{"title":"Hyperspectral Sensor Characteristics","authors":"F. Ortenberg","doi":"10.1201/9781315164151-2","DOIUrl":"https://doi.org/10.1201/9781315164151-2","url":null,"abstract":"","PeriodicalId":304529,"journal":{"name":"Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130175187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Potgieter, J. Watson, B. George-Jaeggli, G. McLean, M. Eldridge, S. Chapman, K. Laws, J. Christopher, K. Chenu, A. Borrell, G. Hammer, D. Jordan
{"title":"The Use of Hyperspectral Proximal Sensing for Phenotyping of Plant Breeding Trials","authors":"A. Potgieter, J. Watson, B. George-Jaeggli, G. McLean, M. Eldridge, S. Chapman, K. Laws, J. Christopher, K. Chenu, A. Borrell, G. Hammer, D. Jordan","doi":"10.1201/9781315164151-5","DOIUrl":"https://doi.org/10.1201/9781315164151-5","url":null,"abstract":"","PeriodicalId":304529,"journal":{"name":"Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116782141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hyperspectral Remote Sensing in Global Change Studies","authors":"J. Qi, Y. Inoue, N. Wiangwang","doi":"10.1201/9781315164151-3","DOIUrl":"https://doi.org/10.1201/9781315164151-3","url":null,"abstract":"","PeriodicalId":304529,"journal":{"name":"Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127196822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Aneece, P. Thenkabail, J. G. Lyon, A. Huete, Terrence Slonecker
{"title":"Spaceborne Hyperspectral EO-1 Hyperion Data Pre-Processing","authors":"I. Aneece, P. Thenkabail, J. G. Lyon, A. Huete, Terrence Slonecker","doi":"10.1201/9781315164151-9","DOIUrl":"https://doi.org/10.1201/9781315164151-9","url":null,"abstract":"","PeriodicalId":304529,"journal":{"name":"Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122431398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Characterization of Soil Properties Using Reflectance Spectroscopy","authors":"E. Ben-Dor, S. Chabrillat, J. Demattê","doi":"10.1201/9781315164151-8","DOIUrl":"https://doi.org/10.1201/9781315164151-8","url":null,"abstract":"","PeriodicalId":304529,"journal":{"name":"Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation","volume":"283 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116093132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-07DOI: 10.1201/9781315164151-14
P. Thenkabail, J. G. Lyon, A. Huete
{"title":"Fifty-Years of Advances in Hyperspectral Remote Sensing of Agriculture and Vegetation—Summary, Insights, and Highlights of Volume I","authors":"P. Thenkabail, J. G. Lyon, A. Huete","doi":"10.1201/9781315164151-14","DOIUrl":"https://doi.org/10.1201/9781315164151-14","url":null,"abstract":"","PeriodicalId":304529,"journal":{"name":"Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126642337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Use of Spectral Databases for Remote Sensing of Agricultural Crops","authors":"A. Hueni, L. Suárez, L. Chisholm, A. Held","doi":"10.1201/9781315164151-7","DOIUrl":"https://doi.org/10.1201/9781315164151-7","url":null,"abstract":"","PeriodicalId":304529,"journal":{"name":"Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121237592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-07DOI: 10.1201/9781315164151-11
A. Plaza, J. Plaza, G. Martín, S. Sánchez
Hyperspectral imaging is concerned with the measurement, analysis, and interpretation of spectra acquired from a given scene (or specific object) at a short, medium, or long distance by an airborne or satellite sensor [1]. The concept of hyperspectral imaging originated at NASA’s Jet Propulsion Laboratory in California with the development of the Airborne visible infrared imaging spectrometer (AVIRIS), able to cover the wavelength region from 400 to 2500 nm using more than 200 spectral channels, at nominal spectral resolution of 10 nm [2]. As a result, each pixel vector collected by a hyperspectral instrument can be seen as a spectral signature or fingerprint of the underlying materials within the pixel. The special characteristics of hyperspectral data sets pose different processing problems [3], which must be necessarily tackled under specific mathematical formalisms, such as classification, segmentation, image coding, or spectral mixture analysis [4]. These problems also require specific dedicated processing software and hardware platforms. In most studies, techniques are divided into full-pixel and mixed-pixel techniques, where each pixel vector defines a spectral signature or fingerprint that uniquely characterizes the underlying materials at each site in a scene [5]. Mostly based on previous efforts in multispectral imaging, full-pixel techniques assume that each pixel vector measures the response of one single underlying material. Often, however, this is not a realistic assumption. If the spatial resolution of the sensor is not fine enough to separate different pure signature classes at a macroscopic level, these can jointly occupy a single pixel, and the resulting spectral signature will be a composite of the individual pure spectra, called endmembers in hyperspectral terminology [6]. Mixed pixels can also result when distinct materials are combined into a homogeneous or intimate mixture, which occurs independently of the spatial resolution of the sensor. contents
{"title":"Hyperspectral Data Processing Algorithms","authors":"A. Plaza, J. Plaza, G. Martín, S. Sánchez","doi":"10.1201/9781315164151-11","DOIUrl":"https://doi.org/10.1201/9781315164151-11","url":null,"abstract":"Hyperspectral imaging is concerned with the measurement, analysis, and interpretation of spectra acquired from a given scene (or specific object) at a short, medium, or long distance by an airborne or satellite sensor [1]. The concept of hyperspectral imaging originated at NASA’s Jet Propulsion Laboratory in California with the development of the Airborne visible infrared imaging spectrometer (AVIRIS), able to cover the wavelength region from 400 to 2500 nm using more than 200 spectral channels, at nominal spectral resolution of 10 nm [2]. As a result, each pixel vector collected by a hyperspectral instrument can be seen as a spectral signature or fingerprint of the underlying materials within the pixel. The special characteristics of hyperspectral data sets pose different processing problems [3], which must be necessarily tackled under specific mathematical formalisms, such as classification, segmentation, image coding, or spectral mixture analysis [4]. These problems also require specific dedicated processing software and hardware platforms. In most studies, techniques are divided into full-pixel and mixed-pixel techniques, where each pixel vector defines a spectral signature or fingerprint that uniquely characterizes the underlying materials at each site in a scene [5]. Mostly based on previous efforts in multispectral imaging, full-pixel techniques assume that each pixel vector measures the response of one single underlying material. Often, however, this is not a realistic assumption. If the spatial resolution of the sensor is not fine enough to separate different pure signature classes at a macroscopic level, these can jointly occupy a single pixel, and the resulting spectral signature will be a composite of the individual pure spectra, called endmembers in hyperspectral terminology [6]. Mixed pixels can also result when distinct materials are combined into a homogeneous or intimate mixture, which occurs independently of the spatial resolution of the sensor. contents","PeriodicalId":304529,"journal":{"name":"Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123993865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-07DOI: 10.1201/9781315164151-10
S. Bajwa, Yu Zhang, A. Shirzadifar
{"title":"Hyperspectral Image Data Mining","authors":"S. Bajwa, Yu Zhang, A. Shirzadifar","doi":"10.1201/9781315164151-10","DOIUrl":"https://doi.org/10.1201/9781315164151-10","url":null,"abstract":"","PeriodicalId":304529,"journal":{"name":"Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123511148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-07DOI: 10.1201/9781315164151-12
M. A. Hoque, S. Phinn
{"title":"Methods for Linking Drone and Field Hyperspectral Data to Satellite Data","authors":"M. A. Hoque, S. Phinn","doi":"10.1201/9781315164151-12","DOIUrl":"https://doi.org/10.1201/9781315164151-12","url":null,"abstract":"","PeriodicalId":304529,"journal":{"name":"Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129421613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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