Three decades of hyperspectral remote sensing of the Earth: a personal view.

By A. Goetz. 2009

833 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/45f9862470a7585f75bedc90933277507c87e281

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Citations in the corpus

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, methodology): “The first developments of imaging spectrometry in remote sensing applications started using satellites, more specifically to support Landsat-1 data analysis through field spectral measurements, according to [19].”
Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows (2018, background): “Commonly, they are distinguished by the arrangement and/or a number of bands [18,19].”
Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data (2010, background): “analyses are performed, the spectral and spatial resolution requirements can be very high, e.g., in urban area mapping, the characterization of mineral composition, or in plant-type differentiation [1], [2].”
Medical hyperspectral imaging: a review (2014, background): “Hyperspectral imaging (HSI), also called imaging spectrometer,(1) originated from remote sensing and has been explored for various applications by NASA.(2) With the advantage of”
Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems (2019, background): “Typically, sensors are characterized by the arrangement and/or the number of spectral bands involved in the instrumental architecture [10,16], as well as the applied image capture method.”
Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance (2015, methodology): “Following the core definition of imaging spectroscopy by Goetz (2009) and Goetz et al. (1985) HS cameras have to record many continuous, registered (characterized), spectral bands.”
Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks (2014, background): “International Journal of Remote Sensing, 2014 Vol. 35, No. 18, 6599–6647, http://dx.doi.org/10.1080/01431161.2014.964349 © 2014 The Author(s).”
Feature Mining for Hyperspectral Image Classification (2013, background): “Hyperspectral airborne or spaceborne sensors, such as AVIRIS, HyMap, HYDICE, and Hyperion, have two properties that are superior to multispectral devices [1]–[3].”
Review of optical-based remote sensing for plant trait mapping (2013, background): “Therefore imaging spectroscopy has emerged to overcome spectral limitations of multispectral systems (Goetz, 2009; Schaepman, 2009).”
Review of snapshot spectral imaging technologies (2013, background): “boost with the launch of the Landsat remote sensing satellite in 1972.(30) With the launch of Landsat also came the develop-”
Review of spectral imaging technology in biomedical engineering: achievements and challenges (2013, background): “has been originally substantiated within remote sensing fields, such as airborne surveillance or satellite imaging, and has been successfully applied to mining and geology, agriculture, military, environmental, and global change research.(2) According to the electromagnetic theory, different biochem”
NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms (2021, background): “Over the past four decades, VSWIR imaging spectroscopy has been successfully applied to geologic and mineral deposit studies in wellexposed, mid-latitude areas at local scale (Coulter et al., 2007; Goetz, 2009; van der Meer et al., 2012; Swayze et al., 2014; Cudahy, 2016).”
A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification (2014, background): “H YPERSPECTRAL imaging has two properties that are superior to multispectral devices [1], [2].”
Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition (2017, background): “2779539 exploration, to name but a few examples [1], [2].”
Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review (2020, background): “Left: The wavelength acquisition of spectral bands for multispectral (below) and hyperspectral sampling (above) (taken from [30]).”
Hyperspectral Band Selection: A Review (2019, background): “THE NECESSITY OF HYPERSPECTRAL BAND SELECTION Due to the advancement of imaging spectrometer technology, a typical hyperspectral sensor now collects the spectral reflectance of ground objects on Earth’s surface using hundreds of narrow bands [1], [2].”