{"id":"W2745791577","doi":"10.3390/rs9090872","title":"Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification","year":2017,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Università degli Studi di Pavia; National Natural Science Foundation of China; Jet Propulsion Laboratory; Purdue University; National Aeronautics and Space Administration","keywords":"Pattern recognition (psychology); Sparse approximation; Computer science; Hyperspectral imaging; Pixel; Artificial intelligence; Classifier (UML); Representation (politics)","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":[0.0007273572,0.0004591523,0.000755403,0.000842548,0.0002429149,0.0004281305,0.0007335946,0.000613709,0.001051618],"category_scores_gemma":[0.001971636,0.0002119237,0.0007124147,0.001236178,0.0004013675,0.0009578089,0.000709139,0.0007759735,0.0003941196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003168145,"about_ca_system_score_gemma":0.0003835054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001962473,"about_ca_topic_score_gemma":0.001795156,"domain_scores_codex":[0.9994936,0.0001273971,0.00002306796,0.0001150905,0.0001937772,0.0000470179],"domain_scores_gemma":[0.9994349,0.000224573,0.0000976105,0.00007653517,0.0001427804,0.00002368917],"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":[0.0002452095,0.0001366495,0.001668062,0.0001706956,0.0001035126,0.0001595911,0.0001703482,0.2208393,0.05531297,0.01817983,0.007413465,0.6956003],"study_design_scores_gemma":[0.000006884182,0.00003731533,0.000442085,0.00000590537,0.00001578657,0.00006217677,0.00001748644,0.9904838,0.004762011,0.002824457,0.001333605,0.000008458109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01322357,0.0003495799,0.9853672,0.0001131509,0.00002114928,0.00001907219,0.00005187698,0.0002921604,0.0005622031],"genre_scores_gemma":[0.4490569,0.0009021505,0.5462308,0.0002254455,0.0001635144,0.00013266,0.0007205805,0.0001216755,0.002446174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001962473,"threshold_uncertainty_score":0.003902137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0771561299403723,"score_gpt":0.3075723505921109,"score_spread":0.2304162206517386,"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."}}