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While common DR methods use linear algebra, we propose a multilinear algebra method to jointly achieve denoising reduction and DR. Multilinear tools consider HSI data as a whole by processing jointly spatial and spectral ways. The lower rank-(K ₁ , K ₂ , K ₃ ) tensor approximation [LRTA-(K ₁ , K ₂ , K ₃ )] was successfully applied to denoise multiway data such as color images. First, we demonstrate that the LRTA-(K ₁ , K ₂ , K ₃ ) performs well as a denoising preprocessing to improve classification results. Then, we propose a novel method, referred to as LRTA _dr -(K ₁ , K ₂ , D ₃ ), which performs both spatial lower rank approximation and spectral DR. The classification algorithm Spectral Angle Mapper is applied to the output of the following three DR and noise reduction methods to compare their efficiency: the proposed LRTA _dr -(K ₁ , K ₂ , D ₃ ), PCA _dr , and PCA _dr associated with Wiener filtering or soft shrinkage of wavelet transform coefficients.","publishedDate","2008-04-01","publishedYear",2008,"venue","IEEE Geoscience and Remote Sensing Letters",314,"authors","keywords",[172,173,174],"pdfUrl","topVenue","Image and Signal Denoising Methods","Sparse and Compressive Sensing Techniques","Tensor decomposition and applications",{"_59":176,"_61":177},[],[178],{"_64":179,"_66":180,"_68":181,"_70":182},1784462400524,{"_73":184,"_75":76,"_77":179,"_79":-5,"_80":26,"_81":26,"_82":26,"_83":-5,"_84":-5,"_85":86,"_35":87,"_88":89},[72,183],"[\"portal\",\"industries\"]","industries",[185,186,187,188,189,190,191,192,193,194,195,196],{"_15":258,"_29":259,"_31":-5,"_32":260,"_33":261,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":200,"_42":-5,"_44":-5,"_201":202,"_203":-5,"_204":76,"_205":206,"_207":262,"_209":26,"_210":86,"_211":26,"_212":-5,"_57":259,"_58":261},{"_15":254,"_29":255,"_31":-5,"_32":-5,"_33":256,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":200,"_42":-5,"_44":-5,"_201":202,"_203":-5,"_204":76,"_205":206,"_207":257,"_209":26,"_210":86,"_211":26,"_212":-5,"_57":255,"_58":256},{"_15":248,"_29":249,"_31":-5,"_32":250,"_33":251,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":200,"_42":-5,"_44":-5,"_201":202,"_203":-5,"_204":76,"_205":206,"_207":252,"_209":26,"_210":86,"_211":26,"_212":-5,"_57":249,"_58":251},{"_15":244,"_29":245,"_31":-5,"_32":-5,"_33":246,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":200,"_42":-5,"_44":-5,"_201":202,"_203":-5,"_204":76,"_205":206,"_207":247,"_209":26,"_210":86,"_211":26,"_212":-5,"_57":245,"_58":246},{"_15":240,"_29":241,"_31":-5,"_32":-5,"_33":242,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":200,"_42":-5,"_44":-5,"_201":202,"_203":-5,"_204":76,"_205":206,"_207":243,"_209":26,"_210":86,"_211":26,"_212":-5,"_57":241,"_58":242},{"_15":234,"_29":235,"_31":-5,"_32":236,"_33":237,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":200,"_42":-5,"_44":-5,"_201":202,"_203":-5,"_204":76,"_205":206,"_207":238,"_209":26,"_210":86,"_211":26,"_212":-5,"_57":235,"_58":237},{"_15":229,"_29":230,"_31":-5,"_32":-5,"_33":231,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":200,"_42":-5,"_44":-5,"_201":202,"_203":-5,"_204":76,"_205":206,"_207":232,"_209":26,"_210":233,"_211":26,"_212":-5,"_57":230,"_58":231},{"_15":225,"_29":226,"_31":-5,"_32":-5,"_33":227,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":200,"_42":-5,"_44":-5,"_201":202,"_203":-5,"_204":76,"_205":206,"_207":228,"_209":26,"_210":86,"_211":26,"_212":-5,"_57":226,"_58":227},{"_15":221,"_29":222,"_31":-5,"_32":-5,"_33":223,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":200,"_42":-5,"_44":-5,"_201":202,"_203":-5,"_204":76,"_205":206,"_207":224,"_209":26,"_210":86,"_211":26,"_212":-5,"_57":222,"_58":223},{"_15":217,"_29":218,"_31":-5,"_32":-5,"_33":219,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":200,"_42":-5,"_44":-5,"_201":202,"_203":-5,"_204":76,"_205":206,"_207":220,"_209":26,"_210":86,"_211":26,"_212":-5,"_57":218,"_58":219},{"_15":213,"_29":214,"_31":-5,"_32":-5,"_33":215,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":200,"_42":-5,"_44":-5,"_201":202,"_203":-5,"_204":76,"_205":206,"_207":216,"_209":26,"_210":86,"_211":26,"_212":-5,"_57":214,"_58":215},{"_15":197,"_29":198,"_31":-5,"_32":-5,"_33":199,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":200,"_42":-5,"_44":-5,"_201":202,"_203":-5,"_204":76,"_205":206,"_207":208,"_209":26,"_210":86,"_211":26,"_212":-5,"_57":198,"_58":199},"tax_ind_smartphone","智能手机","Smartphone","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/智能手机","sortOrder","isLeaf","entityCount","children","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算",true,"tax_ind_blockchain","区块链",[239],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[253],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[263,264],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_267":24},"SITE_BASE_URL"]

S. Bourennane 人物

S. Bourennane · 相关文章