{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009695271,0.0001862337,0.0002029191,0.00009710371,0.0000557618,0.00004471877,0.0001699152,0.0000625448,0.000008881606],"category_scores_gemma":[0.0001114082,0.0002130673,0.00008985891,0.0003010765,0.00003103169,0.0000863465,0.00001652224,0.0003155185,0.0000292975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005956555,"about_ca_system_score_gemma":0.00002249128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002892463,"about_ca_topic_score_gemma":2.713617e-7,"domain_scores_codex":[0.9987787,0.00003883849,0.00036186,0.0003220825,0.0002340687,0.0002644479],"domain_scores_gemma":[0.9994138,0.00007611314,0.00007663295,0.0002674445,0.000065025,0.0001010181],"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":[0.00001027043,0.00001431938,0.00004835086,0.00003644411,0.00000380302,0.000005329967,0.000130683,0.6313865,0.3660983,0.00004583668,0.00008804464,0.002132139],"study_design_scores_gemma":[0.0002721602,0.00005391491,0.0008911188,0.00003666893,0.00001277222,0.000003336817,0.00004882557,0.9554164,0.04304087,0.00001801662,0.00004851377,0.0001574436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.644241,0.00001296063,0.3450424,0.0008901374,0.0001815017,0.0002116396,0.000002629283,0.0006533246,0.008764375],"genre_scores_gemma":[0.9673529,0.000002421088,0.03213063,0.0001875351,0.0002558189,9.957064e-7,0.000007524896,0.00005875998,0.000003392293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3240299,"threshold_uncertainty_score":0.8688633,"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."}}