FPGA Implementation of the HySime Algorithm for the Determination of the Number of Endmembers in Hyperspectral Data

Algorithms have been developed for controlling the calibration and the measurement cycles of hyperspectral equipment. Special calibration and preprocessing methods were necessary to obtain suitable signal level and acceptable repeatability of the measurements. Therefore, the effect of the noise of NIR sensor was decreased, the signal level was enhanced and stability was ensured simultaneously. In order to investigate the properties of the number of objects suitable for statistical analysis, the enormous size of acquired hypercube (gigabytes per object) should be reduced by vector-to-scalar mathematical operators in real-time to extract the desired features. The algorithm developed was able to calculate the score of operators during scanning and the matrices were displayed as pseudo-images to show the distribution of the properties on the surface. The operators had to be determined by analysis of a sample set in preliminary experiments. Stored carrot was chosen as a model sample for investigation of the detection of moisture loss by hyperspectral properties. Determination of the proper operator on different tissues could help to analyze and model drying process and to control storage. Hyperspectral data of different carrot cultivars were tested under different storage conditions. Using improved measurement method the spectral parameter of the suitable operator described quite well the moisture loss of the different carrot tissues.

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Atmospheric correction of hyperspectral data in terms of the determination of plant parameters

10.1117/12.197245 ◽

1994 ◽

Cited By ~ 8

Author(s):

Heike Bach ◽

Wolfram Mauser

Keyword(s):

Atmospheric Correction ◽

Hyperspectral Data

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Determination of the separability of coastal community assemblages of the Al Wajh Barrier Reef, Red Sea, from hyperspectral data

Open Geosciences ◽

10.2478/v10085-009-0007-6 ◽

2009 ◽

Vol 1 (1) ◽

Cited By ~ 1

Author(s):

Sarah Hamylton

Keyword(s):

Remote Sensing ◽

Red Sea ◽

Function Analysis ◽

Remote Sensing Data ◽

Hyperspectral Data ◽

Barrier Reef ◽

Optimal Subset ◽

Coastal Landscapes ◽

Mapping Process

AbstractRemote sensing provides a practical means by which coral reefs and their associated communities are commonly mapped. The availability of spectral information is a key determinant of the detail discernable in the mapping process and consequent detail presented in output maps. Testament to this is the increasing utility of hyperspectral sensors, which typically yield datasets of higher resolution, spectrally continuous wavebands. Image classification algorithms distinguish between the different and unique reflectance characteristics of target features. While the availability of more wavebands provides the opportunity to apply analysis techniques that treat the data as spectrally continuous, such a large number of data dimensions also present a considerable computing burden. Through multiple discriminant function analysis, this paper identifies an optimal subset of wavelengths for resolving the reflectance of key terrestrial and marine coverages at the Al Wajh Barrier reef system, Saudi Arabia, Red Sea. The goal of such analysis is to facilitate the processing of high resolution, spectrally continuous remote sensing data of coastal landscapes.

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Determination of target detection limits in hyperspectral data using band selection and dimensionality reduction

10.1117/12.2240931 ◽

2016 ◽

Cited By ~ 1

Author(s):

W. Gross ◽

J. Boehler ◽

K. Twizer ◽

B. Kedem ◽

A. Lenz ◽

...

Keyword(s):

Dimensionality Reduction ◽

Target Detection ◽

Detection Limits ◽

Hyperspectral Data ◽

Band Selection

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Incorporating Multi-Scale, Spectrally Detected Nitrogen Concentrations into Assessing Nitrogen Use Efficiency for Winter Wheat Breeding Populations

Remote Sensing ◽

10.3390/rs13193991 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3991

Author(s):

Raquel Peron-Danaher ◽

Blake Russell ◽

Lorenzo Cotrozzi ◽

Mohsen Mohammadi ◽

John Couture

Keyword(s):

Winter Wheat ◽

Leaf Nitrogen ◽

Hyperspectral Data ◽

Plant Nitrogen ◽

Nitrogen Levels ◽

Nitrogen Concentrations ◽

Use Efficiency ◽

Canopy Nitrogen ◽

Wheat Lines

Annually, over 100 million tons of nitrogen fertilizer are applied in wheat fields to ensure maximum productivity. This amount is often more than needed for optimal yield and can potentially have negative economic and environmental consequences. Monitoring crop nitrogen levels can inform managers of input requirements and potentially avoid excessive fertilization. Standard methods assessing plant nitrogen content, however, are time-consuming, destructive, and expensive. Therefore, the development of approaches estimating leaf nitrogen content in vivo and in situ could benefit fertilization management programs as well as breeding programs for nitrogen use efficiency (NUE). This study examined the ability of hyperspectral data to estimate leaf nitrogen concentrations and nitrogen uptake efficiency (NUpE) at the leaf and canopy levels in multiple winter wheat lines across two seasons. We collected spectral profiles of wheat foliage and canopies using full-range (350–2500 nm) spectroradiometers in combination with leaf tissue collection for standard analytical determination of nitrogen. We then applied partial least-squares regression, using spectral and reference nitrogen measurements, to build predictive models of leaf and canopy nitrogen concentrations. External validation of data from a multi-year model demonstrated effective nitrogen estimation at leaf and canopy level (R2 = 0.72, 0.67; root-mean-square error (RMSE) = 0.42, 0.46; normalized RMSE = 12, 13; bias = −0.06, 0.04, respectively). While NUpE was not directly well predicted using spectral data, NUpE values calculated from predicted leaf and canopy nitrogen levels were well correlated with NUpE determined using traditional methods, suggesting the potential of the approach in possibly replacing standard determination of plant nitrogen in assessing NUE. The results of our research reinforce the ability of hyperspectral data for the retrieval of nitrogen status and expand the utility of hyperspectral data in winter wheat lines to the application of nitrogen management practices and breeding programs.

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Prediction of Prime Wavelengths for the Estimation of Age Based Water Requirement of Arecanut Cropby Hyperspectral Data and VIP

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i7823.078919 ◽

2019 ◽

Vol 8 (9) ◽

pp. 1778-1783

Keyword(s):

Water Requirement ◽

Hyperspectral Data ◽

Crop Water Requirement ◽

Coefficient Of Determination ◽

Crop Water ◽

Spectral Signatures ◽

The Government ◽

Plsr Model

In the world India is the highest producer and consumer of Arecanut. Also it is widely grown plantation crop in the coastal regions as well other parts of Karnataka. It has a great commercial value both in terms of export potential and revenue generation to the government. The crop sustains for a longer decades and demands huge amount of irrigation water throughout its life span. Assessment of exact amount of crop water requirement in different stages of the plant growth reduces the excessive irrigation. It increases crop yield thereby conserves both ground and surface water. The study targets to identify important wavelengths to predict age based crop water requirement. For this a small portion of the Channagiritaluk is considered for the study. The methodology adopted in this study uses the Hyperspectral data for age based classification of Arecanut crop to map its corresponding water requirement using NDVI based KC method. From the map pixel wise age based crop water requirement values were extractedand regressed with the corresponding spectral signatures from pre-processed satellite imagery. The PLSR model yielded a coefficient of determination of 0.98. The output of PLSR model results were used in VIP. Total of eight wavelengths, spanning across VNIR and SWIR regions were identified as significant in modeling the ACWR these were 1043, 1053, 1033, 1083, 1023, 1013, 1104, and 854nm. The identified wavelengths are useful to develop a model to estimate the water demand of the study area. The study helps for optimized planning of the water resources.

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