{"id":"W4414125552","doi":"10.1080/07038992.2025.2551533","title":"MSDANet: A Multiscale Dual-Channel Spatial Attention Network with Depthwise Separable Convolution for Hyperspectral Image Classification","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Guangdong Province","keywords":"Hyperspectral imaging; Pattern recognition (psychology); Feature extraction; Benchmark (surveying); Contextual image classification; Convolution (computer science); Identification (biology); Feature (linguistics); Spatial analysis; Convolutional 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":[0.0004235094,0.0008506545,0.0006676822,0.0006285096,0.00030782,0.0005956023,0.001440243,0.000684502,0.001638798],"category_scores_gemma":[0.001040472,0.0002880163,0.0007024127,0.0006824281,0.000376269,0.001048682,0.001139982,0.0009307904,0.0004834973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008140404,"about_ca_system_score_gemma":0.0007890896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007277193,"about_ca_topic_score_gemma":0.01136439,"domain_scores_codex":[0.9997801,0.00003581119,0.00001004241,0.00006232564,0.00006579278,0.00004593892],"domain_scores_gemma":[0.9997509,0.00007873873,0.0000277616,0.00002688179,0.00009175514,0.00002394697],"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":[0.0003044821,0.0002194269,0.003355581,0.0001579462,0.0001951927,0.0001909589,0.0001072153,0.3450745,0.03466839,0.007817133,0.01077181,0.5971374],"study_design_scores_gemma":[0.000005313937,0.00003007764,0.000304921,0.000005217869,0.00001653055,0.00003090641,0.00001008565,0.9931262,0.003250717,0.002089094,0.001124224,0.000006691697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04323475,0.001067926,0.9496647,0.000345694,0.0001487819,0.00006122619,0.0002645467,0.00178756,0.003424699],"genre_scores_gemma":[0.7323251,0.0008805572,0.2536511,0.000675676,0.0001540193,0.000172819,0.001288483,0.000175161,0.0106771],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007277193,"threshold_uncertainty_score":0.01446968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01335939870148839,"score_gpt":0.2247656011239972,"score_spread":0.2114062024225088,"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."}}