{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003184746,0.0002258065,0.0002471105,0.0003274861,0.0001795538,0.00008055408,0.00003953095,0.0001987212,0.000001104504],"category_scores_gemma":[0.00006037553,0.0002591149,0.00007384623,0.0003976297,0.00005567937,0.0005960408,0.00001404321,0.00021544,0.000002550137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000201236,"about_ca_system_score_gemma":0.0000177324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000579849,"about_ca_topic_score_gemma":0.000005397735,"domain_scores_codex":[0.9987703,0.00005889466,0.0002950698,0.0003670735,0.0001940233,0.000314652],"domain_scores_gemma":[0.999265,0.00007970757,0.0001105507,0.0003005405,0.0001625892,0.0000816265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002764732,0.000005072524,0.000001754385,0.0001058557,0.00001792877,0.000005822415,0.0003059494,0.05648057,0.8145742,9.415604e-7,0.00005441722,0.1284198],"study_design_scores_gemma":[0.0002247935,0.00002459372,0.000474439,0.000108315,0.00004424819,0.0003015289,0.0004923625,0.6480055,0.3500658,0.00004975851,0.00004695117,0.0001617258],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6881433,0.00009804868,0.3101557,0.00008023473,0.0007613147,0.0002777331,0.000004455914,0.0004142395,0.0000650379],"genre_scores_gemma":[0.7596545,0.0001297023,0.2396955,0.000003431581,0.0003347293,4.600843e-8,0.00004775405,0.00008494551,0.00004933938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5915249,"threshold_uncertainty_score":0.9999861,"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."}}