{"id":"W2890790222","doi":"10.1080/22797254.2018.1503565","title":"Gaussian mixture model and Markov random fields for hyperspectral image classification","year":2018,"lang":"en","type":"article","venue":"European Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Università degli Studi di Pavia","keywords":"Hyperspectral imaging; Pattern recognition (psychology); Markov random field; Artificial intelligence; Mixture model; Spatial analysis; Computer science; Bayesian probability; Benchmark (surveying); Random field; Gaussian; Segmentation; Image segmentation; Mathematics; Geography; Statistics; Cartography","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":[0.001901601,0.0008890803,0.0009416949,0.001887642,0.0003420196,0.0008169058,0.001274354,0.001286354,0.00111126],"category_scores_gemma":[0.002793316,0.000435037,0.001204059,0.001675083,0.0006996261,0.001341101,0.0007084723,0.00151042,0.000738877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008247572,"about_ca_system_score_gemma":0.0008948117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007510706,"about_ca_topic_score_gemma":0.005380029,"domain_scores_codex":[0.9990329,0.0003050094,0.00003961504,0.0002155759,0.0003191306,0.00008779548],"domain_scores_gemma":[0.9991749,0.0004448663,0.0001108215,0.00009320458,0.0001472193,0.00002902074],"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":[0.0002192807,0.0001154037,0.001806182,0.0002298798,0.0001891846,0.0001376139,0.0001195672,0.5271749,0.01085525,0.04350379,0.003997741,0.4116513],"study_design_scores_gemma":[0.000003678998,0.0000153296,0.0003276156,0.00001007483,0.0000133776,0.0000327507,0.000006550634,0.9895481,0.001006876,0.00799783,0.001021818,0.00001598113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003140946,0.0008961271,0.9949713,0.0001100293,0.0000408534,0.00001663214,0.00004993828,0.0003741305,0.0004000702],"genre_scores_gemma":[0.3437552,0.002861616,0.6472239,0.0002822588,0.0003828921,0.0001994984,0.0007866005,0.0002117168,0.004296213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007510706,"threshold_uncertainty_score":0.01493394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01942455416110885,"score_gpt":0.237965501551318,"score_spread":0.2185409473902091,"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."}}