scholarly journals Reflectance Estimation from Multispectral Linescan Acquisitions under Varying Illumination—Application to Outdoor Weed Identification

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3601
Author(s):  
Anis Amziane ◽  
Olivier Losson ◽  
Benjamin Mathon ◽  
Aurélien Dumenil ◽  
Ludovic Macaire
Keyword(s):  
Crop Yields ◽  
Estimation Method ◽  
Imaging Systems ◽  
High Spectral Resolution ◽  
Color Information ◽  
Illumination Variation ◽  
Weed Detection ◽  
Spectral Bands ◽  
Detection And Identification ◽  
Precision Spraying

To reduce the amount of herbicides used to eradicate weeds and ensure crop yields, precision spraying can effectively detect and locate weeds in the field thanks to imaging systems. Because weeds are visually similar to crops, color information is not sufficient for effectively detecting them. Multispectral cameras provide radiance images with a high spectral resolution, thus the ability to investigate vegetated surfaces in several narrow spectral bands. Spectral reflectance has to be estimated in order to make weed detection robust against illumination variation. However, this is a challenge when the image is assembled from successive frames that are acquired under varying illumination conditions. In this study, we present an original image formation model that considers illumination variation during radiance image acquisition with a linescan camera. From this model, we deduce a new reflectance estimation method that takes illumination at the frame level into account. We experimentally show that our method is more robust against illumination variation than state-of-the-art methods. We also show that the reflectance features based on our method are more discriminant for outdoor weed detection and identification.

The Deuterium Balmer Series

2005 ◽  
Vol 13 ◽  
pp. 811-812
Author(s):  
Guillaume Hébrard
Keyword(s):  
Spectral Resolution ◽  
Magellanic Clouds ◽  
High Spectral Resolution ◽  
Excitation Mechanism ◽  
Balmer Series ◽  
Detection And Identification ◽  
Balmer Lines ◽  
Low Metallicity ◽  
Resolution Data ◽  
Narrow Emission

AbstractThe first detection and identification of deuterium Balmer lines were recently reported in H ii regions, using high spectral resolution data secured at CFHT and VLT. The Di lines appear as faint, narrow emission features in the blue wings of the H i Balmer lines and can be distinguished from high-velocity Hi emission. The identification as deuterium and the excitation mechanism as fluorescence are both established beyond doubt. The deuterium Balmer series might lead to a new, optical method of deuterium abundance measurement in the interstellar medium. This may be the only way to observe atomic deuterium in objects like the Magellanic Clouds or low metallicity blue compact galaxies.

Frontier: Autonomy in Detection, Actuation, and Planning for Robotic Weeding Systems

2021 ◽  
Vol 64 (2) ◽  
pp. 557-563
Author(s):  
Piyush Pandey ◽  
Hemanth Narayan Dakshinamurthy ◽  
Sierra N. Young
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Research And Development ◽  
Weed Control ◽  
List Type ◽  
Automated Systems ◽  
Weed Detection ◽  
Detection And Identification ◽  
Key Technologies ◽  
Robotic Weeding

HighlightsRecent research and development efforts center around developing smaller, portable robotic weeding systems.Deep learning methods have resulted in accurate, fast, and robust weed detection and identification.Additional key technologies under development include precision actuation and multi-vehicle planning. Keywords: Artificial intelligence, Automated systems, Automated weeding, Weed control.

scholarly journals PERSIANN-MSA: A Precipitation Estimation Method from Satellite-Based Multispectral Analysis

2009 ◽  
Vol 10 (6) ◽  
pp. 1414-1429 ◽  
Author(s):  
Ali Behrangi ◽  
Kuo-lin Hsu ◽  
Bisher Imam ◽  
Soroosh Sorooshian ◽  
George J. Huffman ◽  
...  
Keyword(s):  
Estimation Method ◽  
Principal Component ◽  
Input Information ◽  
Multispectral Data ◽  
Spectral Bands ◽  
Infrared Imager ◽  
Precipitation Estimation ◽  
Multispectral Analysis ◽  
Near Future ◽  
Infrared Data

Abstract Visible and infrared data obtained from instruments onboard geostationary satellites have been extensively used for monitoring clouds and their evolution. The Advanced Baseline Imager (ABI) that will be launched onboard the Geostationary Operational Environmental Satellite-R (GOES-R) series in the near future will offer a larger range of spectral bands; hence, it will provide observations of cloud and rain systems at even finer spatial, temporal, and spectral resolutions than are possible with the current GOES. In this paper, a new method called Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks–Multispectral Analysis (PERSIANN-MSA) is proposed to evaluate the effect of using multispectral imagery on precipitation estimation. The proposed approach uses a self-organizing feature map (SOFM) to classify multidimensional input information, extracted from each grid box and corresponding textural features of multispectral bands. In addition, principal component analysis (PCA) is used to reduce the dimensionality to a few independent input features while preserving most of the variations of all input information. The above method is applied to estimate rainfall using multiple channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. In comparison to the use of a single thermal infrared channel, the analysis shows that using multispectral data has the potential to improve rain detection and estimation skills with an average of more than 50% gain in equitable threat score for rain/no-rain detection, and more than 20% gain in correlation coefficient associated with rain-rate estimation.

Medical image denoising using simple form of MMSE estimation in Poisson–Gaussian noise model

2016 ◽  
Vol 09 (02) ◽  
pp. 1650020 ◽  
Author(s):  
Pichid Kittisuwan
Keyword(s):  
Simple Form ◽  
Gaussian Noise ◽  
Medical Image ◽  
Additive White Gaussian Noise ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Imaging Systems ◽  
Noise Model ◽  
Logistic Distribution ◽  
Noise Reduction Algorithm

Poisson–Gaussian noise is the basis of image formation for a great number of imaging systems used in variety of applications, including medical and astronomical imaging. In wavelet domain, the application of Bayesian estimation method with generalized Anscombe transform in Poisson–Gaussian noise reduction algorithm has shown remarkable success over the last decade. The generalized Anscombe transform is exerted to convert the Poisson–Gaussian noise into an additive white Gaussian noise (AWGN). So, the resulting data can be denoised with any algorithm designed for the removal of AWGN. Here, we present simple form of minimum mean square error (MMSE) estimator for logistic distribution in Poisson–Gaussian noise. The experimental results show that the proposed method yields good denoising results.

Weeds of Australian rangelands

2006 ◽  
Vol 28 (1) ◽  
pp. 3 ◽  
Author(s):  
Tara G. Martin ◽  
Shane Campbell ◽  
Simone Grounds
Keyword(s):  
Plant Species ◽  
Weed Management ◽  
Expert Knowledge ◽  
Cost Effective ◽  
Perennial Grasses ◽  
Native Plant ◽  
Weed Detection ◽  
Native Plant Species ◽  
Natural Systems ◽  
Detection And Identification

Despite recognition that non-native plant species represent a substantial risk to natural systems, there is currently no compilation of weeds that impact on the biodiversity of the rangelands within Australia. Using published and expert knowledge, this paper presents a list of 622 non-native naturalised species known to occur within the rangelands. Of these, 160 species (26%) are considered a current threat to rangeland biodiversity. Most of these plant species have been deliberately introduced for forage or other commercial use (e.g. nursery trade). Among growth forms, shrubs and perennial grasses comprise over 50% of species that pose the greatest risk to rangeland biodiversity. We identify regions within the rangelands containing both high biodiversity values and a high proportion of weeds and recommend these areas as priorities for weed management. Finally, we examine the resources available for weed detection and identification since detecting weeds in the early stages of invasion is the most cost effective method of reducing further impact.

scholarly journals Weed Density Detection Method Based on Absolute Feature Corner Points in Field

Agronomy ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 113
Author(s):  
Yanlei Xu ◽  
Run He ◽  
Zongmei Gao ◽  
Chenxiao Li ◽  
Yuting Zhai ◽  
...  
Keyword(s):  
Weed Management ◽  
Detection Method ◽  
Digital Camera ◽  
Weed Detection ◽  
Position Information ◽  
Weed Density ◽  
Harris Corner ◽  
Weed Distribution ◽  
First Time ◽  
Precision Spraying

Field weeds identification is challenging for precision spraying, i.e., the automation identification of the weeds from the crops. For rapidly obtaining weed distribution in field, this study developed a weed density detection method based on absolute feature corner point (AFCP) algorithm for the first time. For optimizing the AFCP algorithm, image preprocessing was firstly performed through a sub-module processing capable of segmenting and optimizing the field images. The AFCP algorithm improved Harris corner to extract corners of single crop and weed and then sub-absolute corner classifier as well as absolute corner classifier were proposed for absolute corners detection of crop rows. Then, the AFCP algorithm merged absolute corners to identify crop and weed position information. Meanwhile, the weed distribution was obtained based on two weed density parameters (weed pressure and cluster rate). At last, the AFCP algorithm was validated based on the images that were obtained using one typical digital camera mounted on the tractor in field. The results showed that the proposed weed detection method manifested well given its ability to process an image of 2748 × 576 pixels using 782 ms as well as its accuracy in identifying weeds reaching 90.3%. Such results indicated that the weed detection method based on AFCP algorithm met the requirements of practical weed management in field, including the real-time images computation processing and accuracy, which provided the theoretical base for the precision spraying operations.

scholarly journals 3-D Shape Reconstruction of Non-Uniform Reflectance Surface Based on Pixel Intensity, Pixel Color and Camera Exposure Time Adaptive Adjustment

2020 ◽  
Author(s):  
Jianhua Wang ◽  
Yanxi Yang ◽  
Yuguo Zhou
Keyword(s):  
Exposure Time ◽  
Three Dimensional ◽  
Estimation Method ◽  
Shape Reconstruction ◽  
Color Information ◽  
Pixel Intensity ◽  
Light Interference ◽  
Novel Approach ◽  
Pixel Color ◽  
The Impact

Abstract Three-dimensional (3-D) surface reconstruction of reflective objects and colored objects is a challenge. To this end, we proposed a novel approach. First, a new method of pixel matching between the projected image and the captured image is proposed. Then, we analyzed the impact of color texture and reflective surface on the captured image quality, and proposed a black-and-white (B/W) camera-based color information extraction method, an object surface reflectance and ambient light interference estimation method. Finally, the pixel color, pixel intensity and exposure time can be adaptively adjusted according to the color information and reflectivity of the measured objects. Experiments verified that the proposed method can not only obtain high-quality captured images, but also has a smaller number of additional images and a wider range of applications than existing methods.

scholarly journals What Can Multifractal Analysis Tell Us about Hyperspectral Imagery?

2020 ◽  
Vol 12 (24) ◽  
pp. 4077
Author(s):  
Michał Krupiński ◽  
Anna Wawrzaszek ◽  
Wojciech Drzewiecki ◽  
Małgorzata Jenerowicz ◽  
Sebastian Aleksandrowicz
Keyword(s):  
Infrared Imaging ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
High Spectral Resolution ◽  
Absorption Bands ◽  
Landscape Type ◽  
Spectral Bands ◽  
Before And After ◽  
Additional Value

Hyperspectral images provide complex information about the Earth’s surface due to their very high spectral resolution (hundreds of spectral bands per pixel). Effective processing of such a large amount of data requires dedicated analysis methods. Therefore, this research applies, for the first time, the degree of multifractality to the global description of all spectral bands of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. Subsets of four hyperspectral images, presenting four landscape types, are analysed. In particular, we verify whether multifractality can be detected in all spectral bands. Furthermore, we analyse variability in multifractality as a function of wavelength, for data before and after atmospheric correction. We try to identify absorption bands and discuss whether multifractal parameters provide additional value or can help in the problem of dimensionality reduction in hyperspectral data or landscape type classification.

Tropospheric Mineral Dust Study by High Spectral Resolution Infrared Satellite during intense dust storms

2021 ◽  
Author(s):  
Perla Alalam ◽  
Hervé Herbin
Keyword(s):  
Mineral Dust ◽  
Complex Refractive Index ◽  
Estimation Method ◽  
Dust Storms ◽  
High Spectral Resolution ◽  
Dust Particles ◽  
Physical Parameters ◽  
Chemical Compositions ◽  
Storm Events ◽  
Atmospheric Measurements

<p>Large desert lands such as Sahara, Gobi or Australia present main sources of atmospheric mineral dust caused by intense dust storms. Transported dust particles undergo physical and chemical changes affecting their microphysical and optical properties. This modifies their scattering and absorption properties and alters the global atmospheric radiative budget.</p><p>Currently, remote sensing techniques represent a powerful tool for quantitative atmospheric measurements and the only means of analyzing its evolution from local to global scale. In order to improve the knowledge of atmospheric aerosol distributions, many efforts were made particularly in the development of hyperspectral infrared spectrometers and processing algorithms. However, to fully exploit these measurements, a perfect knowledge of Complex Refractive Index (CRI) is required.</p><p>In that purpose, a new methodology <sup></sup>based on laboratory measurements of mineral dust in suspension coupled with an optimal estimation method has been developed. This approach allows getting access to CRI of several desert samples with various chemical compositions.</p><p>Here, we present the first results of the physical parameters (effective radius and concentration) retrievals using Infrared Atmospheric Sounding Interferometer IASI data, during dust storm events. The latter use the CRI of different desert samples obtained in laboratory and a new radiative transfer algorithm (ARAHMIS) developed at Laboratoire d’Optique Atmosphérique LOA.</p>

Sign in / Sign up

Export Citation Format

Share Document