{"id":"W3024521712","doi":"10.1016/j.neucom.2020.04.138","title":"Hyperspectral image classification based on sparse modeling of spectral blocks","year":2020,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hyperspectral imaging; Discriminative model; Computer science; Benchmark (surveying); Pattern recognition (psychology); Artificial intelligence; Full spectral imaging; Exploit; Sparse approximation; Spatial analysis; Remote sensing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002313639,0.0003749872,0.0004706019,0.0004856417,0.000198497,0.0004775941,0.0004464066,0.0004423696,0.001061345],"category_scores_gemma":[0.0009147139,0.0001895106,0.0004718213,0.0006403902,0.0002871825,0.0008848386,0.00043684,0.0006163248,0.0005203002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002003144,"about_ca_system_score_gemma":0.0003479102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001852014,"about_ca_topic_score_gemma":0.002890866,"domain_scores_codex":[0.999817,0.000042504,0.000006937821,0.00003306716,0.00007710439,0.00002350874],"domain_scores_gemma":[0.9997199,0.00009429253,0.00004652943,0.00004816905,0.00007510182,0.00001601357],"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":[0.0002728722,0.0002213654,0.001569982,0.0001224924,0.00009512246,0.0000866946,0.0001158592,0.4316474,0.1009279,0.01798977,0.003510419,0.4434402],"study_design_scores_gemma":[0.000001688772,0.00001261703,0.0002030568,0.000002197107,0.000005031597,0.00001751455,0.000004771114,0.9957261,0.002071142,0.001666356,0.0002865795,0.000003074493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03161968,0.0001418761,0.9666489,0.0001281212,0.00002405183,0.00001804136,0.00009515134,0.0002483714,0.001075739],"genre_scores_gemma":[0.6722454,0.0005334424,0.3219972,0.0001521899,0.0001136277,0.00009882158,0.0008209745,0.00009085544,0.003947344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001852014,"threshold_uncertainty_score":0.003682494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04120244498329782,"score_gpt":0.2334189012969485,"score_spread":0.1922164563136507,"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."}}