{"id":"W2170392867","doi":"10.5194/isprsarchives-xl-1-w3-341-2013","title":"ASSESMENT AND EVLUATION OF THE IMPACT OF USING POLSAR IMAGERIES WITH DIFERENT INCIDENT ANGLES IN FOREST CLASSIFICATION","year":2013,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Radar; Preprocessor; Radar imaging; Context (archaeology); Polarimetry; Contextual image classification; Computer science; Speckle pattern; Shuttle Radar Topography Mission; Geography; Artificial intelligence; Image (mathematics); Digital elevation model; Scattering","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":[0.002563432,0.0005530034,0.000341597,0.001188382,0.0002916588,0.001148748,0.0002379162,0.0004384462,0.0007426115],"category_scores_gemma":[0.007182102,0.0001541369,0.0004600114,0.0008761487,0.0003190542,0.0006756639,0.0003773833,0.0003403442,0.00027411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003500892,"about_ca_system_score_gemma":0.0002744759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004949942,"about_ca_topic_score_gemma":0.006200663,"domain_scores_codex":[0.9990084,0.000303781,0.0001025062,0.0001592342,0.000301765,0.0001243656],"domain_scores_gemma":[0.9923419,0.004884708,0.0005941963,0.0004216833,0.001607747,0.0001498134],"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":[0.005849527,0.00066734,0.2633715,0.0005561479,0.0006740972,0.0006968577,0.0005057723,0.06970993,0.1997846,0.0005224728,0.001907642,0.4557542],"study_design_scores_gemma":[0.00006158478,0.001506969,0.5212134,0.00008300719,0.0007362563,0.0007657037,0.001103875,0.3315355,0.1403985,0.0003209796,0.002179865,0.00009434803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9828665,0.000579629,0.01396114,0.0001069885,0.00005187151,0.00004020594,0.0002584411,0.0001621867,0.00197298],"genre_scores_gemma":[0.9857389,0.00022747,0.01275584,0.00004365212,0.00001752244,0.0000100049,0.0005262003,0.00002851289,0.0006519652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004949942,"threshold_uncertainty_score":0.0135569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0176359625727693,"score_gpt":0.2634256160569249,"score_spread":0.2457896534841556,"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."}}