An enhanced back propagation method for change analysis of remote sensing images with adaptive preprocessing

C.P, Dalmiya; Santhi, N.; Sathyabama, B.

doi:10.1080/22797254.2019.1692637

Cited by 4 publications

(4 citation statements)

References 30 publications

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“…The critical agent is implemented based on fumigated clusters that use fluid learning and unsupervised clusters to improve the precision of segmenting results and noise immunity. The study shows promising results because of the multi-agent system in remote imaging segmentation [18].…”

Section: Review Finding On Mas In Medical Imagementioning

confidence: 87%

A Review of Medical Image Techniques and Methods based on a Multi-agent System

Nayyef¹,

Sulaiman²,

Hasan³

et al. 2022

TEM Journal

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In cases where the success of artificial intelligence (AI) approaches is more focused on the multi-agent system (MAS) than those that may make AI understandable to humans, in light of the repetitive cycles of the history of AI, we expect to see technology reevaluation that is commonly referred to as 'classical IA' – especially agent and MAS – in the upcoming years. Other than that, agents and multi-agent (MAS) systems are crucial to designing intelligent systems from the start. They can open new possibilities to researchers for explanatory and intelligent systems through their long-term link with logical technology, which is characteristic of its early times. That is why it is now crucial to comprehend the present position of MAS for medical image methods. This paper thus seeks to provide a complete overview by reviewing the MAS and the agent approaches. Two different views are taken and assessed for the resulting technologies: the MAS and the image processing technologies.

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Section: Review Finding On Mas In Medical Imagementioning

confidence: 87%

A Review of Medical Image Techniques and Methods based on a Multi-agent System

Nayyef¹,

Sulaiman²,

Hasan³

et al. 2022

TEM Journal

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“…There are many types of artificial neural networks in AI and the mainstream network structures used for change detection are described above. In addition, other networks, such as Hopfield networks [47,48,65,[205][206][207], back propagation networks [42,149,208,209], multilayer perceptrons (MLPs) [70,[210][211][212][213][214], extreme learning machines [215], and self-organizing map (SOM) networks [55,[216][217][218][219][220][221], do not require a large number of training samples to learn high-level abstract features as deep neural networks do, but due to their shallow network structure, low sample size requirements, and easy training process, they are also widely used in change detection tasks and can achieve satisfactory results. Since they can be regarded as traditional machine learning techniques, we will not make more detailed comments here due to space limitations and existing reviews [7,222,223].…”

Section: Other Artificial Neural Network and Ai Methodsmentioning

confidence: 99%

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

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Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.

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“…In this regard, Halimatu Sadiyah and Mustpha Zubair [7] proposed Convolutional Neural Network (CNN) [12,13] approach to improve pre-planning and post-harvest processing. Similarly, Enhanced Back-propagation neural network (NN) for automatic change detection (CD) using remote sensing (RS) image data has been explored by Dalmiya C.P et al [14]. In addition, Deep Convolutional Neural Network (DCNN) [15] has been proposed by J.P. Cobena Cevallos et al [16] to improve, evaluate, and estimate PA based on the information obtained from remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Multiple Sensors to Implement Precision Agriculture using IoT Infrastructure

Aboneh¹,

Rorissa²

2023

Preprint

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Precision Agriculture is the ability to handle variations in productivity within a field and maximize financial return, optimize resource utilization and minimize impact of the environment. It is also the process of automated data collection, cloud storage and utilization to build robust decision support system. In case of Ethiopia, due to poor communication infrastructure coverage and absence of the state-of-the-art technology in the agriculture sector, implementing precision farming system is a challenging tasks in the domain area. In this work, we proposed a fusion of multiple sensors using IOT and IIOT infrastructure to collect critical data from farming fields to develop precision farming facility for decision makers. The main purpose was to monitor weather variability, automate irrigation process, extract critical soil properties. In addition, we have used time series data collected from sensor devices to build forecasting model. Fusion of multiple IoT device provide a mechanism in the agriculture area to deal with real-time monitoring of crops. It is cost-effective technology and required low-energy with edge computing sensor device. We employed the Message Queuing Telemetry Transport (MQTT) protocol to connect the Industrial/Internet of Things (I/IoT) to the cloud server. The communication between system user and sensor device has been done via cloud using Node-RED platform, web android APIs. The cloud-based Eco-system allows us to aggregate, visualize, and analyze live streams output from each sensor in real-time manner. Finally, we have built time series forecasting model using records collected by each sensor device. Using the multi-variate time series data-set, we have obtained about 99 forecasting accuracy on some important variables. Finally, we have developed mobile and web-based application for the end-user to monitor the proposed system remotely.

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An enhanced back propagation method for change analysis of remote sensing images with adaptive preprocessing

Abstract: To cite this article: Dalmiya C.P , Santhi N & Sathyabama B (2020) An enhanced back propagation method for change analysis of remote sensing images with adaptive preprocessing,

Cited by 4 publications

References 30 publications

A Review of Medical Image Techniques and Methods based on a Multi-agent System

A Review of Medical Image Techniques and Methods based on a Multi-agent System

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Fusion of Multiple Sensors to Implement Precision Agriculture using IoT Infrastructure

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