Principal component analysis (PCA) based hybrid models for the accurate estimation of reservoir water saturation

Asante-Okyere, Solomon; Shen, Chuanbo; Ziggah, Yao Yevenyo; Rulegeya, Mercy Moses; Zhu, Xiangfeng

doi:10.1016/j.cageo.2020.104555

Cited by 36 publications

(13 citation statements)

References 21 publications

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“…These linear combinations maximize the sample variance and make the new k features as uncorrelated as possible. On the basis of retaining the major feature components, the noise and unimportant features are reduced [33].…”

Section: Use Pca To Extract Main Feature Components Of Single-frequencymentioning

confidence: 99%

An Improved Method of Soil Moisture Retrieval Using Multi-Frequency SNR Data

et al. 2021

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Soil moisture monitoring using Global Navigation Satellite System (GNSS) multipath signals has gained continuous interests in recent years. However, traditional GNSS-interferometric reflectometry (GNSS-IR) soil moisture retrieval methods generally utilize a single frequency or single satellite, which fail to take full advantage of different and complementary of satellite signals with different frequencies. An improved algorithm for soil moisture retrieval based on principal component analysis (PCA) and entropy method using multi-frequency amplitude and phase offset fusion data was proposed in this research. The performance of the proposed soil moisture retrieval method was evaluated using data recorded by Plate Boundary Observatory (PBO) H2O networks and a self-built site in Henan, China. The results from GPS and BeiDou both showed that the retrieved soil moisture has a stronger correlation with in situ soil moisture, which can better reflect the fluctuation of ground truth measurements. Compared with the traditional method, the retrieval accuracy of the proposed method in terms of root-mean-square error (RMSE) was improved by 50.93%, and the average correlation coefficient were increased by 11.71%. This research proved that the proposed method could effectively improve retrieval accuracy due to the increasing number of frequencies and tracks clustering. Moreover, this study has illustrated the feasibility of BeiDou signals to precisely estimate surface soil moisture.

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Section: Use Pca To Extract Main Feature Components Of Single-frequencymentioning

confidence: 99%

An Improved Method of Soil Moisture Retrieval Using Multi-Frequency SNR Data

et al. 2021

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“…To solve this issue requires implementing a novel method for restricting the dimension and enhancing PCA entertainment after overall presentation creation. Based on PCA, the reconstruction error can be reduced [ 21 ]. When extending k-dimensional data to subspace, calculations are visible.…”

Section: Proposed Methodologymentioning

confidence: 99%

Objects and Action Detection of Human Faces through Thermal Images Using ANU-Net

Singothu

Chandana²

2022

Sensors

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Thermal cameras, as opposed to RBG cameras, work effectively in extremely low illumination situations and can record data outside of the human visual spectrum. For surveillance and security applications, thermal images have several benefits. However, due to the little visual information in thermal images and intrinsic similarity of facial heat maps, completing face identification tasks in the thermal realm is particularly difficult. It can be difficult to attempt identification across modalities, such as when trying to identify a face in thermal images using the ground truth database for the matching visible light domain or vice versa. We proposed a method for detecting objects and actions on thermal human face images, based on the classification of five different features (hat, glasses, rotation, normal, and hat with glasses) in this paper. This model is presented in five steps. To improve the results of feature extraction during the pre-processing step, initially, we resize the images and then convert them to grayscale level using a median filter. In addition, features are extracted from pre-processed images using principle component analysis (PCA). Furthermore, the horse herd optimization algorithm (HOA) is employed for feature selection. Then, to detect the human face in thermal images, the LeNet-5 method is used. It is utilized to detect objects and actions in face areas. Finally, we classify the objects and actions on faces using the ANU-Net approach with the Monarch butterfly optimization (MBO) algorithm to achieve higher classification accuracy. According to experiments using the Terravic Facial Infrared Database, the proposed method outperforms “state-of-the-art” methods for face recognition in thermal images. Additionally, the results for several facial recognition tasks demonstrate good precision.

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“…PCA is also used to explore similarities and hidden patterns [27] and detect changes in a dataset [28]. PCA has been applied in a wide range of applications in various fields ranging from biochemistry [29], tourism [30], geology [31], image processing [32], environment [33], and marine engineering [34]. PCA has been used to process raster data, as in the following studies [35], [36], [37].…”

Section: Data-driven Exploratory Analysismentioning

confidence: 99%

Data-driven Exploratory Analysis for Raster Data Using Self-Organizing Maps Regressor

Fajri¹,

Ranggadara²,

Suhendra³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Sugarcane is one of the plantation commodities in Indonesia which has a big potential. Sugarcane growth consists of 4 phases that happen in a year. In the Grand Growth phase, sugarcane needs an appropriate condition to grow well and enter the next phase. The factors that affect sugarcane's Grand Growth phase are water, temperature, and sunlight. Rainfall is one of the sugarcane water sources needed, but the rainfall intensity is different, and the rainfall distribution is uneven every year. The uneven rainfall caused water stress in a sugarcane plantation. That is why it is necessary to identify the water content in sugarcane plantations to maintain the quality of sugarcane. This study predicted the water content of sugarcane plantations so the areas indicated with water stress can be anticipated. Raster data are collected from Landsat-8 satellite imagery and analyzed using one of the data-driven exploration analysis methods, PCA (Principal Component Analysis), to analyze the overlay of the Landsat 8 imageries of the sugarcane plantation area. After that, the raster data were processed to calculate the water index of the sugarcane plantation, known as NDWI (Normalized Different Water Index). NDWI values of the sugarcane plantation area are converted into an array and then become data input for the Self-Organizing Map Regressor algorithm to predict the water content of the sugarcane plantation. The results are predicted water index values for the sugarcane plantation with 72% accuracy.

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Principal component analysis (PCA) based hybrid models for the accurate estimation of reservoir water saturation

Cited by 36 publications

References 21 publications

An Improved Method of Soil Moisture Retrieval Using Multi-Frequency SNR Data

An Improved Method of Soil Moisture Retrieval Using Multi-Frequency SNR Data

Objects and Action Detection of Human Faces through Thermal Images Using ANU-Net

Data-driven Exploratory Analysis for Raster Data Using Self-Organizing Maps Regressor

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