{"id":"W4385507417","doi":"10.3390/rs15153855","title":"An Unsupervised Feature Extraction Using Endmember Extraction and Clustering Algorithms for Dimension Reduction of Hyperspectral Images","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Hyperspectral imaging; Dimensionality reduction; Endmember; Pattern recognition (psychology); Artificial intelligence; Computer science; Cluster analysis; Principal component analysis; Classifier (UML); Support vector machine; Feature extraction; Curse of dimensionality","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":[0.0003632611,0.0007895153,0.0005871195,0.001691481,0.0005514364,0.0005312167,0.000741514,0.0005615475,0.0006106555],"category_scores_gemma":[0.0008202126,0.0002945497,0.001178643,0.001151866,0.0003657376,0.0009623822,0.0005823025,0.0007632341,0.0003511936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003269765,"about_ca_system_score_gemma":0.0007171729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002848599,"about_ca_topic_score_gemma":0.003434557,"domain_scores_codex":[0.9994209,0.00006555235,0.0000366667,0.0001867495,0.0002480869,0.00004207523],"domain_scores_gemma":[0.9996387,0.00006482922,0.00005106413,0.00005364464,0.0001796416,0.00001208997],"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":[0.00008683521,0.0001634958,0.001874933,0.0001619124,0.0001428875,0.0001019898,0.000204809,0.04892692,0.09758078,0.00461037,0.002739839,0.8434052],"study_design_scores_gemma":[0.00001590419,0.0001017745,0.005379396,0.00002347985,0.00005545611,0.0003036816,0.00007809185,0.9119378,0.07254642,0.003055497,0.006428186,0.0000742341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01441574,0.000182766,0.9843488,0.0000456489,0.00002698322,0.00004115972,0.00004376376,0.0004572517,0.0004378224],"genre_scores_gemma":[0.1437523,0.0003278191,0.8525282,0.00006842714,0.00004616885,0.0001542813,0.0004681063,0.00009928602,0.002555453],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002848599,"threshold_uncertainty_score":0.00566411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03714904626541637,"score_gpt":0.3085112103608583,"score_spread":0.2713621640954419,"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."}}