{"id":"W2978950899","doi":"10.3390/rs11192289","title":"TCANet for Domain Adaptation of Hyperspectral Images","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Social Fund; European Regional Development Fund; Università degli Studi di Pavia; European Commission; Xunta de Galicia; University of Guelph","keywords":"Computer science; Artificial intelligence; Hyperspectral imaging; Pattern recognition (psychology); Normalization (sociology); Convolutional neural network; Robustness (evolution)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0002837803,0.0006659003,0.0003161818,0.0005147287,0.0002122669,0.0004642407,0.0008417399,0.0005193215,0.005033304],"category_scores_gemma":[0.000602574,0.0002035624,0.0005261219,0.0006352499,0.0002275327,0.0006146603,0.0007006463,0.0008875474,0.001457067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005320086,"about_ca_system_score_gemma":0.0006123541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005576981,"about_ca_topic_score_gemma":0.008116908,"domain_scores_codex":[0.9998521,0.00001587901,0.000005790008,0.00005211911,0.00005041509,0.00002370485],"domain_scores_gemma":[0.9998522,0.00002400528,0.0000177835,0.00003761049,0.00005793502,0.0000105193],"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":[0.0002372672,0.0001943977,0.001734785,0.0002024302,0.0001303513,0.0003007491,0.00006466171,0.2401543,0.07818679,0.01346182,0.02816072,0.6371718],"study_design_scores_gemma":[0.000006064798,0.00002618228,0.0005672566,0.000007032746,0.000008185084,0.00006612065,0.000009507413,0.9737385,0.01597141,0.002307517,0.007283059,0.000009256218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03710631,0.0005516864,0.9424554,0.0002134595,0.0002624851,0.0001295777,0.0008021851,0.009360137,0.00911884],"genre_scores_gemma":[0.3910356,0.0005090037,0.5842822,0.000318638,0.00007960867,0.0002763808,0.004875585,0.0005765398,0.01804653],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005576981,"threshold_uncertainty_score":0.01683813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01460186736168174,"score_gpt":0.2241065739325942,"score_spread":0.2095047065709124,"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."}}