{"id":"W2940368062","doi":"10.1080/01431161.2019.1602792","title":"Unsupervised change detection of VHR remote sensing images based on multi-resolution Markov Random Field in wavelet domain","year":2019,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Wavelet; Change detection; Markov random field; Artificial intelligence; Pattern recognition (psychology); Computer science; Wavelet transform; Scale (ratio); Robustness (evolution); Feature (linguistics); Remote sensing; Computer vision; Image (mathematics); Geography; Image segmentation","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":[0.0009568945,0.0006709446,0.0009708873,0.002154117,0.0003324065,0.000713713,0.00118007,0.0006815042,0.0004603449],"category_scores_gemma":[0.001942541,0.0004502574,0.001401872,0.000971428,0.0006021851,0.00139346,0.0005584118,0.0009600638,0.0002887975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005252798,"about_ca_system_score_gemma":0.0005703064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003178264,"about_ca_topic_score_gemma":0.003566148,"domain_scores_codex":[0.9991688,0.0001474988,0.00004129605,0.0002713089,0.0002926032,0.00007841869],"domain_scores_gemma":[0.9990823,0.0003436312,0.0002063305,0.0001110623,0.0002140862,0.00004257903],"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":[0.0002670119,0.0002844851,0.01026695,0.000312346,0.0002956821,0.00034129,0.0003050721,0.2587874,0.07234887,0.01100893,0.002523256,0.6432588],"study_design_scores_gemma":[0.000009558316,0.00004422933,0.003254094,0.000009955004,0.00003290731,0.0001730851,0.00002826375,0.9856857,0.007558743,0.002379722,0.0007948282,0.00002892495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01762777,0.0001347721,0.9814028,0.00005496367,0.00001824481,0.00003395135,0.00004471396,0.0003405336,0.0003422966],"genre_scores_gemma":[0.4382055,0.0005273451,0.55827,0.0001280657,0.0001242958,0.0001508131,0.000583633,0.0001811817,0.001829152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003178264,"threshold_uncertainty_score":0.006319523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01769889526432237,"score_gpt":0.2504826627372186,"score_spread":0.2327837674728963,"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."}}