Hyperspectral Texture Metrology Based on Joint Probability of Spectral and Spatial Distribution

Chu, Rui Jian; Richard, Noël; Chatoux, Hermine; Fernández-Maloigne, Christine; Hardeberg, Jon Yngve

doi:10.1109/tip.2021.3071557

Cited by 5 publications

(2 citation statements)

References 70 publications

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“…3) After completing the polar coordinate transformation of the current sequence of the standardized new energy grid power load, the GAF method is used to convert the current sequence of the one-dimensional new energy grid power load into an norder matrix, and the values of the elements in the matrix are mapped to the blue-red color range, making the matrix a twodimensional image with color and texture distribution [14].…”

Section: A Sequence Visualization Of Power Load Data Of New Energy Gridmentioning

confidence: 99%

Classification Method for Power Load Data of New Energy Grid based on Improved OTSU Algorithm

Ma¹,

Li²,

Liu³

et al. 2022

IJACSA

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The classification method for power load data of new energy grid based on improved OTSU algorithm is studied to improve the classification accuracy of power load data. According to the idea of two-dimensional visualization of time series, GAF (Geographical Adaptive Fidelity) method is used to transform the current data of power load in new energy grid into twodimensional image of power load in new energy grid. The intra class dispersion is introduced, and the improved OTSU algorithm is used to segment the foreground and background of the twodimensional image according to the pixel gray value of the twodimensional image and the one-dimensional inter class variance corresponding to the pixel neighborhood gray value. The twodimensional foreground image of power load is taken as the input sample of convolution neural network. The convolution neural network extracts the features of the two-dimensional foreground image of power load through convolution layer. According to the extracted features, the classification results of power load data of new energy grid are output through three steps: nonlinear processing, pooling processing and full connection layer classification. The experimental results show that this method can accurately classify the power load data of new energy grid, and the classification accuracy is higher than 97%.

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Section: A Sequence Visualization Of Power Load Data Of New Energy Gridmentioning

confidence: 99%

Classification Method for Power Load Data of New Energy Grid based on Improved OTSU Algorithm

Ma¹,

Li²,

Liu³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…The term "content-based image retrieval," or CBIR, refers to a method that incorporates visual analysis of the elements that make up the query image in an effort to perhaps avoid the problems previously mentioned. Two methods for viewing photos that may extract low-level visual information from the image are Query-By-Image-Content (QBIC) and Simplicity [21]. When basic visual characteristics are combined into a single representation, the result is a feature that is stronger and more immediately discernible than the sum of its parts.…”

Section: Introductionmentioning

confidence: 99%

IRFSC: A high-efficiency hybrid model Image Retrieval Based on Feature Selection Using Continuous Learning Process

Lande

Ridhorkar

2023

Preprint

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Design of high-efficiency feature representation and ranking models is required for retrieval of images based on colour, texture, shape, and other visual aspects. These models must be able to increase retrieval precision while reducing the amount of error and delay required for ranking procedures. Low complexity models can run more quickly, but they are limited in their retrieval performance because they do not exhibit higher retrieval rates. This essay suggests designing a novel hybrid model for high-efficiency feature selection-based picture retrieval using a continuous learning approach to address these problems. A hybrid Elephant Herding Optimization (EHO) & Particle Swarm Optimization (PSO) layer is used in the model's initial extraction of large feature sets from multimodal images in order to continually maximize inter-class feature variance levels. These ranks are post-processed using an incremental optimization method based on Q-Learning, which supports in the continuous optimization of image data sets. As compared to recently proposed state-of-the-art models, the suggested model is able to preserve reduced delay while improving retrieval accuracy by 0.07%, precision by 10.5%, and recall by 3.60%. As a result, the proposed model can be used for a wide range of real-time use cases.

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Multi-scale spatial and spectral feature fusion for soil carbon content prediction based on hyperspectral images

Li,

Qiu

et al. 2024

Ecological Indicators

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Hyperspectral Texture Metrology Based on Joint Probability of Spectral and Spatial Distribution

Cited by 5 publications

References 70 publications

Classification Method for Power Load Data of New Energy Grid based on Improved OTSU Algorithm

Classification Method for Power Load Data of New Energy Grid based on Improved OTSU Algorithm

IRFSC: A high-efficiency hybrid model Image Retrieval Based on Feature Selection Using Continuous Learning Process

Multi-scale spatial and spectral feature fusion for soil carbon content prediction based on hyperspectral images

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