{"id":"W3013641226","doi":"10.1080/07038992.2020.1726735","title":"Fast Unmixing of Noisy Hyperspectral Images Based on Vertex Component Analysis and Singular Spectrum Analysis Algorithms","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Endmember; Hyperspectral imaging; Noise reduction; Independent component analysis; Pattern recognition (psychology); Noise (video); Artificial intelligence; Singular spectrum analysis; Mathematics; Vertex (graph theory); Algorithm; Computer science; Image (mathematics); Singular value decomposition; Graph","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006181729,0.0009731409,0.0008591359,0.001761663,0.0005054525,0.0009190312,0.0006814211,0.0006159772,0.0008070398],"category_scores_gemma":[0.00135773,0.0004103927,0.001050479,0.001611996,0.0006392271,0.001481761,0.0008046159,0.001046891,0.0004325278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003468883,"about_ca_system_score_gemma":0.0006487658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001744577,"about_ca_topic_score_gemma":0.002369737,"domain_scores_codex":[0.9994011,0.00009781453,0.00003493737,0.0001279644,0.0003002382,0.00003803593],"domain_scores_gemma":[0.9995296,0.00016532,0.00005815643,0.00006610859,0.000158724,0.00002212486],"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":[0.0002388382,0.0001078912,0.001326249,0.0002957477,0.0001721712,0.000148763,0.0002696683,0.1051238,0.2010024,0.01797584,0.001157111,0.6721814],"study_design_scores_gemma":[0.000009209773,0.00004559784,0.0006118547,0.0000108238,0.000026407,0.0001304036,0.00003826125,0.9513292,0.040657,0.005308134,0.001800402,0.00003275281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007146366,0.0001163398,0.9921044,0.00002130465,0.00001775755,0.00001996371,0.00001321498,0.0002512964,0.0003093257],"genre_scores_gemma":[0.07644544,0.0002690961,0.9221476,0.00001641668,0.00002613773,0.00005625825,0.0001245226,0.00009748423,0.0008169929],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001761663,"threshold_uncertainty_score":0.003468812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01159328631580667,"score_gpt":0.2034466661169915,"score_spread":0.1918533798011849,"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."}}