{"id":"W2905439256","doi":"10.1109/lgrs.2018.2884898","title":"Synthetic Aperture Radar Image Generation With Deep Generative Models","year":2018,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Synthetic aperture radar; Autoencoder; Computer science; Artificial intelligence; Radar imaging; Deep learning; Generative grammar; Image (mathematics); Generative model; Pattern recognition (psychology); Computer vision; Inverse synthetic aperture radar; Radar; Telecommunications","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":[0.0004370287,0.0005052672,0.0004259546,0.0002674224,0.0001265612,0.0004142582,0.0007456217,0.0005714009,0.001086374],"category_scores_gemma":[0.0009456082,0.0004429897,0.0007002435,0.0003300813,0.00041383,0.0006915841,0.0007134539,0.001077917,0.0003744658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004514279,"about_ca_system_score_gemma":0.000280134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001735969,"about_ca_topic_score_gemma":0.002135886,"domain_scores_codex":[0.9998208,0.0000441718,0.000006982279,0.00005146694,0.00005748258,0.00001914965],"domain_scores_gemma":[0.9996442,0.0001888471,0.00003963047,0.00006062627,0.00004813473,0.00001858958],"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":[0.00006778577,0.00003923368,0.0004280642,0.00003637749,0.00003229716,0.00008622322,0.00004321541,0.9133814,0.01298486,0.01045686,0.001066293,0.06137747],"study_design_scores_gemma":[0.000002218875,0.000005542823,0.00003635396,9.931159e-7,0.00000179383,0.0000135263,8.447328e-7,0.997179,0.001294246,0.001282595,0.0001804377,0.000002500918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01481002,0.000182415,0.9827809,0.0001254722,0.00002740319,0.00002226135,0.00006732706,0.0006380766,0.00134608],"genre_scores_gemma":[0.7589735,0.0004003002,0.2347694,0.0002400866,0.00004961668,0.0001058433,0.0006121662,0.0002318174,0.004617338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001735969,"threshold_uncertainty_score":0.003634334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01111880974607109,"score_gpt":0.2159252705074099,"score_spread":0.2048064607613388,"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."}}