{"id":"W2897552941","doi":"10.3390/rs10101664","title":"Change Detection Based on Multi-Feature Clustering Using Differential Evolution for Landsat Imagery","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Aeronautics and Space Administration; Natural Science Foundation of Hubei Province; China University of Mining and Technology; National Natural Science Foundation of China; U.S. Geological Survey","keywords":"Cluster analysis; Computer science; Remote sensing; Change detection; Pattern recognition (psychology); Land cover; Normalized Difference Vegetation Index; Differential evolution; Artificial intelligence; Data mining; Geology; Land use","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":[0.0005237772,0.000525891,0.0006304766,0.001800449,0.0004634775,0.0005714216,0.0008427744,0.0004871048,0.0003435997],"category_scores_gemma":[0.001271849,0.0002925079,0.0008471165,0.001507999,0.0003908642,0.0007673523,0.0004726708,0.0004530682,0.0001331974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007309334,"about_ca_system_score_gemma":0.0004372692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005361142,"about_ca_topic_score_gemma":0.004854802,"domain_scores_codex":[0.9993693,0.00006063799,0.00003641328,0.0001993092,0.0002891376,0.00004511233],"domain_scores_gemma":[0.9995158,0.0001353545,0.00007227527,0.00006185383,0.0001915933,0.00002306416],"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":[0.0002198345,0.0002051907,0.006464009,0.0001544013,0.0002081046,0.0002051615,0.0003106403,0.1664973,0.09947886,0.002555261,0.00123791,0.7224633],"study_design_scores_gemma":[0.000009178747,0.00003983364,0.004858448,0.000003880012,0.00001944622,0.0000998231,0.00002600351,0.9765534,0.01722294,0.0005972443,0.000542069,0.00002779386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09427278,0.0001667389,0.9038214,0.00007422346,0.00003124142,0.00008272033,0.00005776109,0.0007484603,0.000744684],"genre_scores_gemma":[0.4935491,0.0001340994,0.5048446,0.00004979399,0.00002038786,0.0001114274,0.0002763062,0.00007174398,0.0009425285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005361142,"threshold_uncertainty_score":0.01065987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04586090318448567,"score_gpt":0.2653971008221552,"score_spread":0.2195361976376695,"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."}}