Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery

Hong, Seok Min; Park, Sang‐Hyun; Ting, Kang; Kim, Moon Sung; Nam, Gibeom; Pyo, Juncheol

doi:10.1080/15481603.2022.2037887

Cited by 17 publications

(5 citation statements)

References 85 publications

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“…Image recognition technology is applied to the Internet of Things, and the current Internet of Things scenarios are classified by recognition to facilitate the cross-scenario application of the Internet of Things and promote the further development of the Internet of Things industry. At the present stage, in scene analysis, the use of a convolutional neural network [8] has obtained good results. The CNN method uses multiple convolutional layers to extract image features layer by layer and then restores the segmentation results layer by layer in deconvolution [9] .…”

Section: Related Workmentioning

confidence: 95%

Design and implementation of a scenario classification system for the internet of things application

Zheng,

Li,

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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With the rapid development of the Internet of Things industry today, it is imperative to classify the Internet of Things scenarios to adapt to the needs of the Internet of Things in different scenarios. In recent years, Convolutional Neural Network (CNN) has succeeded in scene parsing. The methods based on CNN generally extract the visual features of the input images through the step-by-step abstraction of multiple convolutional layers and then parse the corresponding segmentation results of the output images through the step-by-step restoration of multiple deconvolutions. Screen the scene pictures of the Internet of Things from the large data set, use the image recognition algorithm to extract the features of the photographs, and use the extracted features to cluster the image set (for example, it can be divided into a family environment, car environment, etc.), divide the pictures with similar features into the same set, and finally measure the clustering accuracy to realize the Internet of Things scene classification. Finally, scene recognition accuracy is about 87%, and the clustering of feature vectors is also satisfactory.

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Section: Related Workmentioning

confidence: 95%

Design and implementation of a scenario classification system for the internet of things application

Zheng,

Li,

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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“…[47] leveraged convolutional neural networks (CNNs) to map the invasive plant species leafy spurge (Euphorbia virgata) across a heterogeneous ecosystem in the USA with Worldview-2 imagery and obtained an accuracy of 96.1%. Furthermore, CNNR has prosperity in many continuous variable estimation applications [93,112,[120][121][122], particularly in the field of atmospheric pollution monitoring. Ref.…”

Section: Rfr and Cnnr Model Performance In Mapping Inws Continuous Covermentioning

confidence: 99%

Mapping the Continuous Cover of Invasive Noxious Weed Species Using Sentinel-2 Imagery and a Novel Convolutional Neural Regression Network

Xing,

An,

Guo

et al. 2024

Remote Sensing

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Invasive noxious weed species (INWS) are typical poisonous plants and forbs that are considered an increasing threat to the native alpine grassland ecosystems in the Qinghai–Tibetan Plateau (QTP). Accurate knowledge of the continuous cover of INWS across complex alpine grassland ecosystems over a large scale is required for their control and management. However, the cooccurrence of INWS and native grass species results in highly heterogeneous grass communities and generates mixed pixels detected by remote sensors, which causes uncertainty in classification. The continuous coverage of INWS at the pixel level has not yet been achieved. In this study, objective 1 was to test the capability of Senginel-2 imagery at estimating continuous INWS cover across complex alpine grasslands over a large scale and objective 2 was to assess the performance of the state-of-the-art convolutional neural network-based regression (CNNR) model in estimating continuous INWS cover. Therefore, a novel CNNR model and a random forest regression (RFR) model were evaluated for estimating INWS continuous cover using Sentinel-2 imagery. INWS continuous cover was estimated directly from Sentinel-2 imagery with an R2 ranging from 0.88 to 0.93 using the CNNR model. The RFR model combined with multiple features had a comparable accuracy, which was slightly lower than that of the CNNR model, with an R2 of approximately 0.85. Twelve green band-, red-edge band-, and near-infrared band-related features had important contributions to the RFR model. Our results demonstrate that the CNNR model performs well when estimating INWS continuous cover directly from Sentinel-2 imagery, and the RFR model combined with multiple features derived from the Sentinel-2 imager can also be used for INWS continuous cover mapping. Sentinel-2 imagery is suitable for mapping continuous INWS cover across complex alpine grasslands over a large scale. Our research provides information for the advanced mapping of the continuous cover of invasive species across complex grassland ecosystems or, more widely, terrestrial ecosystems over large spatial areas using remote sensors such as Sentinel-2.

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“…During the course of time, many researchers have implemented CNNs in a wide range of domains, such as medicine [40][41][42], engineering [43,44], agriculture [45], environmental monitoring [46][47][48][49], image classification [50], computing optimization [51], image processing [52], electricity forecasting [53,54], decision-making [55], meteorology [56].…”

Section: Stage II Developing the Cnn Forecasting Solutionmentioning

confidence: 99%

E-Commerce Sales Revenues Forecasting by Means of Dynamically Designing, Developing and Validating a Directed Acyclic Graph (DAG) Network for Deep Learning

et al. 2022

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As the digitalization process has become more and more important in our daily lives, during recent decades e-commerce has greatly increased in popularity, becoming increasingly used, therefore representing an extremely convenient alternative to traditional stores. In order to develop and maintain profitable businesses, traders need accurate forecasts concerning their future sales, a very difficult task considering that these are influenced by a wide variety of factors. This paper proposes a novel e-commerce sales forecasting method that dynamically builds a Directed Acyclic Graph Neural Network (DAGNN) for Deep Learning architecture. This will allow for long-term, fine-grained forecasts of daily sales revenue, refined up to the level of product categories. The developed forecasting method provides the e-commerce store owner an accurate forecasting tool for predicting the sales of each category of products for up to three months ahead. The method offers a high degree of scalability and generalization capability due to the dynamically incremental way in which the constituent elements of the DAGNN’s architecture are obtained. In addition, the proposed method achieves an efficient use of data by combining the numerous advantages of its constituent layers, registering very good performance metrics and processing times. The proposed method can be generalized and applied to forecast the sales for up to three months ahead in the case of other e-commerce stores, including large e-commerce businesses.

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Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery

Cited by 17 publications

References 85 publications

Design and implementation of a scenario classification system for the internet of things application

Design and implementation of a scenario classification system for the internet of things application

Mapping the Continuous Cover of Invasive Noxious Weed Species Using Sentinel-2 Imagery and a Novel Convolutional Neural Regression Network

E-Commerce Sales Revenues Forecasting by Means of Dynamically Designing, Developing and Validating a Directed Acyclic Graph (DAG) Network for Deep Learning

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