Multi-sensor data fusion of remotely-sensed images with sparse and logarithmic low-rank regularization for shadow removal and denoising

Yang, Feixia; Ma, Fanyuan; Huo, Shuai; Wang, Yanwei

doi:10.1080/01431161.2021.1941388

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…The development of sensor technology has been relatively mature, and there are suitable sensor types for use in different environments, different types of data, and applications with different functions [20]. Most of the data acquired by the sensor is used to obtain the state of the system or to perceive the surrounding environment [21]. Nowadays, the mainstream environmental perception methods can be roughly divided into two types: imagebased information perception and wave-based information perception according to the working principle of the sensor [22,23].…”

Section: Introductionmentioning

confidence: 99%

The Application of Multisensor Information Fusion Technology in Environmental Restoration

Youhua

Guo

et al. 2022

Journal of Sensors

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Multisensor information fusion technology is an advanced processing method that is different from classical data processing technology. Capability and operational accuracy, in order to make an effective comprehensive evaluation of environmental quality, the environmental monitoring system built by NB-IoT technology and sensor technology is used to conduct multisensor data fusion research on the collected environmental factors such as temperature, humidity, formaldehyde, PM2.5, and TVOC. At the same time, the two-level parallel fusion method is adopted to evaluate the environmental quality. Before data fusion, median filtering is used to eliminate abnormal data. Then, the Kalman filter algorithm is used to fuse multiple sets of similar sensors to obtain the best value of it. Finally, the fuzzy comprehensive evaluation method is used to fuse the different types of sensors at the decision-making level, in which the weight value is determined by the entropy method and the membership function is Gaussian. The different environment scenarios are tested by using the above algorithms, and the simulation results show that multisensor data fusion can obtain more abundant and effective environmental information, overcome the simplicity and limitation of single-factor sensor for environmental quality assessment, and improve the reliability and accuracy of the overall environmental quality assessment.

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Section: Introductionmentioning

confidence: 99%

The Application of Multisensor Information Fusion Technology in Environmental Restoration

Youhua

Guo

et al. 2022

Journal of Sensors

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“…The Kenli District is located at the mouth of the Yellow River and forms a typical alluvial fan delta (Yang, Cai, et al, 2021; Yang, Lv, et al, 2021; Yang, Ma, et al, 2021; Yang, Yang, et al, 2021). It belongs to a continental monsoon climate zone, with obvious temperature differences in the four seasons, making it suitable for crop growing.…”

Section: Methodsmentioning

confidence: 99%

“…UAV technology is developing rapidly and is widely used, providing a new high‐precision remote sensing data sources with excellent properties (Ivushkin et al, 2017). Despite their inability to cover large areas, lightweight UAVs can collect multispectral data with centimeter‐to‐decimeter spatial resolution (Yang, Cai, et al, 2021; Yang, Lv, et al, 2021; Yang, Ma, et al, 2021; Yang, Yang, et al, 2021). They are more flexible than space‐borne sensors and can acquire data simultaneously with satellites (Alvarez et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The NMF method has strong advantages in fusing hyperspectral and multispectral data. It can properly select the dimension of feature space and obtain local and spatial details of original data to obtain fusion images with excellent spectral performance and detail retention, which has been widely applied in remote sensing data fusion (Chen et al, 2014; Yang, Cai, et al, 2021; Yang, Lv, et al, 2021; Yang, Ma, et al, 2021; Yang, Yang, et al, 2021). Differential coefficients can obtain different correction coefficients according to the differences between samples, so as to capture heterogeneous relations and achieve more accurate fusion (Madonsela et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning inversion models have been applied in several studies and were shown to be better than traditional statistical models (Lu et al, 2021; Zhou et al, 2020). Machine learning methods such as extreme gradient‐boosting (XGBoost) (Nguyen et al, 2021), extreme learning machine (ELM) (Guo et al, 2021), random forest (RF) (Tao et al, 2021), and CNN (Yang, Cai, et al, 2021; Yang, Lv, et al, 2021; Yang, Ma, et al, 2021; Yang, Yang, et al, 2021) show great potential in soil salinity monitoring. To improve the accuracy and reliability of the inversion model, the satellite‐UAV‐ground integrated method of monitoring soil salinity using machine learning models needs to be further explored.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Soil salinity inversion in coastal cotton growing areas: An integration method using satellite‐ground spectral fusion and satellite‐UAV collaboration

Chang

Yang

et al. 2022

Land Degrad Dev

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Soil salinity is a crucial factor in agriculture, rising salinity undermines cotton (Gossypium spp.) production in coastal areas of China and damages crops in other countries. In this study, we propose an effective integration method using satellite‐ground spectral fusion and satellite‐unmanned arial vehicle (UAV) collaboration for soil salinity monitoring in cotton growing areas. Firstly, an extreme learning machine (ELM), random forest (RF), and extreme gradient boosting (XGBoost) models were constructed based on UAV images from test areas. The optimal model was selected for soil salinity inversion. Meanwhile, ground imaging hyperspectrum and SENTINEL‐2A multispectral images were differentially fused by nonnegative matrix factorization (NMF). Then, taking the inversion results of UAV as the training sample to build convolutional neural network (CNN) model of the fused SENTINEL‐2A, the soil salinity distribution map of cotton fields in the study area was obtained by inversion, and the satellite‐UAV‐ground integrated inversion of soil salinity in coastal cotton fields was realized. The results showed that the spectrum after satellite‐ground fusion was closer to the original ground hyperspectrum, the fusion improved the correlation between spectrum and soil salinity, and UAV inversion data possessed great potential for the reference data of satellite inversion. The soil salinity obtained by satellite‐UAV‐ground integration approach was highly consistent with the measured salinity in the study area (R2 = 0.805), and the integration approach is suitable for soil salinity inversion in the cotton seedling stage in the coastal area. The satellite‐UAV‐ground integration approach proposed in this study fully tap the advantages of remote sensing data from different platforms and improved the ability to obtain soil salinisation information in large‐scale quantitatively, accurately, and quickly.

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Multispectral and hyperspectral images fusion based on subspace representation and nonlocal low-rank regularization

Yang,

Li,

et al. 2024

International Journal of Remote Sensing

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Multi-sensor data fusion of remotely-sensed images with sparse and logarithmic low-rank regularization for shadow removal and denoising

Cited by 6 publications

References 36 publications

The Application of Multisensor Information Fusion Technology in Environmental Restoration

The Application of Multisensor Information Fusion Technology in Environmental Restoration

Soil salinity inversion in coastal cotton growing areas: An integration method using satellite‐ground spectral fusion and satellite‐UAV collaboration

Multispectral and hyperspectral images fusion based on subspace representation and nonlocal low-rank regularization

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