A Sensor Bias Correction Method for Reducing the Uncertainty in the Spatiotemporal Fusion of Remote Sensing Images

Zhang, Hongwei; Huang, Fang; Hong, Xiuchao; Wang, Ping

doi:10.3390/rs14143274

Cited by 5 publications

(4 citation statements)

References 53 publications

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“…Of course, there many other than Kalman filter methods such as the high-dimensional model representation technique [50], the machine learning (random forest regression (RFR)) technique [51], and other methods that are used for forecasting applications (e.g., weather forecasting) such as bias monitoring [52] and statistical model output units (MOS) [53], which have achieved remarkable results. The main advantage of the application of the Kalman filter in applications where parameter prediction is needed is that it can be easily implemented in IoT devices because it has a comparatively low demand for processing power.…”

Section: Discussionmentioning

confidence: 99%

A Kalman Filter Scheme for the Optimization of Low-Cost Gas Sensor Measurements

Christakis,

Tsakiridis,

Kandris

et al. 2023

Electronics

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Air pollution, which is mainly caused by industrialization, intensive transportation, and the heating of buildings, is one of the most important problems in large cities because it seriously harms the health and the quality of life of their citizens. This is why air quality is monitored not only by governmental organizations and official research institutions through the use of sophisticated monitoring systems but also by citizens through the use of low-cost air quality measurement devices. However, the reliability of the measurements derived from low-cost sensors is questionable, so the measurement errors must be eliminated. This study experimentally investigated the impact of the use of a Kalman filter on the accuracy of the measurements of low-cost air quality sensors. Specifically, measurements of air pollutant gases were carried out in the field in real ambient air conditions. This study demonstrates not only the optimization of the measurements through the application of a Kalman filter but also the behavior of the filter coefficients and their impact on the predicted values.

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Section: Discussionmentioning

confidence: 99%

A Kalman Filter Scheme for the Optimization of Low-Cost Gas Sensor Measurements

Christakis,

Tsakiridis,

Kandris

et al. 2023

Electronics

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“…When the window size is consistent with ESTARFM and STNLFFM, the residual error in predicting complex ground objects will be too large [33,35], resulting in a large error in the simulation results in the transition zone from forest and grass to cultivated land. Moreover, the FADSF fusion model based on a single high-and low-resolution image has the problem of insufficient robustness, when there are many outliers in the pixels [32,41,42], resulting in spectral distortion and texture blurring of FADSF-NDVI in the summer maize planting area from August to September. Since the ESTARFM fusion method requires two groups of data corresponding to high and low spatial resolution, the weight calculation will be affected and the time-phase relationship will be maintained with a large deviation.…”

Section: Discussionmentioning

confidence: 99%

“…The accumulation of NPP is higher, and the yield of summer maize is significantly higher than that in the northeast and northern area [11]. Affected by frequent precipitation in summer [38], the spatial heterogeneity in the semi-humid areas is enhanced, and downscaling (only with resampling in FSDAF) may lead to the same pixel values between adjacent pixels [31,42], resulting in an inaccurate FADSF-NDVI simulation, which leads to large errors in yield estimation. The STNLFFM and ESTARFM fusion models based on two high/low resolution images improve the accuracy of similar pixel screening [30,32], and alleviate the simulation errors caused with enhanced spatial heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau

He,

Yang,

et al. 2023

Agronomy

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Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response.

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“…With the development of satellite technology and the improvements in sensor technology, the demand for high spatial resolution is increasing. However, research on spatiotemporal fusion using high-resolution images is scarce; in particular, the accuracy of the high-resolution fusion images is unknown [28][29][30][31][32]. At the same time, there is no research on the fusion accuracy of different models in different land use types in the current spatiotemporal fusion research.…”

Section: Introductionmentioning

confidence: 99%

Applicability Analysis of GF-2PMS and PLANETSCOPE Data for Ground Object Recognition in Karst Region

Shen

Zhou

Yang

et al. 2022

Land

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Remote sensing image with high spatial and temporal resolution is very important for rational planning and scientific management of land resources. However, due to the influence of satellite resolution, revisit period, and cloud pollution, it is difficult to obtain high spatial and temporal resolution images. In order to effectively solve the “space–time contradiction” problem in remote sensing application, based on GF-2PMS (GF-2) and PlanetSope (PS) data, this paper compares and analyzes the applicability of FSDAF (flexible spatiotemporal data fusion), STDFA (the spatial temporal data fusion approach), and Fit_FC (regression model fitting, spatial filtering, and residual compensation) in different terrain conditions in karst area. The results show the following. (1) For the boundary area of water and land, the FSDAF model has the best fusion effect in land boundary recognition, and provides rich ground object information. The Fit_FC model is less effective, and the image is blurry. (2) For areas such as mountains, with large changes in vegetation coverage, the spatial resolution of the images fused by the three models is significantly improved. Among them, the STDFA model has the clearest and richest spatial structure information. The fused image of the Fit_FC model has the highest similarity with the verification image, which can better restore the coverage changes of crops and other vegetation, but the actual spatial resolution of the fused image is relatively poor, the image quality is fuzzy, and the land boundary area cannot be clearly identified. (3) For areas with dense buildings, such as cities, the fusion image of the FSDAF and STDFA models is clearer and the Fit_FC model can better reflect the changes in land use. In summary, compared with the Fit_FC model, the FSDAF model and the STDFA model have higher image prediction accuracy, especially in the recognition of building contours and other surface features, but they are not suitable for the dynamic monitoring of vegetation such as crops. At the same time, the image resolution of the Fit_FC model after fusion is slightly lower than that of the other two models. In particular, in the water–land boundary area, the fusion accuracy is poor, but the model of Fit_FC has unique advantages in vegetation dynamic monitoring. In this paper, three spatiotemporal fusion models are used to fuse GF-2 and PS images, which improves the recognition accuracy of surface objects and provides a new idea for fine classification of land use in karst areas.

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A Sensor Bias Correction Method for Reducing the Uncertainty in the Spatiotemporal Fusion of Remote Sensing Images

Cited by 5 publications

References 53 publications

A Kalman Filter Scheme for the Optimization of Low-Cost Gas Sensor Measurements

A Kalman Filter Scheme for the Optimization of Low-Cost Gas Sensor Measurements

Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau

Applicability Analysis of GF-2PMS and PLANETSCOPE Data for Ground Object Recognition in Karst Region

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