Hyperspectral Imagery
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2021 ◽  
pp. 1-18
Srinivas Kolluru ◽  
Shirishkumar S. Gedam ◽  
Arun B. Inamdar

Prabira Kumar Sethy ◽  
Chanki Pandey ◽  
Yogesh Kumar Sahu ◽  
Santi Kumari Behera

2021 ◽  
Vol 13 (21) ◽  
pp. 4489
Robert Chancia ◽  
Terry Bates ◽  
Justine Vanden Heuvel ◽  
Jan van Aardt

This study aimed to identify the optimal sets of spectral bands for monitoring multiple grapevine nutrients in vineyards. We used spectral data spanning 400–2500 nm and leaf samples from 100 Concord grapevine canopies, lab-analyzed for six key nutrient values, to select the optimal bands for the nutrient regression models. The canopy spectral data were obtained with unmanned aerial systems (UAS), using push-broom imaging spectrometers (hyperspectral sensors). The novel use of UAS-based hyperspectral imagery to assess the grapevine nutrient status fills the gap between in situ spectral sampling and UAS-based multispectral imaging, avoiding their inherent trade-offs between spatial and spectral resolution. We found that an ensemble feature ranking method, utilizing six different machine learning feature selection methods, produced similar regression results as the standard PLSR feature selection and regression while generally selecting fewer wavelengths. We identified a set of biochemically consistent bands (606, 641, and 1494 nm) to predict the nitrogen content with an RMSE of 0.17% (using leave-one-out cross-validation) in samples with nitrogen contents ranging between 2.4 and 3.6%. Further studying is needed to confirm the relevance and consistency of the wavelengths selected for each nutrient model, but ensemble feature selection showed promise in identifying stable sets of wavelengths for assessing grapevine nutrient contents from canopy spectra.

2021 ◽  
Vol 13 (21) ◽  
pp. 4453
Lyuzhou Gao ◽  
Liqin Cao ◽  
Yanfei Zhong ◽  
Zhaoyang Jia

Emissivity information derived from thermal infrared (TIR) hyperspectral imagery has the advantages of both high spatial and spectral resolutions, which facilitate the detection and identification of the subtle spectral features of ground targets. Despite the emergence of several different TIR hyperspectral imagers, there are still no universal spectral emissivity measurement standards for TIR hyperspectral imagers in the field. In this paper, we address the problems encountered when measuring emissivity spectra in the field and propose a practical data acquisition and processing framework for a Fourier transform (FT) TIR hyperspectral imager—the Hyper-Cam LW—to obtain high-quality emissivity spectra in the field. This framework consists of three main parts. (1) The performance of the Hyper-Cam LW sensor was evaluated in terms of the radiometric calibration and measurement noise, and a data acquisition procedure was carried out to obtain the useful TIR hyperspectral imagery in the field. (2) The data quality of the original TIR hyperspectral imagery was improved through preprocessing operations, including band selection, denoising, and background radiance correction. A spatial denoising method was also introduced to preserve the atmospheric radiance features in the spectra. (3) Three representative temperature-emissivity separation (TES) algorithms were evaluated and compared based on the Hyper-Cam LW TIR hyperspectral imagery, and the optimal TES algorithm was adopted to determine the final spectral emissivity. These algorithms are the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm, the improved Advanced Spaceborne Thermal Emission and Reflection Radiometer temperature and emissivity separation (ASTER-TES) algorithm, and the Fast Line-of-sight Atmospheric Analysis of Hypercubes-IR (FLAASH-IR) algorithm. The emissivity results from these different methods were compared to the reference spectra measured by a Model 102F spectrometer. The experimental results indicated that the retrieved emissivity spectra from the ISSTES algorithm were more accurate than the spectra retrieved by the other methods on the same Hyper-Cam LW field data and had close consistency with the reference spectra obtained from the Model 102F spectrometer. The root-mean-square error (RMSE) between the retrieved emissivity and the standard spectra was 0.0086, and the spectral angle error was 0.0093.

2021 ◽  
Vol 9 ◽  
Kathryn Elmer ◽  
Margaret Kalacska ◽  
J. Pablo Arroyo-Mora

Invasive species pose one of the greatest threats to global biodiversity. Early detection of invasive species is critical in order to prevent or manage their spread before they exceed the ability of land management groups to control them. Optical remote sensing has been established as a useful technology for the early detection and mapping of invasive vegetation populations. Through the use of airborne hyperspectral imagery (HSI), this study establishes a target detection methodology used to identify and map the invasive reed Phragmites australis subsp. australis within the entire extent of Îles-de-Boucherville National Park (Quebec, ON, Canada). We applied the Spectral Angle Mapper (SAM) target detection algorithm trained with a high accuracy GNSS ground truth data set to produce a park-wide map illustrating the extent of detected Phragmites. The total coverage of detected Phragmites was 26.74 ha (0.267 km2), which represents 3.28% of the total park area of 814 ha (8.14 km2). The inherent spatial uncertainty of the airborne HSI (∼2.25 m) was accounted for with uncertainty buffers, which, when included in the measurement of detected Phragmites, lead to a total area of 59.17 ha (0.591 km2), or 7.26% of the park. The overall accuracy of the Phragmites map was 84.28%, with a sensitivity of 76.32% and a specificity of 91.57%. Additionally, visual interpretation of the validation ground truth dataset was performed by 10 individuals, in order to compare their performance to that of the target detection algorithm. The overall accuracy of the visual interpretation was lower than the target detection (i.e., 69.18%, with a sensitivity of 59.21% and a specificity of 78.31%). Overall, this study is one of the first to utilize airborne HSI and target detection to map the extent of Phragmites over a moderately large extent. The uses and limitations of such an approach are established, and the methodology described here in detail could be adapted for future remote sensing studies of Phragmites or other vegetation species, native or invasive, at study sites around the world.

2021 ◽  
Vol 15 (04) ◽  
Yuquan Gan ◽  
Lei Li ◽  
Ying Liu ◽  
Chen Yi ◽  
Ji Zhang

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