SHADOW DETECTION IN HYPERSPECTRAL IMAGES ACQUIRED BY UAV

<p><strong>Abstract.</strong> Shadows are common in any kind of remote sensing images. Unmanned Aerial Vehicle &ndash; UAV with a light camera attached can acquire images illuminated either by direct sunlight or by diffuse light under clouds. Indeed, areas with pixels shaded by clouds must be detected and labelled in order to use this additional information for image analysis. Classification of health and diseased plants in permanent culture as the orange plantation field can present some errors due to tree cast shadow. So, hyperspectral or multispectral image classification can be improved by previous shadow detection. Some FPI hyperspectral camera, designed for agricultural applications is limited in the spectral range between 500 to 900&thinsp;nm. Wavelengths in the region of blue light and in the SWIR spectral region have physical properties that enable the enhancement of shaded regions in the images. In this work some combinations of different spectral bands were evaluated in order to specify those suitable to detect shadows in agricultural field images. In this sense, considering that vegetation and soil are the two main kind of coverage in an agricultural field, we hypothesized that wavelengths near blue light and the longest near infrared available in the camera range are good choices. In both spectral regions soil and vegetation targets have small spectral differences which contribute to enhance the differences between shaded and illuminated regions in the image. Hyperspectral images acquired with a FPI hyperspectral camera onboard a UAV over a plantation of oranges were used to evaluate these spectral bands. The results showed that the wavelengths of aproximatelly 510&thinsp;nm and 840&thinsp;nm available in the FPI camera are the best to detect any type of shadows in the agricultural fields.</p>

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ORIENTATION AND CALIBRATION REQUIREMENTS FOR HYPERPECTRAL IMAGING USING UAVs: A CASE STUDY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xl-3-w4-109-2016 ◽

2016 ◽

Vol XL-3/W4 ◽

pp. 109-115

Author(s):

A. M. G. Tommaselli ◽

A. Berveglieri ◽

R. A. Oliveira ◽

L. Y. Nagai ◽

E. Honkavaara

Keyword(s):

Remote Sensing ◽

Data Sets ◽

Spectral Bands ◽

Operational Problem ◽

Fabry Perot ◽

Agricultural Applications ◽

Close Range ◽

The Impact ◽

Hyperspectral Camera

Flexible tools for photogrammetry and remote sensing using unmanned airborne vehicles (UAVs) have been attractive topics of research and development. The lightweight hyperspectral camera based on a Fabry-Pérot interferometer (FPI) is one of the highly interesting tools for UAV based remote sensing for environmental and agricultural applications. The camera used in this study acquires images from different wavelengths by changing the FPI gap and using two CMOS sensors. Due to the acquisition principle of this camera, the interior orientation parameters (IOP) of the spectral bands can vary for each band and sensor and changing the configuration also would change these sets of parameters posing an operational problem when several bands configurations are being used. The objective of this study is to assess the impact of use IOPs estimated for some bands in one configuration for other bands of different configuration the FPI camera, considering different IOP and EOP constraints. The experiments were performed with two FPI-hyperspectral camera data sets: the first were collected 3D terrestrial close-range calibration field and the second onboard of an UAV in a parking area in the interior of São Paulo State.

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ORIENTATION AND CALIBRATION REQUIREMENTS FOR HYPERPECTRAL IMAGING USING UAVs: A CASE STUDY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-3-w4-109-2016 ◽

2016 ◽

Vol XL-3/W4 ◽

pp. 109-115 ◽

Cited By ~ 3

Author(s):

A. M. G. Tommaselli ◽

A. Berveglieri ◽

R. A. Oliveira ◽

L. Y. Nagai ◽

E. Honkavaara

Keyword(s):

Remote Sensing ◽

Data Sets ◽

Spectral Bands ◽

Operational Problem ◽

Fabry Perot ◽

Agricultural Applications ◽

Close Range ◽

The Impact ◽

Hyperspectral Camera

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Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada

Remote Sensing ◽

10.3390/rs11111298 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1298 ◽

Cited By ~ 3

Author(s):

Ahmed Laamrani ◽

Aaron A. Berg ◽

Paul Voroney ◽

Hannes Feilhauer ◽

Line Blackburn ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Near Infrared ◽

Hyperspectral Data ◽

Support Vector ◽

Least Squares Regression ◽

Agricultural Field ◽

Southern Ontario ◽

Spectral Bands ◽

Selection Of

The recent use of hyperspectral remote sensing imagery has introduced new opportunities for soil organic carbon (SOC) assessment and monitoring. These data enable monitoring of a wide variety of soil properties but pose important methodological challenges. Highly correlated hyperspectral spectral bands can affect the prediction and accuracy as well as the interpretability of the retrieval model. Therefore, the spectral dimension needs to be reduced through a selection of specific spectral bands or regions that are most helpful to describing SOC. This study evaluates the efficiency of visible near-infrared (VNIR) and shortwave near-infrared (SWIR) hyperspectral data to identify the most informative hyperspectral bands responding to SOC content in agricultural soils. Soil samples (111) were collected over an agricultural field in southern Ontario, Canada and analyzed against two hyperspectral datasets: An airborne Nano-Hyperspec imaging sensor with 270 bands (400–1000 nm) and a laboratory hyperspectral dataset (ASD FieldSpec 3) along the 1000–2500 nm range (NIR-SWIR). In parallel, a multimethod modeling approach consisting of random forest, support vector machine, and partial least squares regression models was used to conduct band selections and to assess the validity of the selected bands. The multimethod model resulted in a selection of optimal band or regions over the VNIR and SWIR sensitive to SOC and potentially for mapping. The bands that achieved the highest respective importance values were 711–715, 727, 986–998, and 433–435 nm regions (VNIR); and 2365–2373, 2481–2500, and 2198–2206 nm (NIR-SWIR). Some of these bands are in agreement with the absorption features of SOC reported in the literature, whereas others have not been reported before. Ultimately, the selection of optimal band and regions is of importance for quantification of agricultural SOC and would provide a new framework for creating optimized SOC-specific sensors.

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Investigation of Sediment-Rich Glacial Meltwater Plumes Using a High-Resolution Multispectral Sensor Mounted on an Unmanned Aerial Vehicle

Water ◽

10.3390/w11112405 ◽

2019 ◽

Vol 11 (11) ◽

pp. 2405 ◽

Cited By ~ 1

Author(s):

Wójcik ◽

Bialik ◽

Osińska ◽

Figielski

Keyword(s):

Near Infrared ◽

Spectral Band ◽

Correlation Coefficients ◽

Weather Conditions ◽

Spectral Bands ◽

Aerial Vehicle ◽

High Turbidity ◽

Unfavourable Weather ◽

Harsh Climate ◽

Surface Ice

A Parrot Sequoia+ multispectral camera on a Parrot Bluegrass drone registered in four spectral bands (green, red, red edge (RE), and near-infrared (NIR)) to identify glacial outflow zones and determined the meltwater turbidity values in waters in front of the following Antarctic glaciers: Ecology, Dera Icefall, Zalewski, and Krak on King George Island, Southern Shetlands was used. This process was supported by a Red-Green-Blue (RGB) colour model from a Zenmuse X5 camera on an Inspire 2 quadcopter drone. Additional surface water turbidity measurements were carried out using a Yellow Springs Instruments (YSI) sonde EXO2. From this research, it was apparent that for mapping low-turbidity and medium-turbidity waters (<70 formazinenephelometricunits (FNU)), a red spectral band should be used, since it is insensitive to possible surface ice phenomena and registers the presence of both red and white sediments. High-turbidity plumes with elevated FNU values should be identified through the NIR band. Strong correlation coefficients between the reflectance at particular bands and FNU readings (RGreen = 0.85, RRed = 0.85, REdge = 0.84, and RNIR = 0.83) are shown that multispectral mapping using Unmanned Aerial Vehicles (UAVs) can be successfully usedeven in the unfavourable weather conditions and harsh climate of Antarctica. Lastly, the movement of water masses in Admiralty Bay is briefly discussed and supported by the results from EXO2 measurements.

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Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

Remote Sensing ◽

10.3390/rs13030536 ◽

2021 ◽

Vol 13 (3) ◽

pp. 536

Author(s):

Eve Laroche-Pinel ◽

Mohanad Albughdadi ◽

Sylvie Duthoit ◽

Véronique Chéret ◽

Jacques Rousseau ◽

...

Keyword(s):

Near Infrared ◽

Water Status ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Short Wave ◽

Spatial And Temporal Scales ◽

Spectral Bands ◽

Main Challenge ◽

Spectral Scale ◽

Grape Varieties

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

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Forensic analysis of beverage stains using hyperspectral imaging

Scientific Reports ◽

10.1038/s41598-021-85737-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Binu Melit Devassy ◽

Sony George

Keyword(s):

Hyperspectral Imaging ◽

Crime Scene ◽

Forensic Analysis ◽

Classification Model ◽

Support Vector ◽

Additional Information ◽

Non Invasive ◽

Spectral Bands ◽

Data Dimensionality Reduction ◽

Gradient Based

AbstractDocumentation and analysis of crime scene evidences are of great importance in any forensic investigation. In this paper, we present the potential of hyperspectral imaging (HSI) to detect and analyze the beverage stains on a paper towel. To detect the presence and predict the age of the commonly used drinks in a crime scene, we leveraged the additional information present in the HSI data. We used 12 different beverages and four types of paper hand towel to create the sample stains in the current study. A support vector machine (SVM) is used to achieve the classification, and a convolutional auto-encoder is used to achieve HSI data dimensionality reduction, which helps in easy perception, process, and visualization of the data. The SVM classification model was re-established for a lighter and quicker classification model on the basis of the reduced dimension. We employed volume-gradient-based band selection for the identification of relevant spectral bands in the HSI data. Spectral data recorded at different time intervals up to 72 h is analyzed to trace the spectral changes. The results show the efficacy of the HSI techniques for rapid, non-contact, and non-invasive analysis of beverage stains.

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Rapid Quantification of Microalgae Growth with Hyperspectral Camera and Vegetation Indices

Plants ◽

10.3390/plants10020341 ◽

2021 ◽

Vol 10 (2) ◽

pp. 341

Author(s):

Pauliina Salmi ◽

Matti A. Eskelinen ◽

Matti T. Leppänen ◽

Ilkka Pölönen

Keyword(s):

Chlorophyll Fluorescence ◽

Chlorophyll A ◽

Near Infrared ◽

Vegetation Indices ◽

Hyperspectral Data ◽

Strong Correlations ◽

Algae Biomass ◽

Chlorophyll A Concentration ◽

Cost Efficient ◽

Hyperspectral Camera

Spectral cameras are traditionally used in remote sensing of microalgae, but increasingly also in laboratory-scale applications, to study and monitor algae biomass in cultures. Practical and cost-efficient protocols for collecting and analyzing hyperspectral data are currently needed. The purpose of this study was to test a commercial, easy-to-use hyperspectral camera to monitor the growth of different algae strains in liquid samples. Indices calculated from wavebands from transmission imaging were compared against algae abundance and wet biomass obtained from an electronic cell counter, chlorophyll a concentration, and chlorophyll fluorescence. A ratio of selected wavebands containing near-infrared and red turned out to be a powerful index because it was simple to calculate and interpret, yet it yielded strong correlations to abundances strain-specifically (0.85 < r < 0.96, p < 0.001). When all the indices formulated as A/B, A/(A + B) or (A − B)/(A + B), where A and B were wavebands of the spectral camera, were scrutinized, good correlations were found amongst them for biomass of each strain (0.66 < r < 0.98, p < 0.001). Comparison of near-infrared/red index to chlorophyll a concentration demonstrated that small-celled strains had higher chlorophyll absorbance compared to strains with larger cells. The comparison of spectral imaging to chlorophyll fluorescence was done for one strain of green algae and yielded strong correlations (near-infrared/red, r = 0.97, p < 0.001). Consequently, we described a simple imaging setup and information extraction based on vegetation indices that could be used to monitor algae cultures.

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