{"id":"W4362701204","doi":"10.1016/j.eswa.2023.120072","title":"Spectral unmixing based random forest classifier for detecting surface water changes in multitemporal pansharpened Landsat image","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Cégep de la Gaspésie et des Îles","funders":"","keywords":"Thematic Mapper; Endmember; Multispectral image; Remote sensing; Random forest; Change detection; Pixel; Image fusion; Thematic map; Multispectral pattern recognition; Computer science; Environmental science; Cohen's kappa; Artificial intelligence; Pattern recognition (psychology); Satellite imagery; Cartography; Geography; Image (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":[0.0005221377,0.0005036939,0.0004342638,0.001116405,0.0003316047,0.0003011175,0.0004510078,0.0004460559,0.0008818981],"category_scores_gemma":[0.0004952847,0.0001779159,0.000600979,0.000585967,0.0001508292,0.0005354961,0.0001817963,0.0004020847,0.0004874278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001756273,"about_ca_system_score_gemma":0.000397307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004461801,"about_ca_topic_score_gemma":0.007307789,"domain_scores_codex":[0.9997839,0.000022763,0.00001347841,0.00006330779,0.00008255436,0.00003398065],"domain_scores_gemma":[0.9997818,0.00005744373,0.00001806272,0.00001840395,0.0001137774,0.00001041344],"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":[0.000616923,0.0004393001,0.007089997,0.0001751743,0.0001207237,0.0001311153,0.00007996838,0.03865907,0.1419019,0.0007445866,0.002780609,0.8072606],"study_design_scores_gemma":[0.00002490485,0.0001555891,0.01289028,0.00001236873,0.0001162759,0.0001433341,0.00005428433,0.9449577,0.03977399,0.0005166563,0.001331368,0.00002322853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3988099,0.0008901897,0.5942577,0.0001083204,0.0001445528,0.0001261429,0.0005922052,0.002836476,0.002234529],"genre_scores_gemma":[0.7660181,0.000409109,0.2284609,0.00005606552,0.0000576251,0.0000951587,0.001619137,0.00008864557,0.003195281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004461801,"threshold_uncertainty_score":0.008871675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02496666293519973,"score_gpt":0.2535996270485594,"score_spread":0.2286329641133597,"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."}}