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Crop Discrimination Using Field Hyper Spectral Remotely Sensed Data

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Advances in Remote Sensing 2013年第2期2卷 63-70页

作者： sayed M. arafat Mohamed A. Aboelghar Eslam F. Ahmed Agriculture Applications Department National Authority for Remote Sensing and Space Sciences (NARSS)Cairo Egypt

Crop discrimination through satellite imagery is still problematic. Accuracy of crop classification for high spatial resolution satellite imagery in the intensively cultivated lands of the Egyptian Nile delta is still low. Therefore, the main objective of this research is to determine the optimal hyperspectral wavebands in the spectral range of (400 - 2500 nm) to discriminate between two winter crops (Wheat and Clover) and two summer crops (Maize and Rice). This is considered as a first step to improve crop classification through satellite imagery in the intensively cultivated areas in Egypt. Hyperspectral ground measurements of ASD field Spec3 spectroradiometer was used to monitor the spectral reflectance profile during the period of the maximum growth stage of the four crops. 1-nm-wide was aggregated to 10-nm-wide bandwidths. After accounting for atmospheric windows and/or areas of significant noise, a total of 2150 narrow bands in 400 - 2500 nm were used in the analysis. Spectral reflectance was divided into six spectral zones: blue, green, red, near-infrared, shortwave infrared-I and shortwave infrared-II. One Way ANOVA and Tukey’s HSD post hoc analysis was performed to choose the optimal spectral zone that could be used to differentiate the different crops. Then, linear regression discrimination (LDA) was used to identify the specific optimal wavebands in the spectral zones in which each crop could be spectrally identified. The results of Tukey’s HSD showed that blue, NIR, SWIR-1 and SWIR-2 spectral zones are more sufficient in the discrimination between wheat and clover than green and red spectral zones. At the same time, all spectral zones were quite sufficient to discriminate between rice and maize. The results of (LDA) showed that the wavelength zone (727:1299 nm) was the optimal to identify clover crop while three zones (350:712, 1451:1562, 1951:2349 nm) could be used to identify wheat crop. The spectral zone (730:1299 nm) was the optimal to identify maize

关键词： Hyper Spectral Data Crop Discrimination

Assessment of Urban Heat Island Using Remotely Sensed Imagery over Greater Cairo, Egypt

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Advances in Remote Sensing 2015年第1期4卷 35-47页

作者： Khaled Abutaleb Adeline Ngie Ahmed Darwish Mahmoud Ahmed sayed arafat Fathi Ahmed University of Johannesburg Johannesburg South Africa National Authority for Remote Sensing and Space Sciences (NARSS) Cairo Egypt University of the Witwatersrand Johannesburg South Africa

The Urban Heat Island (UHI) results in significant and sometimes dramatic increases in air temperature differences between the urban environment and its surrounding areas. The heat island structure may extend from the ground to the top of roofs and canopy levels above ground. The Urban Heat Island effect is a leading factor in a long list of human health problems which are expected to increase with the rapid growth of urban populations and projected future climate change. Urban heat island studies can be conducted through either direct measurements of air temperature using automobile transects and weather station networks or through measuring surface temperature using airborne or satellite remote sensing. In this study, UHI was investigated over the Greater Cairo during both summer and winter seasons over two different dates. Landsat 7 ETM+ data were used and the mono-window algorithm was applied in the study. Results emphasis that both types of heat islands exist in the study area i.e. the surface and the atmospheric heat islands. Temperature differences ranged between 0.5°C to 3.5°C and these are much related to the existing land use/covers. It could be concluded that expansion of urban areas in Greater Cairo has led to increased thermal radiation of land surface on the highly populated areas.

关键词： UHI Landsat Remote Sensing Mono-Window Greater Cairo

Metaheuristic Optimization for Mobile Robot Navigation Based on Path Planning

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Computers, Materials & Continua 2022年第11期73卷 2241-2255页

作者： El-sayed M.El-kenawy Zeeshan Shafi Khan Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham arafat Ali Ali E.Takieldeen Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Science Department College of Engineering and Computer ScienceMustaqbal UniversityBuraidahSaudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Mechanical Engineering Qassim UniversityBuraidah51452Saudi Arabia

Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous *** is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite.A promising route planning for mobile robots on one side saves time and,on the other side,reduces the wear and tear on the robot,saving the capital *** route planning methods for the mobile robot have been developed and *** to our best knowledge,no method offers an optimum solution among the existing *** Swarm Optimization(PSO),a numerical optimization method based on the mobility of virtual particles in a multidimensional space,is considered one of the best algorithms for route planning under constantly changing environmental *** the researchers,reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile *** paper proposes a PSO Weighted Grey Wolf Optimization(PSOWGWO)*** is a hybrid algorithm based on enhanced Grey Wolf Optimization(GWO)with *** order to measure the statistical efficiency of the proposed algorithm,Wilcoxon rank-sum and ANOVA statistical tests are *** experimental results demonstrate a 25%to 45%enhancement in terms of Area Under Curve(AUC).Moreover,superior performance in terms of data size,path planning time,and accuracy is demonstrated over other state-of-the-art techniques.

关键词： Mobile robot swarm optimization robot route planning neural networks

Performance Evaluation of Multiple Classifiers for Predicting Fake News

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Journal of Computer and Communications 2022年第9期10卷 1-21页

作者： Arzina Tasnim Md. Saiduzzaman Mohammad arafat Rahman Jesmin Akhter Abu sayed Md. Mostafizur Rahaman Department of Information Systems Security Bangladesh University of Professionals Dhaka Bangladesh Institute of Information Technology Jahangirnagar University Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh

The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To assess their performance, we used 14 different classifiers in this study. Secondly, we looked at how soft voting and hard voting classifiers performed in a mixture of distinct individual classifiers. Finally, heuristics are used to create 9 models of stacking classifiers. The F1 score, prediction, recall, and accuracy have all been used to assess performance. Models 6 and 7 achieved the best accuracy of 96.13 while having a larger computational complexity. For benchmarking purposes, other individual classifiers are also tested.

关键词： Fake News Machine Learning TF-IDF Classifier Estimator F1 Score Recall Precision Voting Classifiers Stacking Classifier Soft Voting Hard Voting