{"id":"W2030094301","doi":"10.1109/igarss.2014.6947319","title":"High-resolution wide-swath SAR processing with compressed sensing","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Synthetic aperture radar; Compressed sensing; Computer science; Remote sensing; Inverse synthetic aperture radar; Radar imaging; Image resolution; Signal processing; High resolution; Channel (broadcasting); Iterative reconstruction; Side looking airborne radar; Signal reconstruction; Computer vision; Artificial intelligence; Geology; Radar; Radar engineering details; 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":[0.0003446831,0.0004500918,0.0002917592,0.0004476266,0.0001745452,0.0003537826,0.0004352358,0.0004848962,0.0009512822],"category_scores_gemma":[0.0010774,0.000147265,0.000290529,0.0005633418,0.0003913754,0.0008390269,0.0005253947,0.0005444299,0.0003006158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001686809,"about_ca_system_score_gemma":0.0002786471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004912033,"about_ca_topic_score_gemma":0.0006726243,"domain_scores_codex":[0.9996819,0.0000548778,0.00001415418,0.00003897406,0.0001951558,0.00001479246],"domain_scores_gemma":[0.9995775,0.0001912252,0.00004754963,0.00007757702,0.00009192804,0.00001431553],"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":[0.0002516638,0.0001152575,0.0009332421,0.0004089871,0.00008182523,0.0002713772,0.0001797512,0.142272,0.3093529,0.03039272,0.0026376,0.5131027],"study_design_scores_gemma":[0.00002467997,0.0001892444,0.001048685,0.00003091676,0.0000215945,0.0006323986,0.00004013454,0.8898414,0.0941012,0.0066337,0.007398698,0.00003743694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01585652,0.000287523,0.9817059,0.0001427205,0.00003534245,0.00002866071,0.00003874072,0.0002108126,0.001693808],"genre_scores_gemma":[0.1925395,0.0005997152,0.8048273,0.0001315488,0.0001381354,0.00005270937,0.0002035303,0.0000529028,0.001454632],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0009512822,"threshold_uncertainty_score":0.003182411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006352916898665836,"score_gpt":0.2060279468891143,"score_spread":0.1996750299904485,"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."}}