{"id":"W4388024024","doi":"10.18280/ts.400512","title":"Multiscale Feature Fusion for Hyperspectral Image Classification Using Hybrid 3D-2D Depthwise Separable Convolution Networks","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Artificial intelligence; Pattern recognition (psychology); Convolution (computer science); Separable space; Feature (linguistics); Fusion; Image (mathematics); Computer science; Image fusion; Mathematics; Artificial neural network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0005368507,0.0009094281,0.0007028275,0.00086025,0.0002409781,0.0005315079,0.0007420506,0.0005138901,0.001335593],"category_scores_gemma":[0.0008679231,0.000246905,0.0008613729,0.0006623451,0.0002715243,0.0009607624,0.001070399,0.0006989114,0.0005320763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005676497,"about_ca_system_score_gemma":0.000580716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005536236,"about_ca_topic_score_gemma":0.006971021,"domain_scores_codex":[0.9997328,0.00002861901,0.00001253931,0.00007073174,0.0001075271,0.00004780785],"domain_scores_gemma":[0.9997873,0.00004763769,0.00002940341,0.00003341536,0.00008523672,0.00001708246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003637899,0.0002391323,0.004423533,0.000094824,0.0001678655,0.0001758279,0.0001094945,0.2441117,0.06290251,0.00328274,0.003959716,0.6801689],"study_design_scores_gemma":[0.000003728973,0.00003108781,0.0008837935,0.000003835192,0.00001341046,0.00002784013,0.00001262244,0.9917162,0.005888454,0.0007779708,0.000634079,0.000007017169],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1571728,0.0007201162,0.8359636,0.000238222,0.00007816611,0.00007482252,0.0004134828,0.002398104,0.002940588],"genre_scores_gemma":[0.7601166,0.0003619679,0.2332999,0.0001677955,0.00004799534,0.0001103192,0.001682935,0.00009283674,0.004119743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005536236,"threshold_uncertainty_score":0.01100802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03082173083549894,"score_gpt":0.2589417004181414,"score_spread":0.2281199695826424,"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."}}