scholarly journals An Operational Radiometric Correction Technique for Shadow Reduction in Multispectral UAV Imagery

2021 ◽  
Vol 13 (19) ◽  
pp. 3808
Author(s):  
Xavier Pons ◽  
Joan-Cristian Padró
Keyword(s):  
Spectral Reflectance ◽  
Spectral Characteristics ◽  
Empirical Method ◽  
Surface Model ◽  
Diffuse Irradiance ◽  
Remote Sensor ◽  
Correction Technique ◽  
Land Cover Maps ◽  
The Relationship

This study focuses on the recovery of information from shadowed pixels in RGB or multispectral imagery sensed from unmanned aerial vehicles (UAVs). The proposed technique is based on the concept that a property characterizing a given surface is its spectral reflectance, i.e., the ratio between the flux reflected by the surface and the radiant flux received by the surface, and this ratio is usually similar under direct-plus-diffuse irradiance and under diffuse irradiance when a Lambertian behavior can be assumed. Scene-dependent elements, such as trees, shrubs, man-made constructions, or terrain relief, can block part of the direct irradiance (usually sunbeams), in which part of the surface only receives diffuse irradiance. As a consequence, shadowed surfaces comprising pixels of the image created by the UAV remote sensor appear. Regardless of whether the imagery is analyzed by means of photointerpretation or digital classification methods, when the objective is to create land cover maps, it is hard to treat these areas in a coherent way in terms of the areas receiving direct and diffuse irradiance. The hypothesis of the present work is that the relationship between irradiance conditions in shadowed areas and non-shadowed areas can be determined by following classical empirical line techniques for fulfilling the objective of a coherent treatment in both kinds of areas. The novelty of the presented method relies on the simultaneous recovery of information in non-shadowed and shadowed areas by the in situ spectral reflectance measurements of characterized Lambertian targets followed by smoothing of the penumbra area. Once in the lab, firstly, we accurately detected the shadowed pixels by combining two well-known techniques for the detection of the shadowed areas: (1) using a physical approach based on the sun’s position and the digital surface model of the area covered by the imagery; and (2) the image-based approach using the histogram properties of the intensity image. In this paper, we present the benefits of the combined usage of both techniques. Secondly, we applied a fit between non-shadowed and shadowed areas by using a twin set of spectrally characterized target sets. One set was placed under direct and diffuse irradiance (non-shadowed targets), whereas the second set (with the same spectral characteristics) was placed under diffuse irradiance (shadowed targets). Assuming that the reflectance of the homologous targets of each set was the same, we approximated the diffuse incoming irradiance through an empirical line correction. The model was applied to all detected shadowed areas in the whole scene. Finally, a smoothing filter was applied to the penumbra transitions. The presented empirical method allowed the operational and coherent recovery of information from shadowed areas, which is very common in high-resolution UAV imagery.

scholarly journals Comparing the ensemble and extended Kalman filters for in situ soil moisture assimilation with contrasting conditions

2015 ◽  
Vol 19 (12) ◽  
pp. 4811-4830 ◽  
Author(s):  
D. Fairbairn ◽  
A. L. Barbu ◽  
J.-F. Mahfouf ◽  
J.-C. Calvet ◽  
E. Gelati
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Surface Model ◽  
Random Component ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Correction Technique ◽  
Better Than

Abstract. Two data assimilation (DA) methods are compared for their ability to produce an accurate soil moisture analysis using the Météo-France land surface model: (i) SEKF, a simplified extended Kalman filter, which uses a climatological background-error covariance, and (ii) EnSRF, the ensemble square root filter, which uses an ensemble background-error covariance and approximates random rainfall errors stochastically. In situ soil moisture observations at 5 cm depth are assimilated into the surface layer and 30 cm deep observations are used to evaluate the root-zone analysis on 12 sites in south-western France (SMOSMANIA network). These sites differ in terms of climate and soil texture. The two methods perform similarly and improve on the open loop. Both methods suffer from incorrect linear assumptions which are particularly degrading to the analysis during water-stressed conditions: the EnSRF by a dry bias and the SEKF by an over-sensitivity of the model Jacobian between the surface and the root-zone layers. These problems are less severe for the sites with wetter climates. A simple bias correction technique is tested on the EnSRF. Although this reduces the bias, it modifies the soil moisture fluxes and suppresses the ensemble spread, which degrades the analysis performance. However, the EnSRF flow-dependent background-error covariance evidently captures seasonal variability in the soil moisture errors and should exploit planned improvements in the model physics. Synthetic twin experiments demonstrate that when there is only a random component in the precipitation forcing errors, the correct stochastic representation of these errors enables the EnSRF to perform better than the SEKF. It might therefore be possible for the EnSRF to perform better than the SEKF with real data, if the rainfall uncertainty was accurately captured. However, the simple rainfall error model is not advantageous in our real experiments. More realistic rainfall error models are suggested.

scholarly journals Comparing the Ensemble and Extended Kalman Filters for in situ soil moisture assimilation with contrasting soil conditions

2015 ◽  
Vol 12 (8) ◽  
pp. 7353-7403
Author(s):  
D. Fairbairn ◽  
A. L. Barbu ◽  
J.-F. Mahfouf ◽  
J.-C. Calvet ◽  
E. Gelati
Keyword(s):  
Soil Moisture ◽  
Sandy Soils ◽  
Soil Conditions ◽  
Surface Model ◽  
Random Component ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Correction Technique

Abstract. Two data assimilation methods are compared for their ability to produce a deterministic soil moisture analysis on the Météo-France land surface model: (i) SEKF, a Simplified Extended Kalman Filter, which uses a climatological background-error covariance, (ii) EnSRF, the Ensemble Square Root Filter, which uses an ensemble background-error covariance and approximates random forcing errors stochastically. The accuracy of the deterministic analysis is measured on 12 sites with in situ observations and various soil textures in Southwest France (SMOSMANIA network). In the experiments with real observations, the two methods perform similarly and improve on the open loop. Both methods suffer from incorrect linear assumptions which are particularly degrading to the analysis during water-stressed conditions: the EnSRF by a dry bias and the SEKF by an over-sensitivity of the model Jacobian between the surface and the root zone layers. These problems are less severe for sandy soils than clay soils because sandy soils are less sensitive to perturbations in the initial conditions. A simple bias correction technique is tested on the EnSRF. Although this reduces the bias, it also suppresses the ensemble spread, which degrades the analysis performance. However, the EnSRF flow-dependent background-error covariance evidently captures seasonal variability in the soil moisture errors and should exploit planned improvements in the model physics. Synthetic experiments demonstrate that when there is only a random component in the precipitation forcing errors, the correct stochastic representation of these errors enables the EnSRF to perform better than the SEKF. But in the real experiments the same rainfall error specification does not improve the EnSRF analysis. It is likely that the actual rainfall errors are underestimated and that other sources of errors could limit the usefulness of this information. More comprehensive ways of representing the rainfall errors are suggested, which might improve the EnSRF performance.

Profile Imaging of Silicon Surface Reconstructions

1985 ◽  
Vol 43 ◽  
pp. 262-263
Author(s):  
O.L. Krivanek ◽  
G.J. Wood
Keyword(s):  
Electron Microscopy ◽  
Surface Structure ◽  
Structure Determination ◽  
Silicon Surface ◽  
Good Resolution ◽  
Surface Reconstructions ◽  
Bulk Structure ◽  
Point To Point ◽  
The Relationship

Electron microscopy at 0.2nm point-to-point resolution, 10-10 torr specimei region vacuum and facilities for in-situ specimen cleaning presents intere; ing possibilities for surface structure determination. Three methods for examining the surfaces are available: reflection (REM), transmission (TEM) and profile imaging. Profile imaging is particularly useful because it giv good resolution perpendicular as well as parallel to the surface, and can therefore be used to determine the relationship between the surface and the bulk structure.

A combined ultrastructural and gene molecular research for the relationship between human uterine cervical carcinoma and human papillomavirus

1990 ◽  
Vol 48 (3) ◽  
pp. 204-205
Author(s):  
Kun Lee ◽  
Jingyi Si ◽  
Ricai Han ◽  
Wei Zhang ◽  
Bingbing Tan ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Human Papillomavirus ◽  
Intraepithelial Neoplasia ◽  
Hpv Infection ◽  
Dot Blot ◽  
Homologous Sequences ◽  
Normal Cervical Epithelium ◽  
The Relationship ◽  
Chronic Cervicitis

There are more supports for the view that human papillomavirus (HPV) infection might be an etiological factor in the development of cervical cancer when the association of persistent condylomata is considered. Biopsies from 318 cases with squamous cell carcinoma of uterine cervix, 48 with cervical and vulvar condylomata, 14 with cervical intraepithelial neoplasia (CIN), 34 with chronic cervicitis and 24 normal cervical epithelium were collected from 5 geographic regions of China with different cervical cancer mortalities. All specimens were prepared for Dot blot, Southern blot and in situ DNA-DNA hybridizations by using HPV-11, 16, 18 DNA labelled with 32P and 3H as probes to detect viral homologous sequences in samples. Among them, 32 cases with cervical cancer, 27 with condyloma and 10 normal cervical epitheliums were randomly chosen for comparative EM observation. The results showed that: 1), 192 out of 318 (60.4%) cases of cervical cancer were positive for HPV-16 DNA probe (Table I)

scholarly journals Novel hydrogen chemisorption properties of amorphous ceramic compounds consisting of p-block elements: exploring Lewis acid-base Al–N pair site formed in-situ within polymer-derived silicon-aluminum-nitrogen-based system

2021 ◽  
Author(s):  
Shotaro Tada ◽  
Norifumi Asakuma ◽  
Shiori Ando ◽  
Toru Asaka ◽  
Yusuke Daiko ◽  
...  
Keyword(s):  
Lewis Acid ◽  
Active Site ◽  
Hydrogen Chemisorption ◽  
Acid Base ◽  
Polymer Derived Ceramics ◽  
System P ◽  
Ceramic Compounds ◽  
The Relationship

This paper reports on the relationship between the H2 chemisorption properties and reversible structural reorientation of the possible active site around Al formed in-situ within polymer-derived ceramics (PDCs) based on...

scholarly journals Comparison of the Concentration of Risk Elements in Alluvial Soils Determined by pXRF In Situ, in the Laboratory, and by ICP-OES

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 938
Author(s):  
Ladislav Menšík ◽  
Lukáš Hlisnikovský ◽  
Pavel Nerušil ◽  
Eva Kunzová
Keyword(s):  
Precision Agriculture ◽  
Alluvial Soil ◽  
Coefficient Of Determination ◽  
Fast Method ◽  
Agricultural Practice ◽  
Regression Equations ◽  
Icp Oes ◽  
Risk Elements ◽  
The Relationship

The aim of the study was to compare the concentrations of risk elements (As, Cu, Mn, Ni, Pb, Zn) in alluvial soil, which were measured by a portable X-ray fluorescence analyser (pXRF) in situ (FIELD) and in the laboratory (LABORATORY). Subsequently, regression equations were developed for individual elements through the method of construction of the regression model, which compare the results of pXRF with classical laboratory analysis (ICP-OES). The accuracy of the measurement, expressed by the coefficient of determination (R2), was as follows in the case of FIELD–ICP-OES: Pb (0.96), Zn (0.92), As (0.72), Mn (0.63), Cu (0.31) and Ni (0.01). In the case of LABORATORY–ICP-OES, the coefficients had values: Pb (0.99), Zn (0.98), Cu and Mn (0.89), As (0.88), Ni (0.81). A higher dependence of the relationship was recorded between LABORATORY–ICP-OES than between FIELD–ICP-OES. An excellent relationship was recorded for the elements Pb and Zn, both for FIELD and LABORATORY (R2 higher than 0.90). The elements Cu, Mn and As have a worse tightness in the relationship; however, the results of the model have shown its applicability for common use, e.g., in agricultural practice or in monitoring the quality of the environment. Based on our results, we can say that pXRF instruments can provide highly accurate results for the concentration of risk elements in the soil in real time for some elements and meet the principle of precision agriculture: an efficient, accurate and fast method of analysis.

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