Open AccessBook
Hyperspectral Remote Sensing
TLDR
In this article, the authors provide a holistic treatment of the hyperspectral remote sensing field with a focus on the physical principles of the field and the application of the technology to specific problems.Abstract:
Hyperspectral remote sensing is an emerging, multidisciplinary field with diverse applications that builds on the principles of material spectroscopy, radiative transfer, imaging spectrometry, and hyperspectral data processing. While there are many resources that suitably cover these areas individually and focus on specific aspects of the hyperspectral remote sensing field, this book provides a holistic treatment that thoroughly captures its multidisciplinary nature. The content is oriented toward the physical principles of hyperspectral remote sensing as opposed to applications of hyperspectral technology. Readers can expect to finish the book armed with the required knowledge to understand the immense literature available in this technology area and apply their knowledge to the understanding of material spectral properties, the design of hyperspectral systems, the analysis of hyperspectral imagery, and the application of the technology to specific problems.read more
Citations
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Journal ArticleDOI
Weighted-RXD and Linear Filter-Based RXD: Improving Background Statistics Estimation for Anomaly Detection in Hyperspectral Imagery
TL;DR: Two novel approaches are developed: weighted-RxD (W-RXD) and linear filter-based RXD (LF-R XD) aimed at improving background in RXD-based anomaly detection, indicating that the proposed approaches achieve good performance when compared with other classic approaches for anomaly detection in the literature.
Journal ArticleDOI
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review.
TL;DR: The present review is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields and the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectrals data from a multidisciplinary perspective.
Journal ArticleDOI
Deblurring and Sparse Unmixing for Hyperspectral Images
TL;DR: According to the structure of the TV regularization and sparse unmixing in the model, the convergence of the alternating direction method can be guaranteed and the method is compared to the recent Sparse Unmixing via variable Splitting Augmented Lagrangian and TV method.
Journal ArticleDOI
Meta-analysis assessing potential of steady-state chlorophyll fluorescence for remote sensing detection of plant water, temperature and nitrogen stress
Alexander Ač,Zbyněk Malenovský,Zbyněk Malenovský,Julie Olejníčková,Alexander Gallé,Alexander Gallé,Uwe Rascher,Gina H. Mohammed +7 more
TL;DR: In this article, a random effects meta-analysis of studies using both passively (sun-induced) and actively (e.g., laser-induced), measured steady-state chlorophyll fluorescence (F) for detecting stress reactions in terrestrial vegetation is presented.
Journal ArticleDOI
Strength and uncertainty of phytoplankton metrics for assessing eutrophication impacts in lakes
Laurence Carvalho,Sandra Poikane,A. Lyche Solheim,Geoff Phillips,Gábor Borics,Jordi Catalan,C. de Hoyos,Stina Drakare,Bernard Dudley,Marko Järvinen,Christophe Laplace-Treyture,Kairi Maileht,Claire McDonald,Ute Mischke,Jannicke Moe,Giuseppe Morabito,Peeter Nõges,Tiina Nõges,Ingmar Ott,Agnieszka Pasztaleniec,Birger Skjelbred,Stephen J. Thackeray +21 more
TL;DR: In this paper, the authors integrated a large volume of work on a number of measures, or metrics, developed for using phytoplankton to assess the ecological status of European lakes, as required for the Water Framework Directive.
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