{"id":"W248591590","doi":"10.5589/m08-013","title":"Noise estimation in a noise-adjusted principal component transformation and hyperspectral image restoration","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Principal component analysis; Noise (video); Data striping; Artificial intelligence; Transformation (genetics); Computer science; Linear discriminant analysis; Pattern recognition (psychology); Computer vision; Image noise; Remote sensing; Mathematics; Image (mathematics); Geography","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":[0.0006246188,0.0005996124,0.0003275852,0.0004981193,0.0002784703,0.000446189,0.0004874908,0.000535429,0.0006501522],"category_scores_gemma":[0.002500222,0.0002742023,0.0005034373,0.0006499921,0.0007167147,0.0007390791,0.0004394476,0.0005931465,0.0002841945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002717079,"about_ca_system_score_gemma":0.0004764042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00260198,"about_ca_topic_score_gemma":0.002934794,"domain_scores_codex":[0.9995839,0.0001095516,0.00001513741,0.00009547559,0.0001666562,0.00002925418],"domain_scores_gemma":[0.9996427,0.0001276295,0.00005205203,0.00007015477,0.00009626258,0.00001116207],"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":[0.0003320665,0.0001592507,0.004592632,0.0001184515,0.00009516042,0.0002598854,0.0002476143,0.5888457,0.1316225,0.02017433,0.001099778,0.2524527],"study_design_scores_gemma":[0.000007952572,0.00004098397,0.001205307,0.000003769175,0.000009817943,0.00006708661,0.00001472614,0.9729254,0.02256055,0.002284446,0.0008668053,0.00001325831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04460117,0.00004271759,0.9544543,0.00006363846,0.00002709638,0.00002064341,0.00001861401,0.000257751,0.0005139984],"genre_scores_gemma":[0.318257,0.0001106458,0.6794435,0.00004746542,0.00002573945,0.00007609318,0.0001475527,0.0001264812,0.001765441],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00260198,"threshold_uncertainty_score":0.005173683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02179136260568871,"score_gpt":0.2188354055861408,"score_spread":0.1970440429804521,"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."}}