{"id":"W2503525907","doi":"10.1117/3.1000981.ch11","title":"Increasing the Signal-to-Noise Ratio of Satellite Sensors Using Digital Denoising","year":2013,"lang":"en","type":"book-chapter","venue":"Society of Photo-Optical Instrumentation Engineers eBooks","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Space Agency","funders":"","keywords":"Satellite; Computer science; Noise (video); SIGNAL (programming language); Signal-to-noise ratio (imaging); Remote sensing; Real-time computing; Electronic engineering; Detector; Artificial intelligence; Telecommunications; Engineering; Geography; Aerospace engineering","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":[0.0004199202,0.000742464,0.0007458584,0.001414309,0.0002044278,0.001388675,0.0009179802,0.001063205,0.008650612],"category_scores_gemma":[0.001022093,0.0004565589,0.0005398552,0.001667563,0.0006392858,0.00214328,0.0006369668,0.001523107,0.007170056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007218978,"about_ca_system_score_gemma":0.0002341644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005016329,"about_ca_topic_score_gemma":0.0005521739,"domain_scores_codex":[0.9994621,0.00003177794,0.00002159499,0.0001079197,0.0003608993,0.00001575805],"domain_scores_gemma":[0.9996638,0.0001104018,0.00002162382,0.00003415718,0.0001577896,0.00001221763],"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":[0.0001155489,0.00004470416,0.0003408591,0.001714758,0.00005264118,0.0003084658,0.000214059,0.01032416,0.1203647,0.03685732,0.04393752,0.7857254],"study_design_scores_gemma":[0.00001794212,0.0001916211,0.001196404,0.0004048279,0.00009186036,0.001695261,0.0000972615,0.07752603,0.1052029,0.02962185,0.7838801,0.00007407653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009013552,0.05361181,0.7778232,0.001809315,0.002594339,0.0001376421,0.0003077714,0.002918804,0.1517836],"genre_scores_gemma":[0.09587626,0.06158274,0.5651525,0.002337126,0.001745594,0.0002073212,0.0009895929,0.001647593,0.2704613],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008650612,"threshold_uncertainty_score":0.02893913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01427349952562943,"score_gpt":0.2122306157812379,"score_spread":0.1979571162556085,"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."}}