{"id":"W2981577683","doi":"10.5194/isprs-archives-xlii-4-w18-1107-2019","title":"FULL POLARIMETRIC UAVSAR IMAGE CHANGE DETECTION BASED ON CHANGE INDICES","year":2019,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"California Institute of Technology; National Aeronautics and Space Administration","keywords":"Change detection; Cluster analysis; Kernel (algebra); Synthetic aperture radar; Remote sensing; Artificial intelligence; Pattern recognition (psychology); Dimension (graph theory); Computer science; Geography; Polarimetry; Mathematics","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":[0.0004288817,0.0003956191,0.0002657403,0.002232203,0.0001760513,0.000591773,0.0002027305,0.0001860229,0.0008546517],"category_scores_gemma":[0.000897106,0.0001251782,0.000247628,0.001071377,0.0001976707,0.0005168237,0.0002471994,0.0002673673,0.0005037049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002174246,"about_ca_system_score_gemma":0.0001904398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001498399,"about_ca_topic_score_gemma":0.002651674,"domain_scores_codex":[0.9997191,0.0000412053,0.00001872192,0.0000777282,0.0001146591,0.00002864854],"domain_scores_gemma":[0.9992961,0.00009150573,0.0001151292,0.00008064193,0.000390035,0.00002665555],"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":[0.0004316114,0.0002330315,0.09996928,0.000328388,0.0002500758,0.0002291805,0.0003421244,0.03097604,0.2472339,0.001333505,0.004884886,0.6137881],"study_design_scores_gemma":[0.00002676961,0.0001801083,0.2945675,0.00004401574,0.0001380005,0.0004948716,0.0003573791,0.5653033,0.132343,0.0009180967,0.005566203,0.00006086517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.678788,0.0003847833,0.3081987,0.0001066637,0.0001026523,0.0002014851,0.001069589,0.002212388,0.008935787],"genre_scores_gemma":[0.8978983,0.0001573599,0.09951647,0.00004008528,0.00002474751,0.00005017723,0.001040831,0.00006453738,0.001207522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002232203,"threshold_uncertainty_score":0.002979338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02227770854059042,"score_gpt":0.2449088599256483,"score_spread":0.2226311513850579,"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."}}