Crop Discrimination Using Field Hyper Spectral Remotely Sensed Data

Journals Menu

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Advances in Remote Sensing

ISSN Print: 2169-267X
ISSN Online: 2169-2688
www.scirp.org/journal/ars
E-mail: ars@scirp.org

Google-based Impact Factor: 1.5
Citations h5-index & Ranking*

Journals Menu

"Crop Discrimination Using Field Hyper Spectral Remotely Sensed Data"
written by Sayed M. Arafat, Mohamed A. Aboelghar, Eslam F. Ahmed,
published by Advances in Remote Sensing, Vol.2 No.2, 2013

has been cited by the following article(s):

Google Scholar
CrossRef

[1]	Hyperspectral-Based Classification of Managed Permanent Grassland with Multilayer Perceptrons: Influence of Spectral Band Count and Spectral Regions on Model …
	Agriculture, 2022

[2]	Bayesian Aggregation Improves Traditional Single-Image Crop Classification Approaches
	Sensors, 2022

[3]	Potential of remote and proximal sensing, publicly available datasets and machine learning for site-specific management in Australian irrigated cotton systems
	2022

[4]	Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery
	Agronomy, 2021

[5]	Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data
	2021

[6]	Maize and sorghum field segregation using multi-temporal Sentinel-2 data in central Mexico
	2021

[7]	Real-Time Agriculture Yield Monitor System (AYMS) Using Deep Feedforward (DFF) Neural Network
	2020

[8]	Approaches, applications, and future directions for hyperspectral vegetation studies: An emphasis on yield‐limiting factors in wheat
	2020

[9]	and an Outlook
	2020

[10]	Bayesian aggregation improves traditional single image crop classification approaches
	2020

[11]	Environmental Applications of Remote Sensing in Egypt: A Review and an Outlook
	2020

[12]	Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data. Remote Sens. 2021, 13, 107
	2020

[13]	Machine learning techniques for selecting the crop to increase the yield
	2019

[14]	Analyzing Canarian Archeology Heritage through Hyperspectral lmage Analysis
	2019

[15]	İnsansız hava araçları kullanılarak multi-spektral kameralar ile bitki örtüsü türlerinin ayırt edilmesi ve sınıflandırılması
	2019

[16]	Discrimination Analysis of Corn Species Using Field Hyperspectral Data
	2019

[17]	Identification of mangrove plant using hyperspectral remote sensing data along the Red Sea, Egypt
	Egyptian Journal of Aquatic Biology & Fisheries, 2019

[18]	Reckoning of Photosynthetic Pigments Using Remotely Sensed Spectral Responses of Vigna Radiata Crop for Surge Monitoring
	Proceedings of 2nd International Conference on Communication, Computing and Networking, 2019

[19]	Development of an Apparatus for Crop-Growth Monitoring and Diagnosis
	Sensors, 2018

[20]	Advances in Land Use classification of Urban Areas from Hyperspectral Data
	2018

[21]	Automatic Extraction of Built-Up area From EO-1 Hyperion Hyperspectral Satellite Image Based on NDBI Index
	2018

[22]	Comparative Evaluation between Multispectral and Hyperspectral Data for Discrimination of Fruit Crops using Statistical Techniques
	JOURNAL OF THE INDIAN SOCIETY OF AGRICULTURAL STATISTICS, 2018

[23]	Operaciones de detección y clasificación de señales en imágenes espectrales muestreadas mediante un sistema de sensado compresivo.
	2017

[24]	Una comparación cuantitativa y cualitativa de análisis de rendimiento de las cámaras espectrales compresiva
	2017

[25]	A quantitative and qualitative performance analysis of compressive spectral imagers
	Tecnura?, 2017

[26]	Desarrollo De Un Sistema De Adquisicion De Imagenes Hiperespectrales Para Muestras De Anatomia Patologica
	2017

[27]	Hyperspectral Remote Sensing and Field Measurements for Forest Characteristics-A Case Study in the Hainich National Park, Central Germany
	2017 - Dissertation, Göttingen, 2017

[28]	Evaluation of Spectral Reflectance of Iranian Wheat and barley Varieties Canopies at different growth stages using vegetation
	2016

[29]	Site-Specific Weed Management Using Remote Sensing
	2016

[30]	Hyperspectral Remote Sensing and Field Measurements for Forest Characteristics
	Dissertation, 2016

[31]	Paper Title (use style: paper title)
	2016

[32]	Precision Agriculture using Advanced Remote Sensing techniques for peanut crop in Arid Land
	Journal of Hyperspectral Remote Sensing, 2016

[33]	Development of spectral indices for roofing material condition status detection using field spectroscopy and WorldView-3 data
	Journal of Applied Remote Sensing, 2016

[34]	Evolution of regional to global paddy rice mapping methods: A review
	ISPRS Journal of Photogrammetry and Remote Sensing, 2016

[35]	SPECTRAL DISCRIMINATION AND INDEX DEVELOPMENT OF ROOFING MATERIALS AND CONDITIONS USING FIELD SPECTROSCOPY AND …
	2016

[36]	آنالیز تفکیک‌پذیری طیف‌های حاصل از مشاهدات میدانی ارقام گندم و جوی ایرانی در مراحل مختلف رشد آن‌ها با استفاده از شاخص‌های گیاهی‎
	2016

[37]	Aplicações e desafios do sensoriamento remoto hiperespectral na geologia colombiana
	2015

[38]	OPTICAL AND MICROWAVE REMOTE SENSING IN AGRICULTURAL LAND STUDIES
	2015

[39]	Evaluation of OLI Sensor Images Potential in the Separation of six Iranian WheatVarieties Differentiation using Spectral Library
	2015

[40]	Applications and challenges of hyperspectral remote sensing in the colombian geology
	Facultad de Ingeniería, 2015

[41]	Spectral feature selection and classification of roofing materials using field spectroscopy data
	Journal of Applied Remote Sensing, 2015

[42]	Crop Selection Method to maximize crop yield rate using machine learning technique
	Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), 2015 International Conference on, 2015

[43]	Aplicaciones y retos del sensado remoto hiperespectral en la geología colombiana
	Facultad de Ingeniería, 2015

[44]	Applications and challenges of hyperspectral remote sensing in the Colombian geology/Aplicaciones y retos del sensado remoto hiperespectral en la geologia …
	2015

[45]	冬小麦冠层高光谱对低温胁迫的响应特征
	光谱学与光谱分析, 2014

[46]	Regional scale soil salinity evaluation using Landsat 7, western San Joaquin Valley, California, USA
	Geoderma Regional, 2014

[47]	Hyperspectral data for forest inventories
	2014

[48]	Molecular mechanisms of the human pathogen Candida glabrata involved in the interaction with the host
	2007

[1]	Hyperspectral-Based Classification of Managed Permanent Grassland with Multilayer Perceptrons: Influence of Spectral Band Count and Spectral Regions on Model Performance Agriculture, 2022 DOI:10.3390/agriculture12050579

[2]	Bayesian Aggregation Improves Traditional Single-Image Crop Classification Approaches Sensors, 2022 DOI:10.3390/s22228600

[3]	Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery Agronomy, 2021 DOI:10.3390/agronomy11071435

[4]	Approaches for Hyperspectral Image Classification Detailed Review International Journal of Soft Computing and Engineering, 2021 DOI:10.35940/ijsce.A3522.0911121

[5]	Approaches, applications, and future directions for hyperspectral vegetation studies: An emphasis on yield‐limiting factors in wheat The Plant Phenome Journal, 2020 DOI:10.1002/ppj2.20007

[6]	Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data Remote Sensing, 2020 DOI:10.3390/rs13010107

[7]	Real-Time Agriculture Yield Monitor System (AYMS) Using Deep Feedforward (DFF) Neural Network SSRN Electronic Journal , 2020 DOI:10.2139/ssrn.3734201

[8]	Environmental Remote Sensing in Egypt Springer Geophysics, 2020 DOI:10.1007/978-3-030-39593-3_4

[9]	Discrimination Analysis of Corn Species Using Field Hyperspectral Data 2019 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS), 2019 DOI:10.1109/AGERS48446.2019.9034340

[10]	Proceedings of 2nd International Conference on Communication, Computing and Networking Lecture Notes in Networks and Systems, 2019 DOI:10.1007/978-981-13-1217-5_72

[11]	Development of an Apparatus for Crop-Growth Monitoring and Diagnosis Sensors, 2018 DOI:10.3390/s18093129

[12]	Discriminant analysis for prediction and classification of farmers based on adoption of drought coping mechanisms AGRICULTURE UPDATE, 2017 DOI:10.15740/HAS/AU/12.TECHSEAR(3)2017/635-640

[13]	Evaluation of Spectral Reflectance of Iranian Wheat and barley Varieties Canopies at different growth stages using vegetation Journal of Geospatial Information Technology, 2016 DOI:10.29252/jgit.4.1.61

[14]	Development of spectral indices for roofing material condition status detection using field spectroscopy and WorldView-3 data Journal of Applied Remote Sensing, 2016 DOI:10.1117/1.JRS.10.025021

[15]	Evolution of regional to global paddy rice mapping methods: A review ISPRS Journal of Photogrammetry and Remote Sensing, 2016 DOI:10.1016/j.isprsjprs.2016.05.010

[16]	Evolution of regional to global paddy rice mapping methods: A review ISPRS Journal of Photogrammetry and Remote Sensing, 2016 DOI:10.1016/j.isprsjprs.2016.05.010

[17]	Crop Selection Method to maximize crop yield rate using machine learning technique 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), 2015 DOI:10.1109/ICSTM.2015.7225403

[18]	Regional scale soil salinity evaluation using Landsat 7, western San Joaquin Valley, California, USA Geoderma Regional, 2014 DOI:10.1016/j.geodrs.2014.10.004

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Advances in Remote Sensing

Advances in Remote Sensing

Journals Menu

Home

About SCIRP

Service

Policies