{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006969184,0.0007605546,0.0008092387,0.001105477,0.0002152203,0.001415178,0.0005661378,0.0008869692,0.003298931],"category_scores_gemma":[0.0009511082,0.0003759212,0.0006103794,0.001737049,0.0006797785,0.001557828,0.0004799252,0.0005182368,0.002455455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004001769,"about_ca_system_score_gemma":0.0003516834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001477258,"about_ca_topic_score_gemma":0.002178963,"domain_scores_codex":[0.9995103,0.00009298342,0.00003248879,0.0001407028,0.0001824117,0.00004110832],"domain_scores_gemma":[0.9995177,0.000177972,0.00006278978,0.0001349397,0.00009507431,0.00001154098],"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":[0.0001513424,0.00005522046,0.001072117,0.0002733396,0.00006883343,0.0001225123,0.00009814798,0.08087404,0.1420006,0.02672349,0.005248285,0.7433122],"study_design_scores_gemma":[0.00002065607,0.000116705,0.006190465,0.00006179057,0.00004980548,0.0005266654,0.00007772642,0.8140551,0.1162826,0.02706897,0.03546108,0.00008854062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006636544,0.0004499219,0.988923,0.0001315762,0.00003416114,0.00002957254,0.0001586338,0.00130781,0.002328841],"genre_scores_gemma":[0.1175031,0.00144465,0.8715094,0.0001997817,0.0001432017,0.00007674351,0.001415794,0.0006841813,0.007023189],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003298931,"threshold_uncertainty_score":0.01103604,"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."}}