{"id":"W4417131117","doi":"10.1109/tnnls.2025.3634765","title":"Hyperspectral Anomaly Detection via Hybrid Convolutional and Transformer-Based U-Net With Error Attention Mechanism","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Natural Science Foundation of Jiangxi Province; Nanjing University of Aeronautics and Astronautics; National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Anomaly detection; Pattern recognition (psychology); Pixel; Anomaly (physics); Feature (linguistics); Feature extraction; Convolution (computer science)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000152436,0.000217299,0.0002075948,0.0001882358,0.0003885616,0.0001169412,0.0000391094,0.0001043423,0.000002908939],"category_scores_gemma":[0.000001811569,0.0002070493,0.00005364624,0.0002209661,0.00007067949,0.0001592358,2.761525e-7,0.000543188,0.000001537761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008964017,"about_ca_system_score_gemma":0.00001157763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007553465,"about_ca_topic_score_gemma":0.00004974958,"domain_scores_codex":[0.998973,0.0001163383,0.0002374704,0.0002915408,0.0001363948,0.0002452268],"domain_scores_gemma":[0.9996242,0.00009344475,0.0000465561,0.0001130603,0.00005253907,0.00007021434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008523089,0.00001753992,0.00005354311,0.00008860922,0.00005786546,0.000004209451,0.0000248509,0.9622989,0.02288994,0.0000239236,0.000006386031,0.01444895],"study_design_scores_gemma":[0.0006629428,0.0002022359,0.00156074,0.0001296688,0.00008483956,0.0001001936,0.00009449838,0.9939072,0.002936857,0.000005978574,0.0001140803,0.0002008235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3578171,0.0001606778,0.6408954,0.00007458457,0.0005347754,0.0002237929,0.000002365472,0.0002409145,0.00005036546],"genre_scores_gemma":[0.9993306,0.00003648608,0.0002309688,0.00002381293,0.00006015398,0.00002626839,0.00000794181,0.00003466033,0.000249092],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6415135,"threshold_uncertainty_score":0.8443224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007103225968809235,"score_gpt":0.1937824893598477,"score_spread":0.1866792633910384,"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."}}