{"id":"W2146449740","doi":"10.3390/s8021321","title":"The Successive Projection Algorithm (SPA), an Algorithm with a Spatial Constraint for the Automatic Search of Endmembers in Hyperspectral Data","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Lethbridge","funders":"Networks of Centres of Excellence of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Endmember; Hyperspectral imaging; Pixel; Algorithm; Adjacency list; Computer science; Spatial analysis; Projection (relational algebra); Pattern recognition (psychology); Mathematics; Artificial intelligence; Remote sensing; Geography","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":[0.001958583,0.001625381,0.001174492,0.001209223,0.0008263591,0.001130324,0.001251044,0.0009994978,0.002075485],"category_scores_gemma":[0.004537478,0.00081683,0.001174394,0.001287453,0.00134632,0.001607744,0.001893472,0.001804784,0.000989898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000467607,"about_ca_system_score_gemma":0.001721383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003821515,"about_ca_topic_score_gemma":0.006864223,"domain_scores_codex":[0.9987229,0.0003916061,0.00007298482,0.0002624114,0.0004939382,0.00005610726],"domain_scores_gemma":[0.9981288,0.001124343,0.0001894892,0.0002038378,0.0003006984,0.00005276568],"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":[0.0002167901,0.0001172526,0.002215214,0.0003741473,0.0003596565,0.0001677827,0.0003413893,0.239149,0.04121431,0.03016361,0.005511465,0.6801693],"study_design_scores_gemma":[0.00002675033,0.00009210368,0.0006026949,0.00002284339,0.00002833212,0.0002046132,0.00004516428,0.9640186,0.01716724,0.01088768,0.006867969,0.0000361509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00300203,0.00009750649,0.9961008,0.00003685797,0.00001165554,0.00004110008,0.00002373227,0.000394517,0.0002917674],"genre_scores_gemma":[0.02302129,0.0001395043,0.975593,0.00004027227,0.00001717713,0.0001180136,0.0001552167,0.0001604001,0.0007550406],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003821515,"threshold_uncertainty_score":0.0103581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04154505271190751,"score_gpt":0.2787351103496917,"score_spread":0.2371900576377842,"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."}}