{"id":"W4387717108","doi":"10.1364/hmise.2023.hw5c.2","title":"Denoising and Dimensionality Reduction for Improving Constrained Energy Minimization in Hyperspectral Imagery Analysis","year":2023,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Canadian Space Agency","funders":"","keywords":"Hyperspectral imaging; Dimensionality reduction; Artificial intelligence; Noise reduction; Energy minimization; Computer science; Minification; Pattern recognition (psychology); Energy (signal processing); Image denoising; Reduction (mathematics); Computer vision; Curse of dimensionality; Image (mathematics); Mathematics; Statistics; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001724899,0.00008999935,0.0001380104,0.0004106902,0.00005309198,0.00004824277,0.00002424803,0.00005791271,0.000004373962],"category_scores_gemma":[0.00008941122,0.00009813889,0.00004630174,0.0008540242,0.00003833545,0.0001639234,0.000009225645,0.00004204364,0.000001156829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009230237,"about_ca_system_score_gemma":0.00001394377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007969976,"about_ca_topic_score_gemma":0.00004847641,"domain_scores_codex":[0.9993204,0.00002099877,0.000211091,0.0002044974,0.00007930704,0.0001637243],"domain_scores_gemma":[0.9996836,0.00008381534,0.00002972363,0.0001176352,0.00005183172,0.00003338039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001386802,0.00001078032,0.0003806123,0.00005071096,0.00008467644,0.000003001607,0.0001687984,0.08950716,0.883212,0.00114087,0.0002544199,0.02517315],"study_design_scores_gemma":[0.0001957678,0.000005569317,0.01184274,0.000005683628,0.00007329413,0.000005322247,0.0003847607,0.9584337,0.02862497,0.0002896787,0.00002304333,0.0001155149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7686216,0.00004881829,0.2299176,0.000267745,0.0001303596,0.0001211873,0.000005367139,0.0004389218,0.0004484795],"genre_scores_gemma":[0.9802018,0.00001830808,0.01947319,0.000007295082,0.00004346794,0.000005130609,0.0001061853,0.00001761945,0.0001269763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8689265,"threshold_uncertainty_score":0.4001988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01499756348155662,"score_gpt":0.233382854668278,"score_spread":0.2183852911867214,"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."}}