{"id":"W2945455493","doi":"10.1117/12.2519577","title":"Deep learning for remote sensed target classification in maritime satellite radar images","year":2019,"lang":"en","type":"article","venue":"","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Convolutional neural network; Computer science; Deep learning; Remote sensing; Artificial intelligence; Satellite; Radar imaging; Satellite imagery; Radar; Iceberg; Synthetic aperture radar; Contextual image classification; Pixel; Computer vision; Geology; Meteorology; Geography; Image (mathematics); Telecommunications; Engineering; Sea ice","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":[0.0005192041,0.0007327114,0.0003649328,0.0005057122,0.0001780827,0.0003885555,0.0006090503,0.0005979474,0.001079436],"category_scores_gemma":[0.001165135,0.0002420325,0.000426949,0.0005499413,0.0002120699,0.0006767472,0.0005021311,0.0009308849,0.0004066878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006076904,"about_ca_system_score_gemma":0.0005682021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01006162,"about_ca_topic_score_gemma":0.01053179,"domain_scores_codex":[0.9998283,0.00003217314,0.0000100249,0.00004200937,0.00003910856,0.00004831645],"domain_scores_gemma":[0.999733,0.0001060009,0.00004288073,0.000031005,0.00006744787,0.00001956511],"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":[0.0003636727,0.0003377435,0.005303415,0.0001401555,0.0001525089,0.0001393943,0.00006801574,0.5515117,0.02374944,0.002189698,0.004634168,0.4114101],"study_design_scores_gemma":[0.000003287881,0.00002037332,0.0007258235,0.00000506943,0.000007231058,0.000007426728,0.000007664261,0.9953471,0.003130281,0.0005183819,0.0002245746,0.000002927019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4645422,0.003178286,0.5223639,0.0008568334,0.0001830197,0.00008995852,0.0009075323,0.003350237,0.004527944],"genre_scores_gemma":[0.9241226,0.0005278611,0.0705999,0.0001524228,0.00004189272,0.00004793374,0.001259029,0.00006646011,0.003181926],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01006162,"threshold_uncertainty_score":0.02000612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02160069287411722,"score_gpt":0.2536672752369441,"score_spread":0.2320665823628269,"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."}}