{"id":"W3007310740","doi":"10.3390/rs12040641","title":"Characterizing and Mitigating Sensor Generated Spatial Correlations in Airborne Hyperspectral Imaging Data","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Pixel; Deconvolution; Spatial correlation; Image resolution; Remote sensing; Point spread function; Computer science; Artificial intelligence; Spatial analysis; Image sensor; Spatial variability; Computer vision; Pattern recognition (psychology); Geography; Mathematics; Algorithm; Statistics","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":[0.001539736,0.000638132,0.0003581863,0.0008113555,0.0003520017,0.0006655894,0.0004929808,0.0005026844,0.0002658257],"category_scores_gemma":[0.004617648,0.0001993368,0.0005114882,0.0009045813,0.0004706738,0.0009226359,0.0008125462,0.0008142382,0.0001265355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004409357,"about_ca_system_score_gemma":0.0009598936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002929548,"about_ca_topic_score_gemma":0.004032937,"domain_scores_codex":[0.9991788,0.0001468814,0.00004269863,0.000139051,0.0004146713,0.00007799471],"domain_scores_gemma":[0.9976866,0.001061163,0.000360572,0.0002722753,0.0005756179,0.00004373567],"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":[0.0004687463,0.0003123501,0.03483049,0.0005111953,0.0002697135,0.0009381871,0.0008829522,0.4386984,0.2739511,0.004555717,0.001670591,0.2429106],"study_design_scores_gemma":[0.00002816857,0.0002425377,0.0366293,0.00004456764,0.0001004916,0.0005326695,0.0002947069,0.7896336,0.1670947,0.002373475,0.002946144,0.00007967911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6693076,0.0003802213,0.3281285,0.0001548596,0.00003814599,0.00007676166,0.0002824249,0.0006081205,0.001023355],"genre_scores_gemma":[0.825269,0.0002999833,0.1731575,0.00008745403,0.00002259204,0.00004359172,0.0006535893,0.000108297,0.0003579663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002929548,"threshold_uncertainty_score":0.008143008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03455564608808143,"score_gpt":0.2399827750892106,"score_spread":0.2054271290011292,"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."}}