{"id":"W2015035081","doi":"10.1109/igarss.2014.6946601","title":"PolSAR image segmentation — Advanced statistical modelling versus simple feature extraction","year":2014,"lang":"en","type":"article","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Space Agency; Danmarks Tekniske Universitet; European Space Agency","keywords":"Wishart distribution; Artificial intelligence; Pattern recognition (psychology); Computer science; Segmentation; Image segmentation; Feature extraction; Feature (linguistics); Covariance matrix; Simple (philosophy); Image (mathematics); Scale-space segmentation; Computer vision; Algorithm; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008979352,0.0001475518,0.0001247384,0.00005128208,0.0000773174,0.00003822957,0.00007840858,0.00009612204,0.0002475824],"category_scores_gemma":[0.00002359204,0.0001392649,0.00003363422,0.00008935526,0.00002389828,0.0001631417,0.00001063003,0.0001477038,0.00008333135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007534885,"about_ca_system_score_gemma":0.000005473242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002606614,"about_ca_topic_score_gemma":0.0000168139,"domain_scores_codex":[0.9993154,0.00001667491,0.0001528789,0.0001870145,0.0001397368,0.0001883017],"domain_scores_gemma":[0.9994178,0.0002037043,0.00002581709,0.0002478888,0.00003813237,0.00006663865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006098612,0.00005351404,0.000008157334,0.00005545317,0.00004239469,5.499679e-7,0.0001093918,0.004706333,0.06183079,0.03527248,0.01851333,0.8793466],"study_design_scores_gemma":[0.0003413326,0.00003217143,0.00003529132,0.000007526547,0.00002498694,0.000004625625,0.00007065439,0.2838761,0.02551855,0.003464371,0.6863895,0.0002348444],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002581332,0.00004188515,0.9796525,0.0001478631,0.0001215823,0.0002000879,0.00001940215,0.0007347413,0.01650065],"genre_scores_gemma":[0.1973894,0.00005371479,0.8021201,0.0000280509,0.00007813471,0.0000332415,0.0001252842,0.0000330635,0.0001389696],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8791118,"threshold_uncertainty_score":0.5679059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01299922238299683,"score_gpt":0.2791917938971529,"score_spread":0.266192571514156,"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."}}