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Yidong Peng
Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

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Journal article
Published: 02 July 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Tradeoffs between the spatial and temporal resolutions of current satellite instruments limit our ability to conduct high-quality and continuous monitoring of the earth's surface dynamics. Spatiotemporal image fusion has become increasingly necessary to obtain remote sensing images with high spatiotemporal resolution. However, current learning-based methods concentrate on predicting images only from spatial similarity and neglect spectral correlations of remote sensing images, leading to significant spectral information loss. In this article, we develop a novel nonlocal tensor sparse representation-based semicoupled dictionary learning approach (SCDNTSR) for spatiotemporal fusion. In the SCDNTSR method, the spectral correlation and the spatial similarity of the nonlocal similar cubes are simultaneously exploited through the tensor-tensor product-based tensor sparse representation. Furthermore, the semicoupled mapping prior knowledge of sparse coefficients across the high- and low-spatial resolution (HSR\backslashLSR) image spaces is exploited with the coupled dictionary to constrain the similarity of sparse coefficients to improve the prediction performance. In addition, to capture additional prior spatial information, the SCDNTSR provides a new method to determine the degradation relationship between the target HSR and LSR difference images with the help of the known HSR and LSR difference images. The proposed SCDNTSR method was tested on real datasets at both the Coleambally Irrigation Area study site and the Lower Gwydir Catchment study site. Results show that the proposed method outperforms five state-of-the-art methods, especially in maintaining the spectral information, proving the feasibility of integrating the degradation relationship, spatio-spectral-nonlocal correlation, and semicoupled mapping priors of the multisource data into the proposed model.

ACS Style

Yidong Peng; Weisheng Li; Xiaobo Luo; Jiao Du; Xiayan Zhang; Yi Gan; Xinbo Gao. Spatiotemporal Reflectance Fusion via Tensor Sparse Representation. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -18.

AMA Style

Yidong Peng, Weisheng Li, Xiaobo Luo, Jiao Du, Xiayan Zhang, Yi Gan, Xinbo Gao. Spatiotemporal Reflectance Fusion via Tensor Sparse Representation. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-18.

Chicago/Turabian Style

Yidong Peng; Weisheng Li; Xiaobo Luo; Jiao Du; Xiayan Zhang; Yi Gan; Xinbo Gao. 2021. "Spatiotemporal Reflectance Fusion via Tensor Sparse Representation." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-18.

Journal article
Published: 01 July 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Land surface temperature (LST) is a key parameter in numerous environmental studies. However, currently, there is no satellite sensor that can completely provide LST data with both high spatial and high temporal resolutions simultaneously. LST downscaling is regarded as an effective remedy for improving the temporal and spatial resolutions of LST data. In this study, a geographically and temporally weighted autoregressive (GTWAR) model of LST downscaling is that comprehensively considers the spatial heterogeneity, spatial autoregression and temporality of LST is newly proposed. The normalized difference water index (NDWI), the normalized difference built-up index (NDBI), and the normalized difference vegetation index (NDVI) were selected as explanatory variables to downscale the moderate resolution imaging spectroradiometer (MODIS) LST from 1000 m to 100 m, while the Landsat 8 LST was selected as the reference data. Compared with the thermal data sharpening (TsHARP), the geographically weighted regression (GWR), the geographically weighted autoregressive (GWAR) and the geographically and temporally weighted regression (GTWR) downscaling methods, the proposed method was superior based on quantitative indices, with the lowest root mean square error (RMSE) (Zhangye: 1.57, Beijing: 1.22) and mean absolute error (MAE) (Zhangye: 1.06, Beijing: 0.85). The downscaling model of GTWAR will facilitate improvements in the accuracy of downscaling for temporal series of LST data.

ACS Style

Xiaobo Luo; Yuan Chen; Zhi Wang; Hua Li; Yidong Peng. Spatial Downscaling of MODIS Land Surface Temperature Based on a Geographically and Temporally Weighted Autoregressive Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.

AMA Style

Xiaobo Luo, Yuan Chen, Zhi Wang, Hua Li, Yidong Peng. Spatial Downscaling of MODIS Land Surface Temperature Based on a Geographically and Temporally Weighted Autoregressive Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.

Chicago/Turabian Style

Xiaobo Luo; Yuan Chen; Zhi Wang; Hua Li; Yidong Peng. 2021. "Spatial Downscaling of MODIS Land Surface Temperature Based on a Geographically and Temporally Weighted Autoregressive Model." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.

Journal article
Published: 28 April 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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This study presents a novel global gradient sparse and nonlocal low-rank tensor decomposition model with a hyper-Laplacian prior for hyperspectral image (HSI) super-resolution to produce a high-resolution HSI (HR-HSI) by fusing a low-resolution HSI (LR-HSI) with an HR multispectral image (HR-MSI). Inspired by the investigated hyper-Laplacian distribution of the gradients of the difference images between the upsampled LR-HSI and latent HR-HSI, we formulate the relationship between these two data sets as a p (0 < p < 1)-norm term to enforce spectral preservation. Then, the relationship between the HR-MSI and latent HR-HSI is built using a tensor-based fidelity term to recover the spatial details. To effectively capture the high spatio-spectral-nonlocal similarities of the latent HR-HSI, we design a novel nonlocal low-rank Tucker decomposition to model the three-dimensional regular tensors constructed from the grouped nonlocal similar HR-HSI cubes. The global spatial-spectral total variation regularization is then adopted to ensure the global spatial piecewise smoothness and spectral consistency of the reconstructed HR-HSI from nonlocal low-rank cubes. Finally, an alternating direction method of multipliers-based algorithm is designed to efficiently solve the optimization problem. Experiments on both the synthetic and real data sets collected by different sensors show the effectiveness of the proposed method, from visual and quantitative assessments.

ACS Style

Yidong Peng; Weisheng Li; Xiaobo Luo; Jiao Du. Hyperspectral Image Superresolution Using Global Gradient Sparse and Nonlocal Low-Rank Tensor Decomposition With Hyper-Laplacian Prior. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 5453 -5469.

AMA Style

Yidong Peng, Weisheng Li, Xiaobo Luo, Jiao Du. Hyperspectral Image Superresolution Using Global Gradient Sparse and Nonlocal Low-Rank Tensor Decomposition With Hyper-Laplacian Prior. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):5453-5469.

Chicago/Turabian Style

Yidong Peng; Weisheng Li; Xiaobo Luo; Jiao Du. 2021. "Hyperspectral Image Superresolution Using Global Gradient Sparse and Nonlocal Low-Rank Tensor Decomposition With Hyper-Laplacian Prior." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 5453-5469.

Research article
Published: 29 December 2020 in International Journal of Remote Sensing
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In this paper, we propose a new spatiotemporal fusion method based on a convolutional neural network to which we added attention and multiscale mechanisms (AMNet). Different from the previous spatiotemporal fusion methods, the residual image obtained by subtracting moderate resolution imaging spectroradiometer (MODIS) images at two times is directly used to train the network, and two special structures of multiscale mechanism and attention mechanism are used to increase the accuracy of fusion. Our proposed method uses one pair of images to achieve spatiotemporal fusion. The work is mainly divided into three steps. The first step is to extract feature maps of two types of images at different scales and fuse them separately. The second step is to use the attention mechanism to focus on the important information in the feature maps. And the third step is to reconstruct the image. We used two classical datasets for the experiment, and compared our experimental results with the other three state-of-the-art spatiotemporal fusion methods. The results of our proposed method have richer spatial details and more accurate prediction of temporal changes.

ACS Style

Weisheng Li; Xiayan Zhang; Yidong Peng; Meilin Dong. Spatiotemporal Fusion of Remote Sensing Images using a Convolutional Neural Network with Attention and Multiscale Mechanisms. International Journal of Remote Sensing 2020, 42, 1973 -1993.

AMA Style

Weisheng Li, Xiayan Zhang, Yidong Peng, Meilin Dong. Spatiotemporal Fusion of Remote Sensing Images using a Convolutional Neural Network with Attention and Multiscale Mechanisms. International Journal of Remote Sensing. 2020; 42 (6):1973-1993.

Chicago/Turabian Style

Weisheng Li; Xiayan Zhang; Yidong Peng; Meilin Dong. 2020. "Spatiotemporal Fusion of Remote Sensing Images using a Convolutional Neural Network with Attention and Multiscale Mechanisms." International Journal of Remote Sensing 42, no. 6: 1973-1993.

Journal article
Published: 15 October 2020 in IEEE Sensors Journal
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ACS Style

Weisheng Li; Xiayan Zhang; Yidong Peng; Meilin Dong. DMNet: A Network Architecture Using Dilated Convolution and Multiscale Mechanisms for Spatiotemporal Fusion of Remote Sensing Images. IEEE Sensors Journal 2020, 20, 12190 -12202.

AMA Style

Weisheng Li, Xiayan Zhang, Yidong Peng, Meilin Dong. DMNet: A Network Architecture Using Dilated Convolution and Multiscale Mechanisms for Spatiotemporal Fusion of Remote Sensing Images. IEEE Sensors Journal. 2020; 20 (20):12190-12202.

Chicago/Turabian Style

Weisheng Li; Xiayan Zhang; Yidong Peng; Meilin Dong. 2020. "DMNet: A Network Architecture Using Dilated Convolution and Multiscale Mechanisms for Spatiotemporal Fusion of Remote Sensing Images." IEEE Sensors Journal 20, no. 20: 12190-12202.

Journal article
Published: 11 August 2020 in Information Fusion
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Remote sensing image fusion is considered a cost effective method for handling the tradeoff between the spatial, temporal and spectral resolutions of current satellite systems. However, most current fusion methods concentrate on fusing images in two domains among the spatial, temporal and spectral domains, and a few efforts have been made to comprehensively explore the relationships of spatio-temporal–spectral features. In this study, we propose a novel integrated spatio-temporal–spectral fusion framework based on semicoupled sparse tensor factorization to generate synthesized frequent high-spectral and high-spatial resolution images by blending multisource observations. Specifically, the proposed method regards the desired high spatio-temporal–spectral resolution images as a four-dimensional tensor and formulates the integrated fusion problem as the estimation of the core tensor and the dictionary along each mode. The high-spectral correlation across the spectral domain and the high self-similarity (redundancy) features in the spatial and temporal domains are jointly exploited using the low dimensional and sparse core tensors. In addition, assuming that the sparse coefficients in the core tensors across the observed and desired image spaces are not strictly the same, we formulate the estimation of the core tensor and the dictionaries as a semicoupled sparse tensor factorization of available heterogeneous spatial, spectral and temporal remote sensing observations. Finally, the proposed method can exploit the multicomplementary spatial, temporal and spectral information of any combination of remote sensing data based on this single unified model. Experiments on multiple data types, including spatio-spectral, spatio-temporal, and spatio-temporal–spectral data fusion, demonstrate the effectiveness and efficiency of the proposed method.

ACS Style

Yidong Peng; Weisheng Li; Xiaobo Luo; Jiao Du; Yi Gan; Xinbo Gao. Integrated fusion framework based on semicoupled sparse tensor factorization for spatio-temporal–spectral fusion of remote sensing images. Information Fusion 2020, 65, 21 -36.

AMA Style

Yidong Peng, Weisheng Li, Xiaobo Luo, Jiao Du, Yi Gan, Xinbo Gao. Integrated fusion framework based on semicoupled sparse tensor factorization for spatio-temporal–spectral fusion of remote sensing images. Information Fusion. 2020; 65 ():21-36.

Chicago/Turabian Style

Yidong Peng; Weisheng Li; Xiaobo Luo; Jiao Du; Yi Gan; Xinbo Gao. 2020. "Integrated fusion framework based on semicoupled sparse tensor factorization for spatio-temporal–spectral fusion of remote sensing images." Information Fusion 65, no. : 21-36.

Journal article
Published: 22 May 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Land surface temperature (LST) is a key parameter in numerous thermal environmental studies. Due to technical constraints, satellite thermal sensors are unable to supply thermal infrared images with simultaneous high spatial and temporal resolution. LST downscaling algorithms can alleviate this problem and improve the spatiotemporal resolution of LST data. Spatial nonstationary and spatial autocorrelation coexist in most spatial variables. The spatial characteristics of the LST should be fully considered as a spatial variable in the downscaling process. However, previous studies on LST downscaling considered only spatial nonstationary, and spatial autocorrelation was neglected. In this paper, we propose a new algorithm based on the geographically weighted autoregressive (GWAR) model for LST spatial downscaling. The digital elevation model (DEM) and normalized difference build-up index (NDBI) were chosen as explanatory variables to downscale the spatial resolution of the moderate resolution imaging spectroradiometer (MODIS) LST data from 1000 to 100 m, and Lanzhou and Beijing were taken as the study areas. The performance of the GWAR model was compared with that of the thermal data sharpening (TsHARP) model and the geographically weighted regression (GWR) model. The Landsat 8 LST was used to verify the downscaled LST. The results indicate that the GWAR-based algorithm outperforms the TsHARP and GWR-based algorithms with lower root mean square error (RMSE) (Beijing: 1.37°, Lanzhou: 1.76°) and mean absolute error (MAE) (Beijing: 0.86°, Lanzhou: 1.33°).

ACS Style

Shumin Wang; Xiaobo Luo; Yidong Peng. Spatial Downscaling of MODIS Land Surface Temperature Based on Geographically Weighted Autoregressive Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 2532 -2546.

AMA Style

Shumin Wang, Xiaobo Luo, Yidong Peng. Spatial Downscaling of MODIS Land Surface Temperature Based on Geographically Weighted Autoregressive Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):2532-2546.

Chicago/Turabian Style

Shumin Wang; Xiaobo Luo; Yidong Peng. 2020. "Spatial Downscaling of MODIS Land Surface Temperature Based on Geographically Weighted Autoregressive Model." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 2532-2546.

Research article
Published: 20 January 2017 in Journal of the Indian Society of Remote Sensing
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For purpose of improving the accuracy of the built-up quick mapping, this paper proposed an improved optimal segmentation threshold algorithm, namely the improved double-window flexible pace search (IDFPS) approach, by redesigning the valuation criteria and the sampling method based on the double-window flexible pace search (DFPS) approach. Moreover, the Normalized Difference Built-up Index (NDBI), the Index-based Built-up Index (IBI), the Enhanced Built-up and Bareness Index (EBBI) and the Urban Index (UI) inversed from Landsat 5 TM images were used for quick mapping by the IDFPS approach and the DFPS approach in different geographical areas. Results from the experiments exemplified by Chongqing (a mountain city) and Chengdu (a plain city) showed that the IDFPS approach was comprehensively superior to the DFPS approach. The IDFPS approach had more than 4.30% higher overall accuracy and 0.12 higher Kappa coefficients than the DFPS approach when both were implemented simultaneously at both the above-mentioned study areas. Besides, a new discovery in this paper was found that the UI had a better performance with higher overall accuracy and Kappa coefficient, lower omission error and commission error than the NDBI, IBI and EBBI because of the strong relationship between the UI and the density of built-up land. This new method has an important reference value for built-up quick mapping and some other applied researches.

ACS Style

Xiaobo Luo; Yidong Peng; Yanghua Gao. An Improved Optimal Segmentation Threshold Algorithm and Its Application in the Built-up Quick Mapping. Journal of the Indian Society of Remote Sensing 2017, 45, 953 -964.

AMA Style

Xiaobo Luo, Yidong Peng, Yanghua Gao. An Improved Optimal Segmentation Threshold Algorithm and Its Application in the Built-up Quick Mapping. Journal of the Indian Society of Remote Sensing. 2017; 45 (6):953-964.

Chicago/Turabian Style

Xiaobo Luo; Yidong Peng; Yanghua Gao. 2017. "An Improved Optimal Segmentation Threshold Algorithm and Its Application in the Built-up Quick Mapping." Journal of the Indian Society of Remote Sensing 45, no. 6: 953-964.

Journal article
Published: 15 September 2016 in Remote Sensing
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Urban heat island (UHI) effect, the side effect of rapid urbanization, has become an obstacle to the further healthy development of the city. Understanding its relationships with impact factors is important to provide useful information for climate adaptation urban planning strategies. For this purpose, the geographically-weighted regression (GWR) approach is used to explore the scale effects in a mountainous city, namely the change laws and characteristics of the relationships between land surface temperature and impact factors at different spatial resolutions (30–960 m). The impact factors include the Soil-adjusted Vegetation Index (SAVI), the Index-based Built-up Index (IBI), and the Soil Brightness Index (NDSI), which indicate the coverage of the vegetation, built-up, and bare land, respectively. For reference, the ordinary least squares (OLS) model, a global regression technique, is also employed by using the same dependent variable and explanatory variables as in the GWR model. Results from the experiment exemplified by Chongqing showed that the GWR approach had a better prediction accuracy and a better ability to describe spatial non-stationarity than the OLS approach judged by the analysis of the local coefficient of determination (R2), Corrected Akaike Information Criterion (AICc), and F-test at small spatial resolution (< 240 m); however, when the spatial scale was increased to 480 m, this advantage has become relatively weak. This indicates that the GWR model becomes increasingly global, revealing the relationships with more generalized geographical patterns, and then spatial non-stationarity in the relationship will tend to be neglected with the increase of spatial resolution.

ACS Style

Xiaobo Luo; Yidong Peng. Scale Effects of the Relationships between Urban Heat Islands and Impact Factors Based on a Geographically-Weighted Regression Model. Remote Sensing 2016, 8, 760 .

AMA Style

Xiaobo Luo, Yidong Peng. Scale Effects of the Relationships between Urban Heat Islands and Impact Factors Based on a Geographically-Weighted Regression Model. Remote Sensing. 2016; 8 (9):760.

Chicago/Turabian Style

Xiaobo Luo; Yidong Peng. 2016. "Scale Effects of the Relationships between Urban Heat Islands and Impact Factors Based on a Geographically-Weighted Regression Model." Remote Sensing 8, no. 9: 760.