00006009582,6.845698e-8,0.0001010259,0.000007583114,0.00000269579],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9931592,"threshold_uncertainty_score":0.3525093,"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."}}