{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001666265,0.0002127609,0.0002395343,0.0001120979,0.0001061103,0.0001474776,0.0001036928,0.00007063561,0.000002739926],"category_scores_gemma":[0.0003229317,0.0002604342,0.00002377879,0.0003465097,0.00005741053,0.0003565089,0.0000824435,0.0003788357,0.00001864903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001005828,"about_ca_system_score_gemma":0.00002735029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001288279,"about_ca_topic_score_gemma":0.00002666747,"domain_scores_codex":[0.9986224,0.00006912361,0.0003735297,0.000450463,0.0001470827,0.0003374217],"domain_scores_gemma":[0.9992787,0.00008545633,0.00007025267,0.0003858484,0.00005052995,0.0001292082],"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":[0.000005310389,0.000002044939,0.0001392926,0.00003674355,0.00001222321,0.00008056669,0.001125007,0.006741244,0.8159525,0.00000254603,0.00004585333,0.1758566],"study_design_scores_gemma":[0.0002929691,0.000004512679,0.003748981,0.0001271453,0.00002024352,0.0001061476,0.0003474147,0.9799952,0.01487414,0.00001069552,0.000214864,0.0002577154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8618973,0.0002055083,0.1337792,0.002531197,0.0002671251,0.0001909861,0.00001374815,0.0004951336,0.0006198633],"genre_scores_gemma":[0.9186389,0.0000401546,0.08051446,0.0002206836,0.000376794,4.844801e-9,0.0001326985,0.00007080565,0.000005431044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9732539,"threshold_uncertainty_score":0.9999848,"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."}}