{"title":"高光谱图像的无损压缩","authors":"Raffaele Pizzolante","doi":"10.1109/CCP.2011.31","DOIUrl":null,"url":null,"abstract":"In this paper we review the Spectral oriented Least SQuares (SLSQ) algorithm : an efficient and low complexity algorithm for Hyper spectral Image loss less compression, presented in [2]. Subsequently, we consider two important measures : Pearson's Correlation and Bhattacharyya distance and describe a band ordering approach based on this distances. Finally, we report experimental results achieved with a Java-based implementation of SLSQ on data cubes acquired by NASA JPL's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS).","PeriodicalId":167131,"journal":{"name":"2011 First International Conference on Data Compression, Communications and Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Lossless Compression of Hyperspectral Imagery\",\"authors\":\"Raffaele Pizzolante\",\"doi\":\"10.1109/CCP.2011.31\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we review the Spectral oriented Least SQuares (SLSQ) algorithm : an efficient and low complexity algorithm for Hyper spectral Image loss less compression, presented in [2]. Subsequently, we consider two important measures : Pearson's Correlation and Bhattacharyya distance and describe a band ordering approach based on this distances. Finally, we report experimental results achieved with a Java-based implementation of SLSQ on data cubes acquired by NASA JPL's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS).\",\"PeriodicalId\":167131,\"journal\":{\"name\":\"2011 First International Conference on Data Compression, Communications and Processing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 First International Conference on Data Compression, Communications and Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCP.2011.31\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 First International Conference on Data Compression, Communications and Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCP.2011.31","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper we review the Spectral oriented Least SQuares (SLSQ) algorithm : an efficient and low complexity algorithm for Hyper spectral Image loss less compression, presented in [2]. Subsequently, we consider two important measures : Pearson's Correlation and Bhattacharyya distance and describe a band ordering approach based on this distances. Finally, we report experimental results achieved with a Java-based implementation of SLSQ on data cubes acquired by NASA JPL's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS).