{"id":"W4319601347","doi":"10.5194/isprs-archives-xlviii-4-w6-2022-179-2023","title":"HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER)","year":2023,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Hyperspectral imaging; Pattern recognition (psychology); Artificial intelligence; Perceptron; Multilayer perceptron; Computer science; Curse of dimensionality; Contextual image classification; Image (mathematics); Artificial neural network","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":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.0014436,0.0005223515,0.0004686823,0.00133728,0.001339958,0.0009614108,0.001547145,0.0001369734,0.000008842931],"category_scores_gemma":[0.0007291812,0.0003620432,0.0004199415,0.001542496,0.002932525,0.0007710297,0.0006431865,0.0005730949,0.00001514045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001200448,"about_ca_system_score_gemma":0.0002033929,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4667804,"about_ca_topic_score_gemma":0.06439477,"domain_scores_codex":[0.9951459,0.0002938483,0.001561959,0.0004802281,0.001833907,0.0006841869],"domain_scores_gemma":[0.9968732,0.0007633662,0.001170939,0.000639945,0.0003726065,0.0001799166],"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":[0.00007021146,0.00001629901,0.0001567533,0.0000550373,0.00008840676,4.618688e-7,0.003499747,0.01062884,0.05913051,0.000005096896,0.00007817462,0.9262705],"study_design_scores_gemma":[0.0007091437,0.00006563398,0.01074024,0.0002916887,0.00005833974,0.0001337859,0.003219189,0.9653259,0.01533029,0.001428848,0.002302647,0.0003942889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05806712,0.0000275202,0.9310054,0.002522496,0.002423648,0.0007162986,0.00009829133,0.0001813734,0.004957841],"genre_scores_gemma":[0.987245,0.0002087781,0.01174759,0.0003802612,0.0001956445,4.330248e-7,0.00007271657,0.00002814646,0.0001214059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9546971,"threshold_uncertainty_score":0.9999602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0391927244795356,"score_gpt":0.2813689944002686,"score_spread":0.242176269920733,"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."}}