{"id":"W2103540378","doi":"10.1109/igarss.2007.4423107","title":"Investigation of nonlinearity in hyperspectral remotely sensed imagery — a nonlinear time series analysis approach","year":2007,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Hyperspectral imaging; Nonlinear system; Computer science; Time series; Remote sensing; Time domain; Statistic; Artificial intelligence; Pattern recognition (psychology); Series (stratigraphy); Independent component analysis; Gaussian process; Gaussian; Data mining; Mathematics; Statistics; Computer vision; Machine learning; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005886994,0.00025835,0.0001865231,0.0005997676,0.0001871703,0.0003862754,0.0001765024,0.0003162143,0.0005175188],"category_scores_gemma":[0.002654377,0.0001011436,0.0001998105,0.0004219997,0.0005483527,0.0007630719,0.0002657925,0.0003404738,0.0000792248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003730262,"about_ca_system_score_gemma":0.0003619715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001859116,"about_ca_topic_score_gemma":0.001554656,"domain_scores_codex":[0.9997604,0.00007259329,0.00001151167,0.00003418854,0.0001016575,0.00001949688],"domain_scores_gemma":[0.9987279,0.0008407861,0.0001667547,0.00006984115,0.0001700511,0.00002469329],"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":[0.0004981502,0.0002383801,0.06462447,0.0002803525,0.0001339693,0.001185485,0.0007587073,0.5230298,0.169568,0.04115665,0.0008754744,0.1976505],"study_design_scores_gemma":[0.000001860934,0.00003272503,0.0110097,0.000004025837,0.000003976757,0.00009525077,0.00007289992,0.9779475,0.007676722,0.002884112,0.000262317,0.000008892274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6638857,0.0001725007,0.332082,0.0003103752,0.00001540318,0.00002720746,0.00009908464,0.0001167053,0.003291043],"genre_scores_gemma":[0.9785816,0.00009120642,0.02044923,0.00001798472,0.00001053483,0.0000164982,0.00007339861,0.000009493546,0.0007501133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001859116,"threshold_uncertainty_score":0.00369662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02261955037291134,"score_gpt":0.2333780776112847,"score_spread":0.2107585272383734,"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."}}