{"id":"W1580532928","doi":"","title":"A fast and precise registration method for repeat-pass interferometric ALOS PALSAR data through baseline estimation","year":2011,"lang":"en","type":"article","venue":"IEEE Asia-Pacific Conference on Synthetic Aperture Radar","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Azimuth; Computer science; Offset (computer science); Pixel; Synthetic aperture radar; Interferometric synthetic aperture radar; Remote sensing; Computer vision; Artificial intelligence; Interferometry; Satellite; Image registration; Image (mathematics); Mathematics; Geology; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005669186,0.0008416204,0.0007845646,0.001822963,0.0006735441,0.0007085857,0.001149463,0.000542445,0.002280976],"category_scores_gemma":[0.001056731,0.0006174042,0.0007467691,0.001783508,0.0003753633,0.001342109,0.0009071114,0.0009933216,0.0021172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003572979,"about_ca_system_score_gemma":0.001009929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002843774,"about_ca_topic_score_gemma":0.004439821,"domain_scores_codex":[0.999127,0.00008845967,0.00004396638,0.000248372,0.0004312358,0.00006100088],"domain_scores_gemma":[0.9993961,0.00005337578,0.00009928487,0.0001491556,0.0002788029,0.00002338001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001262291,0.00008645408,0.001168753,0.0001110391,0.00009037846,0.0001047083,0.0001648595,0.01566101,0.1102725,0.002917479,0.005093514,0.864203],"study_design_scores_gemma":[0.0000796526,0.0003042107,0.008336161,0.00002683021,0.0001239793,0.001291611,0.0001975124,0.7912069,0.1560775,0.003468883,0.03865426,0.0002325803],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006136947,0.0001043908,0.9916687,0.00003738168,0.00004348045,0.00004006589,0.00006075687,0.00138653,0.0005217203],"genre_scores_gemma":[0.06069002,0.000225263,0.9354714,0.00003540974,0.00004155727,0.00009338151,0.0004509599,0.0002343596,0.002757646],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002843774,"threshold_uncertainty_score":0.007630646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06709917674377804,"score_gpt":0.2961156200612267,"score_spread":0.2290164433174486,"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."}}