{"id":"W2469929894","doi":"10.1007/s11859-016-1152-y","title":"Land cover classification of RADARSAT-2 SAR data using convolutional neural network","year":2016,"lang":"en","type":"article","venue":"Wuhan University Journal of Natural Sciences","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Softmax function; Computer science; Pattern recognition (psychology); Synthetic aperture radar; Convolutional neural network; Artificial intelligence; Feature (linguistics); Land cover; Backpropagation; Feature extraction; Remote sensing; Pooling; Contextual image classification; Artificial neural network; Deep learning; Geology; Image (mathematics); Land use","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004230328,0.00007164775,0.000128395,0.0001277152,0.0001223712,0.00002137862,0.0005170444,0.00004177215,0.00001194667],"category_scores_gemma":[0.00007146293,0.00005148896,0.00004173403,0.0003577873,0.0003355164,0.001039401,0.00005773634,0.0001082777,0.000002521784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001434462,"about_ca_system_score_gemma":0.00009117155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000120625,"about_ca_topic_score_gemma":0.000006020462,"domain_scores_codex":[0.999168,0.00005769689,0.0001857803,0.0001147849,0.0003257309,0.0001479767],"domain_scores_gemma":[0.9992801,0.0001288069,0.0002297462,0.0001490162,0.000160123,0.00005223301],"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":[0.0002559765,0.00005870677,0.09919009,0.00005412469,0.000193817,0.00006256114,0.0002035615,0.1320098,0.7026745,0.002025477,0.008864769,0.05440665],"study_design_scores_gemma":[0.000969406,0.00008644112,0.155297,0.000274733,0.00008299269,0.0002038968,0.0001536338,0.8233923,0.001877551,0.0002127929,0.01719851,0.0002507494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847463,0.0005193577,0.01332797,0.0004035276,0.0007141446,0.00004125166,0.00001162706,0.00001782643,0.0002179563],"genre_scores_gemma":[0.991042,0.00008314579,0.008671025,0.000006088972,0.0001373579,4.741021e-10,0.000002170764,0.000003520505,0.00005470439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7007969,"threshold_uncertainty_score":0.2099659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05710185858406018,"score_gpt":0.2517824780967041,"score_spread":0.1946806195126439,"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."}}