{"id":"W2123930659","doi":"10.5194/isprsarchives-xl-2-w3-35-2014","title":"AN EFFICIENT INITIALIZATION METHOD FOR K-MEANS CLUSTERING OF HYPERSPECTRAL DATA","year":2014,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Università degli Studi di Pavia","keywords":"Hyperspectral imaging; Initialization; Cluster analysis; Computer science; Endmember; Thresholding; Pattern recognition (psychology); Confusion matrix; Artificial intelligence; Simplex; Image (mathematics); Mathematics","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":[0.001032539,0.001322133,0.0009681147,0.001534516,0.0009895939,0.0008842023,0.001541258,0.001099447,0.002848838],"category_scores_gemma":[0.002765096,0.0006157162,0.001075306,0.001633223,0.0005827978,0.001039415,0.001325105,0.001434654,0.002412912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008959954,"about_ca_system_score_gemma":0.001605561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007484087,"about_ca_topic_score_gemma":0.009873219,"domain_scores_codex":[0.9990156,0.0002100896,0.00006734319,0.0002163004,0.0004129765,0.00007768418],"domain_scores_gemma":[0.9991484,0.0001677475,0.00008764601,0.0001076387,0.0004522792,0.0000362707],"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":[0.0003271195,0.000142119,0.001589827,0.0003764136,0.0001501024,0.0001202553,0.0003973999,0.2145629,0.03732662,0.009716829,0.01100371,0.7242867],"study_design_scores_gemma":[0.00001969412,0.00003270467,0.0007618187,0.00002868975,0.0000165379,0.0000655393,0.00004664745,0.9756402,0.0166788,0.002446233,0.004225648,0.00003749465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004297893,0.0001268618,0.9937303,0.00003158,0.00004331092,0.00006608928,0.00005905786,0.001143356,0.0005015054],"genre_scores_gemma":[0.05663309,0.0001249416,0.9408187,0.00003565904,0.00002454209,0.0002101218,0.00057028,0.000259128,0.001323451],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007484087,"threshold_uncertainty_score":0.01488101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02868060640415367,"score_gpt":0.2889958834230084,"score_spread":0.2603152770188548,"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."}}