Cloud enhancement of NOAA multispectral images by using independent component analysis and principal component analysis for sustainable systems

Venkatakrishnamoorthy, T.; Reddy, G. Umamaheswara

doi:10.1016/j.compeleceng.2019.01.005

Cited by 11 publications

(4 citation statements)

References 17 publications

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“…By referring to the relevant works of multispectral remote sensing image enhancement [30][31][32][33][34][35][36][37][38], five well-known evaluation indexes, including the contrast, image intensity, information entropy, average gradient, and execution time are used to evaluate the performance of different methods.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The intensity component of the IHS transform was replaced by the first principal component of the PCA transform, and the inverse IHS transform was applied to obtain an enhanced image. T. Venkatakrishnamoorthy et al [36] mainly expounded on a method based on spatial enhancement and spectral enhancement, which was applied to false-color-synthetic satellite cloud images. The algorithm was used in image processing after extracting useful features using independent component analysis (ICA) and PCA.…”

Section: Related Workmentioning

confidence: 99%

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Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

et al. 2022

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Compared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispectral image enhancement method based on the improved dark channel prior (IDCP) and bilateral fractional differential (BFD) model to make full use of the multiband information. First, the original multispectral image is inverted to meet the prior conditions of dark channel theory. Second, according to the characteristics of multiple bands, the dark channel algorithm is improved. The RGB channels are extended to multiple channels, and the spatial domain fractional differential mask is used to optimize the transmittance estimation to make it more consistent with the dark channel hypothesis. Then, we propose a bilateral fractional differentiation algorithm that enhances the edge details of an image through the fractional differential in the spatial domain and intensity domain. Finally, we implement the inversion operation to obtain the final enhanced image. We apply the proposed IDCP_BFD method to a multispectral dataset and conduct sufficient experiments. The experimental results show the superiority of the proposed method over relative comparison methods.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

et al. 2022

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“…The Moving robot sensors capturedimage have various resolutions, which collect data from the earth's surface of objects and then combine all the bands to form as multispectral or hyperspectral images, which collect data from across the electromagnetic spectrum [9] [10]. The primary focus of this imaging is to carry out pixel operations in the scene image in order to identify or categorize the object or detection process.…”

Section: Multispectral /Hyper Spectral Robot Captured Imagementioning

confidence: 99%

5G Enabled Moving Robot Captured Image Encryption with Principal Component Analysis Method

Moorthy,

Bachu,

Ramesh

et al. 2023

IJRITCC

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Estimating the captured image of moving robots is very difficult. These images are vital in analyzing earth's surface objects for many applications like studying environmental conditions, Land use and Land Cover changes, and change detection studies of worldwide change. Multispectral robot-captured images have a massive amount of low-resolution data, which is lost due to a lack of capture efficiency due to artificial and atmospheric reasons. The image transformation is required in a 5G network with effective transmission by reducing noise, inconsistent lighting, and low resolution, degrading image quality. In this paper, the authors proposed the machine learning dimensionality reduction technique i.e. Principle Component Analysis (PCA) and which is used for metastasizing the 5 G-enabled moving robot captured image to enrich the image's visual perception to analyze the exact information of global or local data. The encryption algorithm implanted for data reduction and transmission over the 5G network gives sophisticated results compared with other standard methods. This proposed algorithm gives better performance in developing data reduction, network convergence speed, reduces the training time for object classification, and improves accuracy for multispectral moving robot-captured images by the support of 5G network.

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“…(1) According to the measurement range information of each measurement point, remove the measurement point data which obviously deviates from the measurement range (2) Combined with the actual operation experience of the power plant, remove the data which obviously deviates from the experience value under the current working condition At the same time, in order to shorten the time of the data processing and remove redundant information from the monitoring data, this paper proposes a feature extraction method based on the latent structure model for the monitoring data. Two common methods, principal component analysis (PCA) [29,30] and independent component analysis (ICA) [31,32], are used in this latent structure model.…”

Section: Databasementioning

confidence: 99%

The Bidirectional Information Fusion Using an Improved LSTM Model

Zheng

Wang

Guo

et al. 2021

Mobile Information Systems

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The information fusion technology is of great significance in intelligent systems. At present, the modern coal-fired power plant has the fully functional sensor network. However, many data that are important for the operation of a power plant, such as the coal quality, cannot be directly obtained. Therefore, the information fusion technology needs to be introduced to obtain the implied information of the power plant. As a practical application, the soft measurement of coal quality is taken as the research object. This paper proposes an improved LSTM model combined with the bidirectional deep fusion, alertness mechanism, and parameter self-learning (DFAS-LSTM) to realize online soft computing for the coal quality analyses of industries and elements. First, a latent structure model is established to preprocess the noisy and redundant sensor network data. Second, an alertness mechanism is proposed and the self-learning method of the activation function parameters is used for the data feature extraction. Third, a deeply bidirectional fusion layer is added to the long short-term memory neural network model to solve the problem of the insufficient accuracy and the weak generalization. Using the historical data of the sensor network, the DFAS-LSTM model is established. Then, the online data of the sensor network is input to the DFAS-LSTM model to implement the online coal quality analyses. Experiment shows that the accuracy of the coal quality analyses is increased by 1%–2.42% compared to the traditionally bidirectional LSTM.

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Cloud enhancement of NOAA multispectral images by using independent component analysis and principal component analysis for sustainable systems

Cited by 11 publications

References 17 publications

Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

5G Enabled Moving Robot Captured Image Encryption with Principal Component Analysis Method

The Bidirectional Information Fusion Using an Improved LSTM Model

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