{"id":"W2930359273","doi":"10.1016/j.isprsjprs.2019.03.015","title":"A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem","year":2019,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":223,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"Environment and Climate Change Canada; Research and Development Corporation of Newfoundland and Labrador; Natural Sciences and Engineering Research Council of Canada; Department of Environment and Conservation, Government of Newfoundland and Labrador; Government of Canada","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Segmentation; Land cover; Synthetic aperture radar; Remote sensing; Inference; Feature (linguistics); Encoder; Computer vision; Land use; Geology","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":[0.0003170215,0.0008586085,0.0006448495,0.0009101142,0.0003381815,0.0006977651,0.001125732,0.0009576465,0.002264413],"category_scores_gemma":[0.0003925826,0.0004637168,0.000762655,0.0007386333,0.0002650875,0.0008510764,0.0006947751,0.000594453,0.0007686349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007274817,"about_ca_system_score_gemma":0.0009862595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02299857,"about_ca_topic_score_gemma":0.03129866,"domain_scores_codex":[0.9998361,0.00001233394,0.000008700616,0.00006122982,0.00003791765,0.00004371723],"domain_scores_gemma":[0.9998736,0.00002747301,0.00001333835,0.00001831172,0.00005298334,0.00001419129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003993281,0.0002703751,0.003133434,0.0001427307,0.0002410965,0.000196323,0.00006506294,0.2859705,0.04424118,0.00370809,0.008628988,0.6530029],"study_design_scores_gemma":[0.000003371142,0.00001734105,0.0005923093,0.000005283217,0.0000187958,0.00002252841,0.000005391217,0.9949136,0.003120044,0.0005967366,0.0006992637,0.000005278095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1198005,0.001397061,0.8672088,0.0004346116,0.0002400995,0.0001116307,0.001230586,0.004219736,0.005356845],"genre_scores_gemma":[0.6527015,0.0008927732,0.3259006,0.0004329922,0.0001312233,0.0001178497,0.004050667,0.000286012,0.01548636],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02299857,"threshold_uncertainty_score":0.0457294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01007266394602705,"score_gpt":0.2349706970812468,"score_spread":0.2248980331352197,"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."}}