scholarly journals Sensitivity of near‐infrared transmittance calculations for remote sensing applications to recent changes in spectroscopic information

2019 ◽  
Vol 20 (12) ◽  
Author(s):  
Kaah P. Menang
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Sensing Applications ◽  
Infrared Transmittance ◽  
Spectroscopic Information ◽  
Remote Sensing Applications

scholarly journals Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

2020 ◽  
pp. 35
Author(s):  
M. Campos-Taberner ◽  
F.J. García-Haro ◽  
B. Martínez ◽  
M.A. Gilabert
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Near Infrared ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Support Vector ◽  
Sensing Applications ◽  
Learning Techniques ◽  
Remote Sensing Applications ◽  
Sentinel 2

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>

scholarly journals Multispectral Sensor Calibration and Characterization for sUAS Remote Sensing

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4453 ◽  
Author(s):  
Mamaghani ◽  
Salvaggio
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Laboratory Method ◽  
Sensor Calibration ◽  
Radiometric Calibration ◽  
End User ◽  
Sensing Applications ◽  
A Value ◽  
Remote Sensing Applications

This paper focuses on the calibration of multispectral sensors typically used for remote sensing. These systems are often provided with "factory" radiometric calibration and vignette correction parameters. These parameters, which are assumed to be accurate when the sensor is new, may change as the camera is utilized in real-world conditions. As a result, regular calibration and characterization of any sensor should be conducted. An end-user laboratory method for computing both the vignette correction and radiometric calibration function is discussed in this paper. As an exemplar, this method for radiance computation is compared to the method provided by MicaSense for their RedEdge series of sensors. The proposed method and the method provided by MicaSense for radiance computation are applied to a variety of images captured in the laboratory using a traceable source. In addition, a complete error propagation is conducted to quantify the error produced when images are converted from digital counts to radiance. The proposed methodology was shown to produce lower errors in radiance imagery. The average percent error in radiance was −10.98%, −0.43%, 3.59%, 32.81% and −17.08% using the MicaSense provided method and their "factory" parameters, while the proposed method produced errors of 3.44%, 2.93%, 2.93%, 3.70% and 0.72% for the blue, green, red, near infrared and red edge bands, respectively. To further quantify the error in terms commonly used in remote sensing applications, the error in radiance was propagated to a reflectance error and additionally used to compute errors in two widely used parameters for assessing vegetation health, NDVI and NDRE. For the NDVI example, the ground reference was computed to be 0.899 ± 0.006, while the provided MicaSense method produced a value of 0.876 ± 0.005 and the proposed method produced a value of 0.897 ± 0.007. For NDRE, the ground reference was 0.455 ± 0.028, MicaSense method produced 0.239 ± 0.026 and the proposed method produced 0.435 ± 0.038.

THE APPLICATION OF REMOTE SENSING IN PRECISION AGRICULTURE

1970 ◽  
pp. 10-16
Author(s):  
S. Yu. Blokhina
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Spatial Resolution ◽  
Precision Agriculture ◽  
Near Infrared ◽  
Crop Yields ◽  
Temporal Frequency ◽  
Organic Matter Content ◽  
Sensing Applications ◽  
Remote Sensing Applications

The paper provides an overview of foreign literature on the remote sensing applications in precision agriculture. Remote sensing applications in precision agriculture began with sensors for soil organic matter content, and have quickly advanced to include hand held sensors to tractor or aerial or satellite mounted sensors. Wavelengths of electromagnetic radiation initially focused on a few key visible or near infrared bands, and nowadays electromagnetic wavelengths in use range from the ultraviolet to microwave portions of the spectrum. Spectral bandwidth has decreased dramatically with the advent of hyperspectral remote sensing, allowing improved analysis of crop stress, crop biophysical or biochemical characteristics and specific compounds. A variety of spectral indices have been widely implemented within various precision agriculture applications, rather than a focus on only normalized difference vegetation indices. Spatial resolution and temporal frequency of remote sensing imagery has increased significantly, allowing evaluation of soil and crop properties at fine spatial resolution at the expense of increased data storage and processing requirements. At present there is considerable interest in collecting remote sensing for operational management of soil and crop yields, as well as control over the spread of pests and weeds practically in real time.

X-ray diffraction and infrared characterization of Oxisols from central and southeastern Brazil

Clay Minerals ◽  
2008 ◽  
Vol 43 (4) ◽  
pp. 549-560 ◽  
Author(s):  
R. P. Nitzsche ◽  
J. B. Percival ◽  
J. K. Torrance ◽  
J. A. R. Stirling ◽  
J. T. Bowen
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Microwave Remote Sensing ◽  
Short Wave ◽  
Southeastern Brazil ◽  
X Ray Diffraction ◽  
X Ray ◽  
Sensing Applications ◽  
Remote Sensing Applications

AbstractEleven Oxisols with high clay contents, 2.6–59.7 wt.% Fe2O3, and containing hematite, goethite, magnetite and maghemite, from São Paulo, Minas Gerais and Goiás, Brazil, were studied for the purpose of microwave remote sensing applications in the 0.3 to 300 GHz range. Of special interest are: the pseudosand effect caused by Fe-oxide cementation of clusters of soil particles; the mineralogy; and whether the soil magnetic susceptibility affected by ferromagnetic magnetite and maghemite interferes with microwave propagation. Quantitative mineralogical analyses were conducted using X-ray diffraction with Rietveld refinement. Visible, near infrared and short wave infrared spectroscopic analyses were used to characterize the samples qualitatively for comparison with published spectral radiometry results. Quartz (3–88%), hematite (2–36%) and gibbsite (1–40%) occurred in all soils, whereas kaolinite (2–70%) and anatase (2–13%) occurred in nine samples. Ilmenite (1–8%) was found in eight soils and goethite (2–39%) in seven. Of the ferromagnetic minerals, maghemite occurred in seven soils (1–13%) and three contained magnetite (<2%). These results will be applied to the interpretation of the effect of Fe oxides, particularly the ferromagnetic oxides, on microwave interaction with high-Fe soils, with ultimate application to the monitoring of soil water content by microwave remote sensing.

scholarly journals High-resolution, non-scanning FTS for remote sensing

1995 ◽  
Vol 149 ◽  
pp. 338-339
Author(s):  
K. Døhlen ◽  
A. Cañas
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
High Resolution ◽  
Near Infrared ◽  
Michelson Interferometer ◽  
Resolving Power ◽  
Diode Array ◽  
Sensing Applications ◽  
First Results ◽  
Remote Sensing Applications

We present the first results from a portable spectrometer for the visible and very near infrared based upon the principle of heterodyned holographic Fourier transform spectroscopy (HHS) (Dohi and Suzuki 1971, Dohlen 1994). The instrument uses a Michelson interferometer where one of the mirrors is replaced with a grating. This produces a spatially located, frequency-shifted interferogram which is read out by an all-reflective relay lens and a photo-diode array and processed on a portable PC. A battery pack ensures an autonomy of about 7 hours. Instrumental assets include high optical throughput, variable resolving power, and no moving parts.We have successfully used the instrument in two different remote sensing applications: detection of vegetation reflectance and atmospheric absorption.

Miniaturized visible near-infrared hyperspectral imager for remote-sensing applications

2012 ◽  
Vol 51 (11) ◽  
pp. 111720-1 ◽  
Author(s):  
Christopher P. Warren ◽  
Detlev Even ◽  
William Pfister ◽  
Keith Nakanishi ◽  
Arleen Velasco ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Sensing Applications ◽  
Remote Sensing Applications
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