{"id":"W2981063497","doi":"10.5194/isprs-archives-xlii-4-w18-379-2019","title":"IMPACT OF POLARIMETRIC SAR SPECKLE REDUCTION ON CLASSIFICATION OF AGRICULTURE LANDS","year":2019,"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":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"National Aeronautics and Space Administration","keywords":"Speckle pattern; Support vector machine; Polarimetry; Computer science; Artificial intelligence; Remote sensing; Speckle noise; Synthetic aperture radar; Pattern recognition (psychology); Geography; Physics; Scattering; Optics","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":[0.001578571,0.0004580908,0.0003675245,0.0009438773,0.0002300735,0.0006422688,0.000195284,0.0003266637,0.0004875312],"category_scores_gemma":[0.001593169,0.0001071865,0.0003187979,0.0004551881,0.0003567837,0.0004605903,0.0002770902,0.0002571893,0.0002213746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001993678,"about_ca_system_score_gemma":0.0002055087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001671776,"about_ca_topic_score_gemma":0.00256848,"domain_scores_codex":[0.9993665,0.0001701455,0.00004083188,0.0001292253,0.0002290307,0.00006438612],"domain_scores_gemma":[0.9989659,0.0004107184,0.0001042541,0.000112783,0.0003717133,0.00003478554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001848125,0.000475328,0.0517281,0.0003434484,0.00023704,0.0003150295,0.000210455,0.1018677,0.3384804,0.0005179303,0.001820566,0.502156],"study_design_scores_gemma":[0.00003957084,0.0007218161,0.1407045,0.0000450384,0.0002039789,0.0003830495,0.0002904319,0.6635502,0.1917737,0.0004052722,0.00183199,0.00005051124],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9557418,0.0004884578,0.04100747,0.0001013313,0.00006598042,0.000041044,0.0001524356,0.000298945,0.002102473],"genre_scores_gemma":[0.9736323,0.0002239494,0.02494255,0.00005912928,0.00002160356,0.00001500459,0.0004305493,0.000029476,0.0006454517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001671776,"threshold_uncertainty_score":0.008348346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01221956663214743,"score_gpt":0.2493264955279806,"score_spread":0.2371069288958332,"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."}}