A Machine Learning approach to reconstruct cloudy affected vegetation indices imagery via data fusion from Sentinel-1 and Landsat 8

Santos, Erli Pinto dos; Silva, Demétrius David da; Amaral, Cibele Hummel do; Filho, Elpídio Inácio Fernandes; Dias, Rafael Luís Silva

doi:10.1016/j.compag.2022.106753

Cited by 23 publications

(13 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the arithmetic mean is the simplest way to merge data. Because estimators and classifiers perform differently, this method is not applicable [ 7 – 9 ].…”

Section: Methodsmentioning

confidence: 99%

“…A diagnosis based on data fusion is an exciting application of the Industrial Internet of Things for the efficient use of motor monitoring data. A multimodal neural network (DRMNN) based on dynamic routing was introduced by Wang et al to follow the concept of deep multimodal learning (MDL) [ 8 , 9 ]. They proposed a strategy for dimensionality reduction and invariant feature capture using vibration and stator current signals to extract multimodal features from multisource data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Method for Predicting Production Costs Based on Data Fusion from Multiple Sources for Industry 4.0: Trends and Applications of Machine Learning Methods

Soleimani,

Naderian,

Afshinfar

et al. 2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

There is a growing need for manufacturing processes that improve product quality and production rates while reducing costs. With the advent of multisensory information fusion technology, individuals can acquire a broader range of information. Several data fusion and machine learning methods have been discussed in this article within the context of the Industry 4.0 paradigm. Depending on its purpose, a prognostic method can be categorized as descriptive, predictive, or prescriptive. ANN and CNN models are applied to predicting production costs using neural networks based on multisource information fusion, and multisource information fusion theory is examined and applied to ANNs and CNNs. In this study, ANN and CNN predictions have been compared. CNN has demonstrated more remarkable skill in predicting the six cost categories than ANN. When predicting the true value of each cost category, CNN is superior to ANN. As a result, CNN’s forecast error for the current month’s total income is 0.0234. Because of its improved prediction accuracy and more straightforward training technique, CNN is better suited to incorporating information from several sources. Furthermore, both neural networks overestimate indirect costs, including direct material costs and item consumption prices.

show abstract

“…Using the arithmetic mean is the simplest way to merge data. Because estimators and classifiers perform differently, this method is not applicable [ 7 – 9 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method for Predicting Production Costs Based on Data Fusion from Multiple Sources for Industry 4.0: Trends and Applications of Machine Learning Methods

Soleimani,

Naderian,

Afshinfar

et al. 2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Land 2024, 13, x FOR PEER REVIEW 3 of 26 resolution imagery unaffected by weather conditions, are extensively utilized for land cover classification and crop mapping [35,36]. However, research focusing on extracting abandoned land based on Sentinel-1 and Sentinel-2 images remains limited.…”

Section: Study Areamentioning

confidence: 99%

“…Sentinel-2 images, renowned for their high spatial and temporal resolution, have been widely used for land use and cover change analysis. Similarly, Sentinel-1 images, providing high-resolution imagery unaffected by weather conditions, are extensively utilized for land cover classification and crop mapping [35,36]. However, research focusing on extracting abandoned land based on Sentinel-1 and Sentinel-2 images remains limited.…”

Section: Introductionmentioning

confidence: 99%

Mapping Cropland Abandonment in the Cloudy Hilly Regions Surrounding the Southwest Basin of China

Wei,

Wen,

Zhou

et al. 2024

Land

View full text Add to dashboard Cite

Cropland is a vital resource intricately connected to food security. Currently, the issue of cropland abandonment poses a serious threat to food production and supply, presenting a significant challenge to rural economies and the stability of the food supply chain. The hilly and cloudy regions of southwest China are particularly affected by cropland abandonment, presenting significant challenges in accurately mapping the distribution of abandoned cropland due to fragmentation and heavy cloud pollution. Therefore, this study focuses on Mingshan County, located in Ya’an City, Sichuan Province, China, as the study area. Utilizing Google Earth Engine (GEE) and a random forest algorithm, a method integrating multi-source data from Landsat 8, Sentinel-2, and Sentinel-1 is proposed to extract abandoned cropland spanning from 2018 to 2022. This study analyzes spatial and temporal characteristics, employing the Geodetector with optimal parameters to explore the underlying mechanisms. The findings reveal the following: (1) The method achieves an overall accuracy of land use classification surpassing 88.67%, with a Kappa coefficient exceeding 0.87. Specifically, the accuracy for identifying abandoned cropland reaches 87.00%. (2) From 2018 to 2022, the abandonment rate in Mingshan County fluctuated between 4.58% and 5.77%, averaging 5.03%. The lowest abandonment rate occurred in 2019–2020, while the highest was observed in 2020–2021. (3) Cropland abandonment is influenced by both natural and social factors. Elevation and slope are the main driving factors, alongside factors such as distance to road, town, and residential settlement that all significantly contribute to abandonment trends. These five factors exhibit positive correlation with the abandonment rate, with distance to the river showing relatively weaker explanatory power.

show abstract

“…The data is an image that has been subjected to GRD boundary noise and thermal noise elimination, radiometric calibration, and topographic correction (orthorectification). Based on the above preprocessed Sentinel-1 data, this study performs outlier filtering and speckle noise filtering [14], and uses different polarization methods (VV, VH) to construct backscattering coefficient characteristics; considering that mangroves grow in the intertidal zone and the terrain is low, this paper uses SRTM 30 m resolution DEM data mask to study the area with elevation greater than 23 m and slope greater than 20 °. The data set is shown in Table 1.…”

Section: Data Source and Preprocessingmentioning

confidence: 99%

Study on mangrove extraction in Beibu Gulf of Guangxi considering phenological characteristics

Li,

Chen

2023

Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023)

View full text Add to dashboard Cite

A Machine Learning approach to reconstruct cloudy affected vegetation indices imagery via data fusion from Sentinel-1 and Landsat 8

Cited by 23 publications

References 38 publications

A Method for Predicting Production Costs Based on Data Fusion from Multiple Sources for Industry 4.0: Trends and Applications of Machine Learning Methods

A Method for Predicting Production Costs Based on Data Fusion from Multiple Sources for Industry 4.0: Trends and Applications of Machine Learning Methods

Mapping Cropland Abandonment in the Cloudy Hilly Regions Surrounding the Southwest Basin of China

Study on mangrove extraction in Beibu Gulf of Guangxi considering phenological characteristics

Contact Info

Product

Resources

About