{"id":"W2148955138","doi":"10.1109/igarss.1996.516452","title":"SAR image segmentation by mathematical morphology and texture analysis","year":2002,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Université de Sherbrooke","keywords":"Synthetic aperture radar; Artificial intelligence; Image segmentation; Mathematical morphology; Computer science; Context (archaeology); Image texture; Segmentation; Land cover; Computer vision; Remote sensing; Radar imaging; Pixel; Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Texture (cosmology); Image (mathematics); Geography; Image processing; Land use; Radar; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006055849,0.00005568924,0.0001023891,0.00004747529,0.00005418649,0.00004022137,0.00002888578,0.00003854326,0.0128267],"category_scores_gemma":[0.000009294332,0.000035947,0.00002772801,0.0001493314,0.0000386425,0.00006222592,0.000001970499,0.0000437993,0.0003288931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":6.187835e-7,"about_ca_system_score_gemma":8.061274e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003392148,"about_ca_topic_score_gemma":0.0001501473,"domain_scores_codex":[0.999584,0.00002976277,0.00008318192,0.0001222695,0.0000747693,0.0001060618],"domain_scores_gemma":[0.9997898,0.00005111283,0.00001847997,0.00007308732,0.000007399284,0.00006013493],"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":[0.00002418269,0.00008868777,0.3228981,0.00003542516,0.0004786889,0.00008759018,0.001563746,0.0004544243,0.002700377,0.00008167394,0.1129758,0.5586112],"study_design_scores_gemma":[0.0004366316,0.0001170681,0.1422026,0.000003634261,0.0003351856,0.00009802459,0.0004140151,0.8509174,0.0005173826,0.0008663079,0.003820761,0.0002710244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.949112,0.0002945918,0.004643267,0.0006432799,0.00002467077,0.00004425918,0.0000206705,0.00003403917,0.04518324],"genre_scores_gemma":[0.9897988,0.00009148144,0.006673608,0.0003361055,0.0000171452,1.269868e-8,0.00007260469,0.0000010443,0.003009175],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.850463,"threshold_uncertainty_score":0.9880757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009884680888444705,"score_gpt":0.2060170732277097,"score_spread":0.196132392339265,"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."}}