{"id":"W2132083814","doi":"10.1109/igarss.2008.4779288","title":"Estimating Dimensionality of Hyperspectral Data Using False Neighbour Method","year":2008,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; University of Victoria","funders":"Natural Resources Canada; National Aeronautics and Space Administration","keywords":"Hyperspectral imaging; Curse of dimensionality; Pattern recognition (psychology); Pixel; Computer science; Artificial intelligence; Land cover; Nonlinear system; Dimensionality reduction; Remote sensing; Mathematics; Data mining; Geography; Land use","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00137501,0.0003973797,0.000660591,0.001238936,0.000451487,0.0009998559,0.0007013174,0.0007348428,0.0005395437],"category_scores_gemma":[0.007651951,0.0002424486,0.0005321456,0.0006833574,0.0008730174,0.001862216,0.0007182779,0.0006895869,0.0002141859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004555988,"about_ca_system_score_gemma":0.0003433764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001170507,"about_ca_topic_score_gemma":0.001167458,"domain_scores_codex":[0.998961,0.0003214387,0.00006548472,0.0001783941,0.0004049086,0.0000687632],"domain_scores_gemma":[0.9965951,0.002027376,0.0003486701,0.0004322637,0.0005364833,0.0000600864],"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":[0.0008827245,0.0001662136,0.018608,0.0004824458,0.0002475847,0.0007743593,0.001065551,0.4138723,0.1119036,0.0545045,0.00257963,0.394913],"study_design_scores_gemma":[0.000005913972,0.00003684822,0.003948051,0.000009609427,0.00001158411,0.000249762,0.00006430656,0.9715588,0.01413096,0.00921182,0.0007229592,0.00004928806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0836918,0.0001468543,0.915005,0.00006967259,0.00002593683,0.00002803529,0.00007372351,0.000135303,0.0008235911],"genre_scores_gemma":[0.5979756,0.0002100789,0.4003903,0.00002885571,0.00003042941,0.00007342609,0.0002872624,0.00003820216,0.0009657326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00137501,"threshold_uncertainty_score":0.007271826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1317369693003696,"score_gpt":0.3355495147851595,"score_spread":0.2038125454847899,"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."}}