{"id":"W4399666848","doi":"10.5566/ias.3078","title":"An Experimental Study for the Effects of Noise on Hyperspectral Imagery Classification","year":2024,"lang":"en","type":"article","venue":"Image Analysis & Stereology","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Space Agency; Concordia University","funders":"","keywords":"Hyperspectral imaging; Noise (video); Computer science; Artificial intelligence; Pattern recognition (psychology); Gaussian noise; Data cube; Contextual image classification; Computer vision; Image (mathematics); Data mining","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":[0.001991667,0.0008571685,0.0005877654,0.0008418927,0.0007476225,0.0007654444,0.001034031,0.00085102,0.002047516],"category_scores_gemma":[0.01077388,0.0002819179,0.0004891392,0.0009364456,0.0008754143,0.00118636,0.000756328,0.000752318,0.0004466991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005139354,"about_ca_system_score_gemma":0.0004235622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002000899,"about_ca_topic_score_gemma":0.001831583,"domain_scores_codex":[0.9978661,0.0003837041,0.0001986334,0.0004308952,0.0009499867,0.0001706456],"domain_scores_gemma":[0.9887303,0.005527744,0.0007254052,0.001374969,0.003397237,0.0002444185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005377104,0.002348464,0.01670518,0.001710426,0.0003519391,0.0006787764,0.0009637233,0.1551116,0.6018684,0.003051952,0.004887482,0.2069449],"study_design_scores_gemma":[0.0001296898,0.003430726,0.0253469,0.0001565454,0.0001798986,0.0005386897,0.0007543867,0.4720137,0.4893884,0.001952286,0.005967377,0.0001413952],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8603924,0.0009243122,0.1333108,0.0002835859,0.0003513029,0.0002182229,0.0006162469,0.0009552195,0.002947971],"genre_scores_gemma":[0.862333,0.0004403477,0.1328933,0.0002335148,0.00005865968,0.0002898598,0.001458651,0.0003211125,0.001971476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002047516,"threshold_uncertainty_score":0.01053309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01593998674057442,"score_gpt":0.3016800433227775,"score_spread":0.2857400565822031,"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."}}