{"id":"W2100717729","doi":"10.1109/igarss.2006.292","title":"Sensitivity of Spectral Unmixing Analysis to a Spectrally Dependent Gain Error in Hyperspectral Data","year":2006,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Hyperspectral imaging; MODTRAN; Remote sensing; Radiance; Sensitivity (control systems); Full spectral imaging; Calibration; Spectral sensitivity; Radiometric calibration; Atmospheric correction; Computer science; Mathematics; Reflectivity; Optics; Statistics; Geology; Physics; Wavelength","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":[0.0006213372,0.0001850735,0.0003523539,0.000558479,0.00001985284,0.00004327785,0.0002199655,0.00006679657,0.00003198388],"category_scores_gemma":[0.00007659639,0.0001992324,0.00008346039,0.00122607,0.0000290871,0.0002076551,0.00006193217,0.0001640147,0.00002555947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002500209,"about_ca_system_score_gemma":0.00002548803,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002558052,"about_ca_topic_score_gemma":0.02489454,"domain_scores_codex":[0.9984602,0.00007683034,0.0004170362,0.0004098301,0.0002658344,0.000370285],"domain_scores_gemma":[0.9988793,0.00009027209,0.00004400544,0.0008890462,0.00003921771,0.0000581856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001404292,0.00007364021,0.006334144,0.00001874359,0.0001444686,0.00008581471,0.00011389,0.3866837,0.604765,0.0004852509,0.000460899,0.000820445],"study_design_scores_gemma":[0.0001868433,0.00001144706,0.1993746,0.00001126302,0.0001146972,0.00001293834,0.0001355959,0.7064807,0.09335192,0.00007194179,0.00002035109,0.000227601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7365875,0.00002541401,0.2525649,0.0002739235,0.00005772644,0.0001768388,0.00003070072,0.0002004777,0.01008253],"genre_scores_gemma":[0.9291856,0.000003212638,0.07045794,0.00001867174,0.0000736045,7.343359e-7,0.00007973519,0.00002713581,0.0001533504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.511413,"threshold_uncertainty_score":0.9928986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02590164574676216,"score_gpt":0.2595090902318524,"score_spread":0.2336074444850903,"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."}}