A High-Performance Infrastructure for Remote Sensing Data Applications Using HPC Paradigms

Chembian, W. T.; Hemanand, D.; Roy, A. Thomas Paul; Franklin, P. Deepak; Kumari, G. Krishna; Kumar, N. C. Sendhil; Hemanth, S V; Soundari, A. Gnana

doi:10.37391/ijeer.100255

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…The multiscale Object based CNN [5] [26] uses aerial imagery taken from National Agriculture Imagery Program (NAIP) conducted in 2015 in the Iowa state of USA. A large dataset was created consisting of ~6100 image tiles resulting in around 1million images, roughly 955 GB with 140-170 MB /tile.…”

Section: Dataset Preparationmentioning

confidence: 99%

“…The process of remotely identifying, measuring and studying the features of an entity or land cover surface from a distance is called remote sensing. In the past few years, there has been remarkable increase in the generation and collection of remote sensing data [31] due to the introduction of numerous active and passive remote sensors into the space. This has led to a huge growth in the size of RS datasets.…”

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A Comparative Study of the CNN Based Models Used for Remote Sensing Image Classification

Murthy M S

2023

IJEER

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Remotely sensed images, their classification and accuracy play a vital role in measuring a country’s scientific growth and technological development. Remote Sensing (RS) can be interpreted as a way of assessing the characteristics of a surface or an entity from a distance. This task of identifying and classifying datasets of RS images can be done using Convolutional Neural Network (CNN). For classifying images of large-scale areas, the traditional CNN approach produces coarse maps. For addressing this issue, Object based CNN method can be used. Classifying images with high spatial resolution can be done effectively using Object based image analysis. Deep learning methods offer the strength of auto learning the spatial features of an image. Object scale based adaptive CNN is a novel technique that can improve the accuracy of image classification of high spatial resolution images. For efficient RS image classification, a novel Deep learning approach called distributed CNN can be used which leads to enhanced accuracy of RS image classification. In this paper, three CNN models have been compared while considering the training time and efficiency to classify RS images as parameters of measure to assess the CNN models.

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Section: Dataset Preparationmentioning

confidence: 99%

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A Comparative Study of the CNN Based Models Used for Remote Sensing Image Classification

Murthy M S

2023

IJEER

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Assessment of Selection of Suitable for Spar of the HumanPowered Aircraft Using COPRAS Method

Sudha¹,

Vidhya²,

Vimala³

et al. 2023

aae

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Generally speaking, while making decisions, it is more customary to rely on intuition rather than any form of numerical technique. In areas where there are many options and many factors impacting the choice, such as the selection of materials, a more precise approach would be necessary. It is necessary to understand the material's characteristics, price, design principles, and interactions while making mechanical parts. Material science has not only produced traditional materials such as metals, ceramics, and polymers but has also given rise to various novel materials like composites, smart (intelligent) materials, and functionally graded materials. A designer now has access to a wider range of materials than ever before thanks to the growth of knowledge. In these instances, a strategic choice must be made in order to satisfy all the functional specifications of the product while upholding client requests and preferences. The performance criteria of the component must be specified, along with a general description of the properties and processing needs of the primary material, As the first stage of the material selection process. Consequently, certain material types might be ruled out, while others are chosen as potential options for the component. The relevant material properties are subsequently listed and assigned a priority ranking. The best material can then be chosen using optimization techniques. The result of the COPRAS analysis is followed by Al 2024-T4 is the first rank, Carbon 63% Epoxy is last rank. Based on the analysis, the materials ranked highest for usage are as follows: Al 2024-T4 aluminum alloy, E-glass 73%-Epoxy, E-glass 56% Epoxy, and E-glass 65% Polyester. These selections have been carefully assessed and determined to be the most suitable options, taking into account their specific properties and how well they align with the intended application. The Al 2024-T4 aluminum alloy exhibits excellent strength-to-weight ratio and corrosion resistance, making it an ideal choice for structural components. E-glass 73%-Epoxy, E-glass 56% Epoxy, and E-glass 65% Polyester offer a combination of strength, flexibility, and durability, catering to various requirements within the application. The meticulous evaluation of these materials ensures that the chosen ones will deliver optimal performance and reliability in the designated context.

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Evaluation of Drinking Water Quality for Salem District Using Weighted Product Method

Sathiyaraj¹,

Sangeetha²,

Ramachandran³

et al. 2023

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The quality of drinking water is one of the most important factors affecting human health. However, in many countries, especially developing countries the quality of drinking water is not satisfactory and Poor drinking water quality causes many waterborne diseases. This special issue of Expression and Health focuses on drinking water quality Edited to better understand the implications for public health, thus improving many countries appropriate action may be taken. This editorial introduction, reviewed some recent research, briefly summarizing the main points of each contribution to this issue, then in to increase scientific proposed some research/directions. The articles in this magazine are interesting and this research covers many aspects of the topic, and is meaningful for sustainable drinking water quality protection. Using the correct linear mathematical equation Home selection problems cannot be solved and treated as semi-structured problem a heuristic process is required. Computer-based decision support systems can be used as an alternative to expert decision-making by inexperienced users. This article is about improving decision support systems for the household selection and Presents empirical work. For choosing a house based on weighted product method providing web-based decision support systems with the general objective of the research. As a result of the interview with the respondents, the calculation of choosing this house. In the weighted product method, help people choose the best alternative, Computational results show that the given alternatives can be sorted. The Alternative Parameters is Salem West, Salem South, Yercaud, Attur, Pethanaickenpalayam, Valapady, Sankari, Edapadi, Mettur, Omalur, Kadayampatti. The Evaluation Parameters is PH, TDS, TH, Calcium, Magnesium, Chloride, Sulphate. First rank is Pethanaickenpalayam and first rank is Salem West is lowest rank.

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A High-Performance Infrastructure for Remote Sensing Data Applications Using HPC Paradigms

Cited by 3 publications

References 19 publications

A Comparative Study of the CNN Based Models Used for Remote Sensing Image Classification

A Comparative Study of the CNN Based Models Used for Remote Sensing Image Classification

Assessment of Selection of Suitable for Spar of the HumanPowered Aircraft Using COPRAS Method

Evaluation of Drinking Water Quality for Salem District Using Weighted Product Method

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