Crop Type Discrimination and Health Assessment using Hyperspectral Imaging

Nigam, Rahul; Tripathy, Rojalin; Dutta, Sujay Kumar; Bhagia, Nita; Nagori, R.; Chandrasekar, K.; Kot, Rajsi; Bhattacharya, Bimal K.; Ustin, Susan L.

doi:10.18520/cs/v116/i7/1108-1123

Cited by 22 publications

(14 citation statements)

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“…В результате было установлено, что гиперспектральные данные обеспечивают лучшую точность классификации исследованных объектов по сравнению с мультиспектральными снимками, и более точное картографирование состояния земель сельскохозяйственного назначения (Thenkabail et al, 2004;Gitelson et al, 2011;Clark, 2017;Nigam et al, 2019).…”

Section: рис 4 разнообразие залежных массивов по качеству почвенныхunclassified

“…Совместные исследования ученых Индии и США (Nigam et al, 2019) с использованием гиперспектрального анализа показали (Рисунок 10), что вредоносность Puccinia имеет весьма узкий спектр распознавания -лишь в диапазонах видимой части солнечного спектра 662-702 нм и среднем ИК диапазоне SWIR 2155-2175 нм.…”

Section: рис 4 разнообразие залежных массивов по качеству почвенныхunclassified

“…Рис. 10. а) Распознавание кривой светоотражения для идентификации болезни пшеницы; б) отражение света массивами здоровой и больной пшеницы с использованием различных методов анализа светоотражения экосистем (Nigam et al, 2019). В видимой коротковолновой части спектра от 400 до 500 нм, при использовании двух вариантов анализа, различия в спектре объектов практически отсутствуют.…”

Section: рис 4 разнообразие залежных массивов по качеству почвенныхunclassified

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Improved Methodology for the Analysis of Space Information on the Solar Energy Absorption by Terrestrial Ecosystems

2020

Biogeosystem Technique

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The article outlines the satellite images using principles by the solar radiation full spectrum absorption analysing, including both the near and middle infrared ranges, which can currently be used by any land users. Since 1980, American researchers A. Gitelson et al. and P.S. Thenkabail et al. began to use the methodology based on the laboratory spectrometers studying of the plants leaf apparatus area, the water content, as well as for the chlorophyll and other components in plants. Very important result was a wheat rust (Puccinia) remote diagnosis. Since 2010, they began to install the devices on unmanned aerial vehicles, and then on airplanes. In 2015-2017, American researchers, together with Indian scientists, conducted a survey using the above-mentioned technique to separate the annual and perennial field crop sections in several regions of India. German researchers applied the same technique to study the system of wheat fertilizer application. Already in 1980, the Russian scientists at Omsk State Agrarian University substantiated the possibility of studying the abovementioned issues, but using standard satellite imagery. Since 2004, the Russian scientists at Omsk State Agrarian University began to use their sophisticated analysis by image clustering in various natural zones of the Middle Priirtyshye. It was found that the most informative is the mid-infrared range of the solar radiation spectrum. This makes it possible to distinguish the field crops and soil types differences. At the same time, the possibility of the automorphic and hydromorphic agroecosystems state diagnosing was established. Both when used in field crop rotation and in a fall state. The factors were determined of the feasibility and priority of the arable lands redevelopment into arable land.

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Section: рис 4 разнообразие залежных массивов по качеству почвенныхunclassified

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Improved Methodology for the Analysis of Space Information on the Solar Energy Absorption by Terrestrial Ecosystems

2020

Biogeosystem Technique

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“…Basically three types of sensors are widely used in Hyperspectral imaging: (i) The push broom scanners and the related whisk broom scanners:, helps in image scanning (ii) The band sequential scanner helps in spectral scanning, where images of an area pertaining to different wavelengths are captured (iii) The snapshot Hyperspectral imaging is the third variation of sensors where an staring array is used to create an image in a given instant.The researches carried out in last few decades extracted a lot amount of information with respect to spatial, temporal and spectral resolutions. The HSIs carry a generous amount of spectral information, which introduced many newer applications, new technological challenges and advancement in newer domains of research [1]. The high spectral resolution made the HSI to be used in extraction of subtle objects, materials with and are widely used in applications like remote sensing, quantification [2], identification, surveillance [3] and in precision urban agriculture [4].The first Indian Imaging Satellite by (HySIS) was launched by ISRO on 29 th November which captured HSI images and used for earth observations, applications related to forestry, geology, assessment of coastal zones, agriculture and studies related to environment.…”

Section: Introductionmentioning

confidence: 99%

Approaches for Hyperspectral Image Classification Detailed Review

Kushalatha¹,

Prasantha²,

Shetty³

2021

IJSCE

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Hyperspectral Image (HSI) processing is the new advancement in image / signal processing field. The growth over the years is appreciable. The main reason behind the successful growth of the Hyperspectral imaging field is due to the enormous amount of spectral and spatial information that the imagery contains. The spectral band that the HSI which contains is also more in number. When an image is captured through the HSI cameras, it contains around 200-250 images of the same scene. Nowadays HSI is used extensively in the fields of environmental monitoring, Crop-Field monitoring, Classification, Identification, Remote sensing applications, Surveillance etc. The spectral and spatial information content present in Hyperspectral images are with high resolutions.Hyperspectral imaging has shown significant growth and widely used in most of the remote sensing applications due to its presence of information of a scene over hundreds of contiguous bands In. Hyperspectral Image Classification of materials is the critical application of HSI using Hyperspectral sensors. It collects hundreds of spectrum channels, where each channel consists of a sharp point of Electromagnetic Spectrum. The paper mainly focuses on Deep Learning techniques such as Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Support Vector machines (SVM), K-Nearest Neighbour (KNN) for the accuracy in classification. Finally in the summary the current state-of-the-art scheme, a critical discussion after reviewing the research work by other professionals and organizing it into review-based paper, also implying about the present status on classification accuracy using neural networks is carried out.

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“…The researches carried out in last few decades extracted a lot amount of information with respect to spatial, temporal and spectral resolutions. The multispectral imaging carries a generous amount of spectral information, which introduced many newer applications, new technological challenges and advancement in newer domains of research [1]. The high spectral resolution made the multispectral imaging to be used in extraction of subtle objects, materials with and are widely used in applications like remote sensing, quantification [2], identification, and surveillance [3] and in precision urban agriculture [4].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Image Classification Techniques and Its Applications

R¹,

Prashantha²,

Shetty³

2021

AJCT

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Image classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of multispectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, multispectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In this paper, we propose a novel classification method for multispectral imagery, named as support vector machine (SVM). Pixel multispectral imagery can be represented by amplitude, phase and residual in frequency. Its applicability and effects are assessed by the experiment using data set, in which CNN, and KNN based feature extraction methods are adopted for comparison, aiming to evaluate the performance of the proposed method. Experimental results illustrate that the proposed model gains the highest classification accuracy. Comparison of various performance parameters showed that KNN works better than SVM.

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Crop Type Discrimination and Health Assessment using Hyperspectral Imaging

Cited by 22 publications

References 0 publications

Improved Methodology for the Analysis of Space Information on the Solar Energy Absorption by Terrestrial Ecosystems

Improved Methodology for the Analysis of Space Information on the Solar Energy Absorption by Terrestrial Ecosystems

Approaches for Hyperspectral Image Classification Detailed Review

Comparison of Image Classification Techniques and Its Applications

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