{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006107905,0.000752265,0.0006352721,0.0006224699,0.0002751456,0.0006368692,0.001381653,0.0006052191,0.001084965],"category_scores_gemma":[0.001255904,0.0002404883,0.0004616311,0.0005352535,0.0005390499,0.001456424,0.001057655,0.0006073722,0.0002603907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007644053,"about_ca_system_score_gemma":0.000687591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005310663,"about_ca_topic_score_gemma":0.006031558,"domain_scores_codex":[0.9997287,0.00003311312,0.00001402301,0.0000775923,0.00009332502,0.00005322217],"domain_scores_gemma":[0.9996892,0.0000906964,0.00004810161,0.00003830104,0.0001072506,0.00002645236],"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":[0.0005392387,0.0002180043,0.004069383,0.0001142536,0.0001227271,0.0002334115,0.00009317712,0.3438878,0.0482997,0.01136452,0.00300773,0.5880499],"study_design_scores_gemma":[0.000003110531,0.00002930967,0.0002203569,0.000002461961,0.000009913966,0.00003520157,0.000004705787,0.9903673,0.007881669,0.001181664,0.0002597387,0.000004625907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05488208,0.0002916742,0.9410671,0.0001129872,0.00004439862,0.00003855691,0.00005921298,0.00164121,0.001862772],"genre_scores_gemma":[0.8338865,0.0002168035,0.1616298,0.0001738058,0.00003154035,0.00005204193,0.0002232694,0.00008170824,0.003704494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005310663,"threshold_uncertainty_score":0.0105595,"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."}}