{"id":"W1979237284","doi":"10.1109/igarss.2010.5651992","title":"Unsupervised nonparametric classification of polarimetric SAR data using the K-nearest neighbor graph","year":2010,"lang":"en","type":"article","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Initialization; Cluster analysis; Feature vector; Pattern recognition (psychology); Computer science; Synthetic aperture radar; k-nearest neighbors algorithm; Polarimetry; Artificial intelligence; 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":[0.000785413,0.0004699675,0.0007447731,0.002359004,0.0004150419,0.001028185,0.001133487,0.000847353,0.0005811834],"category_scores_gemma":[0.003828046,0.000291269,0.0006020399,0.001906011,0.0008755469,0.001371394,0.0007600319,0.0007384233,0.0003900561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000784633,"about_ca_system_score_gemma":0.0005121292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003587642,"about_ca_topic_score_gemma":0.005432752,"domain_scores_codex":[0.999072,0.0003122431,0.00004422902,0.0002287637,0.0002978195,0.00004496453],"domain_scores_gemma":[0.9979387,0.001113081,0.0002530824,0.0003724789,0.0002814693,0.00004124198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001604765,0.000157048,0.004728561,0.0001431513,0.00008736646,0.0001071658,0.0002174932,0.5544768,0.008048403,0.01848307,0.003347217,0.4100432],"study_design_scores_gemma":[0.000003953988,0.00001687376,0.001188527,0.000005322855,0.000004906413,0.00003237032,0.00002459621,0.9870391,0.0009026083,0.01016933,0.0006031037,0.000009220025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02269163,0.0001047614,0.9758291,0.00008494504,0.00001413912,0.00003745123,0.0001727258,0.000383002,0.0006823405],"genre_scores_gemma":[0.485533,0.000341686,0.510082,0.000104814,0.00008342839,0.0001688843,0.001650774,0.0001477168,0.001887739],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003587642,"threshold_uncertainty_score":0.007133484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04144427527425655,"score_gpt":0.2766386766857249,"score_spread":0.2351944014114684,"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."}}