Spatial multiple materials detection in hyperspectral imagery

2011 ◽  
Author(s):  
Zhenwei Shi ◽  
Zhen Qin ◽  
Shuo Yang ◽  
Zhiguo Jiang
Keyword(s):  
Hyperspectral Imagery ◽  
Multiple Materials

A quasi-Newton-based spatial multiple materials detector for hyperspectral imagery

2012 ◽  
Vol 23 (2) ◽  
pp. 403-409 ◽  
Author(s):  
Zhen Qin ◽  
Zhenwei Shi ◽  
Zhiguo Jiang
Keyword(s):  
Hyperspectral Imagery ◽  
Multiple Materials ◽  
Quasi Newton

Biodiversity estimation technique using medium spatial resolution hyperspectral imagery

2007 ◽  
Vol 13 (4) ◽  
pp. 25-39
Author(s):  
S.A. Stankevich ◽  
◽  
A.O. Kozlova ◽  
Keyword(s):  
Spatial Resolution ◽  
Hyperspectral Imagery ◽  
Estimation Technique

Automated Cartographic Feature Attribution Using Panchromatic and Hyperspectral Imagery.

1999 ◽  
Author(s):  
David M. McKeown ◽  
McGlone Jr. ◽  
Ford J. C. ◽  
Cochran Stephen J. ◽  
Shufelt Steven D. ◽  
...  
Keyword(s):  
Hyperspectral Imagery

SPICEE: An Extension of SPICE for Sparse Endmember Estimation in Hyperspectral Imagery

2016 ◽  
Vol 13 (12) ◽  
pp. 1910-1914 ◽  
Author(s):  
Seniha Esen Yuksel ◽  
Sefa Kucuk ◽  
Paul D. Gader
Keyword(s):  
Hyperspectral Imagery

Detection of gaseous plumes in airborne hyperspectral imagery

2016 ◽  
Author(s):  
Eyal Agassi ◽  
Eitan Hirsch ◽  
Martin Chamberland ◽  
Marc-André Gagnon ◽  
Holger Eichstaedt
Keyword(s):  
Hyperspectral Imagery

scholarly journals Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight

2021 ◽  
Vol 13 (15) ◽  
pp. 3024
Author(s):  
Huiqin Ma ◽  
Wenjiang Huang ◽  
Yingying Dong ◽  
Linyi Liu ◽  
Anting Guo
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Hyperspectral Imagery ◽  
Spectral Feature ◽  
Support Vector ◽  
Detection Accuracy ◽  
Feature Combination ◽  
Field Scale ◽  
Head Blight ◽  
Detection Model

Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features (WFs), in this study, we explore the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle (UAV), to detect wheat FHB. First, during the wheat filling period, two UAV-based hyperspectral images were acquired. SBs, VIs, and WFs that were sensitive to wheat FHB were extracted and optimized from the two images. Subsequently, a field-scale wheat FHB detection model was formulated, based on the optimal spectral feature combination of SBs, VIs, and WFs (SBs + VIs + WFs), using a support vector machine. Two commonly used data normalization algorithms were utilized before the construction of the model. The single WFs, and the spectral feature combination of optimal SBs and VIs (SBs + VIs), were respectively used to formulate models for comparison and testing. The results showed that the detection model based on the normalized SBs + VIs + WFs, using min–max normalization algorithm, achieved the highest R2 of 0.88 and the lowest RMSE of 2.68% among the three models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB. Combining traditional SBs and VIs with WFs can improve the detection accuracy of wheat FHB effectively.

scholarly journals Author Correction: Using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kenneth Clarke ◽  
Andrew Hennessy ◽  
Andrew McGrath ◽  
Robert Daly ◽  
Sam Gaylard ◽  
...  
Keyword(s):  
Large Scale ◽  
Hyperspectral Imagery ◽  
Genus Distribution ◽  
Seagrass Cover

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

scholarly journals Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Moisture Content ◽  
Layer Thickness ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Barrier Island ◽  
Water Layer ◽  
Radiative Transfer Model ◽  
Transfer Model

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

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