{"id":"W4283017199","doi":"10.1142/s0219691322500254","title":"Hyperspectral imagery classification with minimum noise fraction, 2D spatial filtering and SVM","year":2022,"lang":"en","type":"article","venue":"International Journal of Wavelets Multiresolution and Information Processing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Space Agency; Concordia University","funders":"","keywords":"Hyperspectral imaging; Support vector machine; Data cube; Pattern recognition (psychology); Artificial intelligence; Cube (algebra); Noise (video); Computer science; Pixel; Spatial analysis; Curse of dimensionality; Remote sensing; Image (mathematics); Data mining; Mathematics; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002878465,0.0001334109,0.0001382143,0.0004312974,0.0002304454,0.0002707334,0.0001226084,0.00004096503,0.00002211435],"category_scores_gemma":[0.0000809422,0.0001295772,0.00003151738,0.0001342834,0.00006975204,0.003254014,0.00003918086,0.0003011572,0.000002407916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003170706,"about_ca_system_score_gemma":0.00007289827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001164757,"about_ca_topic_score_gemma":0.000001442967,"domain_scores_codex":[0.9986701,0.00003264055,0.0005506203,0.00009265052,0.0005192281,0.0001347403],"domain_scores_gemma":[0.9988751,0.00003510123,0.0004416681,0.00006844188,0.0005054412,0.00007422909],"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":[0.0005638947,0.00009079873,0.001613853,0.0001608764,0.0001451799,0.00002529683,0.004803126,0.02431248,0.1331727,0.0002135284,0.001011739,0.8338865],"study_design_scores_gemma":[0.001365425,0.00009796522,0.05006021,0.0001058825,0.00002870014,0.001246568,0.002193337,0.9295371,0.002893485,0.0000396141,0.01219542,0.0002363362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7037864,0.0002923925,0.2922415,0.001497802,0.0007314897,0.0001656066,0.00001955096,0.0001118934,0.001153391],"genre_scores_gemma":[0.9907787,0.0001387536,0.008688761,0.0001700914,0.0001498495,0.000004623048,0.00003661159,0.00001505599,0.00001755441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9052246,"threshold_uncertainty_score":0.5284003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01093994360670935,"score_gpt":0.2264873048338828,"score_spread":0.2155473612271735,"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."}}