{"id":"W2160179017","doi":"10.1109/ccece.2011.6030597","title":"Information theoretic assessment of correlated noise in hyperspectral signal unmixing","year":2011,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hyperspectral imaging; Noise (video); Gaussian noise; Full spectral imaging; Computer science; Artificial intelligence; Pattern recognition (psychology); Value noise; White noise; Gradient noise; Noise measurement; Mathematics; Statistics; Noise reduction; Noise floor; Image (mathematics)","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":[0.005556792,0.001094232,0.0008687691,0.002257107,0.0005060087,0.00152514,0.001029443,0.001221953,0.0006665289],"category_scores_gemma":[0.02347682,0.000488066,0.0006233217,0.001405516,0.002889407,0.00321519,0.001882319,0.0009682237,0.0001400609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001366854,"about_ca_system_score_gemma":0.001031758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001524768,"about_ca_topic_score_gemma":0.001150899,"domain_scores_codex":[0.9978423,0.000866505,0.00008630499,0.0002159279,0.0008744596,0.0001144553],"domain_scores_gemma":[0.9844258,0.01278762,0.000844037,0.0005503721,0.001201535,0.0001906571],"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":[0.000141812,0.00006216809,0.001954641,0.0001557222,0.00009087868,0.0002997912,0.0001117401,0.8660566,0.004188268,0.1045296,0.0005646137,0.02184423],"study_design_scores_gemma":[0.000004016501,0.00002116006,0.0004749723,0.00001050021,0.000009855837,0.00003863514,0.00001624541,0.9799067,0.001259286,0.0181133,0.0001306716,0.00001457335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05442635,0.0006311709,0.9420569,0.0002547211,0.0000250954,0.00003782389,0.00007249913,0.00007036749,0.002425028],"genre_scores_gemma":[0.8848612,0.001149439,0.1119075,0.0001364998,0.0001318693,0.0001556922,0.0002958837,0.00006871407,0.001293262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005556792,"threshold_uncertainty_score":0.02938747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01374405612305165,"score_gpt":0.2186997494073376,"score_spread":0.204955693284286,"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."}}