{"id":"W2122809155","doi":"10.5194/isprsarchives-xl-1-w3-169-2013","title":"KERNEL-BASED UNSUPERVISED CHANGE DETECTION OF AGRICULTURAL LANDS USING MULTI-TEMPORAL POLARIMETRIC SAR DATA","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":"Remote-Sensing Image Classification","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Agriculture and Agri-Food Canada; California Institute of Technology; National Aeronautics and Space Administration","keywords":"Cluster analysis; Computer science; Kernel (algebra); Change detection; Initialization; Pattern recognition (psychology); Artificial intelligence; Polarimetry; Remote sensing; Kernel method; Support vector machine; Mathematics; Geography; Scattering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003478853,0.0002826888,0.0003057585,0.001605111,0.0001725954,0.0005131461,0.000315531,0.000250016,0.0004021115],"category_scores_gemma":[0.0009853838,0.0001257502,0.0003633661,0.0008774578,0.0002198871,0.000619872,0.0003493259,0.000302087,0.000258796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002809723,"about_ca_system_score_gemma":0.0002977574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002435745,"about_ca_topic_score_gemma":0.003271968,"domain_scores_codex":[0.9997039,0.00005789385,0.00002069548,0.0000880845,0.00009081692,0.00003845291],"domain_scores_gemma":[0.9994598,0.0001315814,0.0001038698,0.00007522964,0.0002019149,0.00002754959],"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":[0.0007102491,0.0004838356,0.03750821,0.0001767896,0.0002541212,0.0003626778,0.0003320465,0.1839121,0.1186876,0.003202558,0.002717488,0.6516523],"study_design_scores_gemma":[0.000008146186,0.00005641948,0.02531896,0.000005638029,0.00002591528,0.0001145013,0.00007726543,0.9528388,0.01971525,0.0008098247,0.001012187,0.00001711352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4193405,0.0001595078,0.5777918,0.00008328471,0.00003374815,0.00006382105,0.0003699706,0.0009132494,0.001244158],"genre_scores_gemma":[0.8943834,0.00008822469,0.1033713,0.00001489042,0.00001624078,0.00003523111,0.0009862194,0.00004651181,0.001058029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002435745,"threshold_uncertainty_score":0.004843116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0454165728787396,"score_gpt":0.2628624145777052,"score_spread":0.2174458416989656,"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."}}