{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001062889,0.0004281538,0.0004440128,0.001152335,0.0007971509,0.0006444647,0.001761156,0.0001169265,0.0000055519],"category_scores_gemma":[0.0006395311,0.0002753899,0.0002827505,0.001478467,0.001973871,0.0009593238,0.0007122457,0.0004226299,0.000003464698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006961296,"about_ca_system_score_gemma":0.000146662,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8056692,"about_ca_topic_score_gemma":0.085023,"domain_scores_codex":[0.9959055,0.0002564137,0.001453627,0.0003865591,0.00152745,0.0004705076],"domain_scores_gemma":[0.9967498,0.0006690159,0.001337267,0.0006798899,0.0004189533,0.0001450749],"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":[0.00004734343,0.00001972733,0.0006613443,0.00007074499,0.00008088921,1.217469e-7,0.001317157,0.005893285,0.0302601,7.75556e-7,0.000009351166,0.9616392],"study_design_scores_gemma":[0.0007371517,0.00007944557,0.02827217,0.0003044291,0.00005369172,0.00007037255,0.00105502,0.9423705,0.02560559,0.0003800835,0.0007728758,0.0002986111],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06926549,0.00005941359,0.9262096,0.000925581,0.001558914,0.0008267377,0.0001402266,0.00007383239,0.0009402442],"genre_scores_gemma":[0.988831,0.00008956794,0.01048962,0.0002986315,0.0001427148,2.23717e-7,0.0001116845,0.00001675584,0.00001977919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9613405,"threshold_uncertainty_score":0.9999698,"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."}}