{"id":"W3205581890","doi":"10.3390/rs13204102","title":"Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Endmember; Hyperspectral imaging; Anomaly detection; Pattern recognition (psychology); Sparse approximation; Computer science; Pixel; Artificial intelligence; Representation (politics); Spectral signature; Anomaly (physics); Remote sensing; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004989795,0.0005854266,0.0004571778,0.0008710059,0.0002465142,0.00046711,0.0005450129,0.0005787825,0.0009169237],"category_scores_gemma":[0.001401343,0.0001814679,0.000549314,0.001013364,0.0005243876,0.0008782013,0.0009177515,0.00106384,0.0004940508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002733605,"about_ca_system_score_gemma":0.000499076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001350545,"about_ca_topic_score_gemma":0.001548041,"domain_scores_codex":[0.9995838,0.00008917177,0.00001811147,0.00009195712,0.0001830616,0.00003399625],"domain_scores_gemma":[0.9995292,0.0001674739,0.00007996201,0.00007598369,0.0001236622,0.00002369896],"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":[0.0001645136,0.0001983149,0.002000211,0.000190537,0.0001014649,0.0001331512,0.0001564586,0.2099474,0.07748146,0.03120449,0.00444768,0.6739743],"study_design_scores_gemma":[0.000004242493,0.00002639123,0.0003715205,0.000005259965,0.000008304155,0.00005580585,0.00001445365,0.9864699,0.006582771,0.005341152,0.001111039,0.000009222102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008957198,0.0001223786,0.9899731,0.00008069757,0.00001515352,0.00001547986,0.00003942658,0.0002273773,0.0005692024],"genre_scores_gemma":[0.2943819,0.0005758855,0.7012595,0.0001848462,0.0001179403,0.0001083476,0.0007499504,0.00009900156,0.002522622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001350545,"threshold_uncertainty_score":0.003067434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02253133342749227,"score_gpt":0.2552358408634938,"score_spread":0.2327045074360015,"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."}}