{"id":"W3196006465","doi":"10.1080/01431161.2021.1939910","title":"Deep support vector machine for PolSAR image classification","year":2021,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Support vector machine; Pattern recognition (psychology); Computer science; Artificial neural network; Confusion matrix; Synthetic aperture radar; Parametric statistics; Contextual image classification; Mathematics; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007177441,0.000779307,0.0007309947,0.0007482094,0.0002107,0.0006825074,0.0007161601,0.0008129203,0.002028114],"category_scores_gemma":[0.002246332,0.0002729479,0.000589862,0.001297742,0.0002584543,0.0007522986,0.0004874757,0.001318546,0.00100364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004879153,"about_ca_system_score_gemma":0.0006113423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004198018,"about_ca_topic_score_gemma":0.002668037,"domain_scores_codex":[0.9994721,0.0001194736,0.0000494037,0.0001058726,0.0001948021,0.00005830062],"domain_scores_gemma":[0.9992686,0.0002995357,0.00007693755,0.00007244501,0.0002627149,0.0000198428],"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":[0.000128297,0.00009480782,0.001248997,0.0001915306,0.00006638692,0.00009157107,0.00005018982,0.297274,0.01129782,0.005859986,0.007392787,0.6763036],"study_design_scores_gemma":[0.000003031541,0.00001941547,0.0002994654,0.000008381337,0.000003787093,0.00001402369,0.000005782374,0.9951002,0.0015012,0.001871406,0.001166864,0.000006427879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02149575,0.002343533,0.9707868,0.0004062201,0.0001169695,0.00005595414,0.0004754709,0.002497342,0.001821989],"genre_scores_gemma":[0.6047621,0.002062676,0.3826128,0.000240439,0.000185759,0.000245636,0.00267725,0.000151095,0.007062163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004198018,"threshold_uncertainty_score":0.008347154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01252354266699032,"score_gpt":0.2706508899775248,"score_spread":0.2581273473105344,"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."}}