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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.
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 StyleYidong 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 StyleYidong 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.
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.
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 StyleXiaobo 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 StyleXiaobo 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.
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.
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 StyleYidong 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 StyleYidong 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.
Land surface temperature (LST) is a vital physical parameter in geoscience research and plays a prominent role in surface and atmosphere interaction. Due to technical restrictions, the spatiotemporal resolution of satellite remote sensing LST data is relatively low, which limits the potential applications of these data. An LST downscaling algorithm can effectively alleviate this problem and endow the LST data with more spatial details. Considering the spatial nonstationarity, downscaling algorithms have been gradually developed from least square models to geographical models. The current geographical LST downscaling models only consider the linear relationship between LST and auxiliary parameters, whereas non-linear relationships are neglected. Our study addressed this issue by proposing an LST downscaling algorithm based on a non-linear geographically weighted regressive (NL-GWR) model and selected the optimal combination of parameters to downscale the spatial resolution of a moderate resolution imaging spectroradiometer (MODIS) LST from 1000 m to 100 m. We selected Jinan city in north China and Wuhan city in south China from different seasons as study areas and used Landsat 8 images as reference data to verify the downscaling LST. The results indicated that the NL-GWR model performed well in all the study areas with lower root mean square error (RMSE) and mean absolute error (MAE), rather than the linear model.
Shumin Wang; Youming Luo; Xia Li; Kaixiang Yang; Qiang Liu; Xiaobo Luo; Xiuhong Li. Downscaling Land Surface Temperature Based on Non-Linear Geographically Weighted Regressive Model over Urban Areas. Remote Sensing 2021, 13, 1580 .
AMA StyleShumin Wang, Youming Luo, Xia Li, Kaixiang Yang, Qiang Liu, Xiaobo Luo, Xiuhong Li. Downscaling Land Surface Temperature Based on Non-Linear Geographically Weighted Regressive Model over Urban Areas. Remote Sensing. 2021; 13 (8):1580.
Chicago/Turabian StyleShumin Wang; Youming Luo; Xia Li; Kaixiang Yang; Qiang Liu; Xiaobo Luo; Xiuhong Li. 2021. "Downscaling Land Surface Temperature Based on Non-Linear Geographically Weighted Regressive Model over Urban Areas." Remote Sensing 13, no. 8: 1580.
Image super-resolution (SR) is a widely used and cost-effective technology in remote sensing image processing. Deep learning-based SR methods have shown promising performance, but they are prone to losing texture details. Instead, generative adversarial nets (GAN)-based methods can generate more visually acceptable results. However, GAN-based SR methods are suffering from scene variance and uncontrollable performance of discriminators as well as unstable training. Besides, both these two methods cannot yield arbitrary high-time SR images. To solve these issues, we propose a novel SR method for remote sensing images using Cascade Generative Adversarial Nets (CGAN) with introduction of content fidelity and scene constraint, which can achieve arbitrary high-time high-quality SR image. More specifically, the scene-constraint item is incorporated to constrain generated feature for avoiding the risk of scene change. Then, content fidelity is introduced to stabilize the training and avoid gradient vanishing. Besides, an edge enhancement module is designed to preserve edge detail and suppress the noise. CGAN with these three components has achieved higher quality SR results than other recent state-of-the-art methods. Compared with these methods, our proposed method outperformed average increments of 7.3% SSIM, 7.3% FSIM and 6.0% MSIM on WHU-RS19 and NWPU-RESISC45 datasets. In addition, the evaluation of GAN-train and GAN-test gained average increments of 6.3% and 4.5% on the WHU-RS19 and AID datasets, respectively.
Dongen Guo; Ying Xia; Liming Xu; Weisheng Li; Xiaobo Luo. Remote sensing image super-resolution using cascade generative adversarial nets. Neurocomputing 2021, 443, 117 -130.
AMA StyleDongen Guo, Ying Xia, Liming Xu, Weisheng Li, Xiaobo Luo. Remote sensing image super-resolution using cascade generative adversarial nets. Neurocomputing. 2021; 443 ():117-130.
Chicago/Turabian StyleDongen Guo; Ying Xia; Liming Xu; Weisheng Li; Xiaobo Luo. 2021. "Remote sensing image super-resolution using cascade generative adversarial nets." Neurocomputing 443, no. : 117-130.
With the development of supervised deep neural networks, classification performance on existing remote sensing scene datasets has been markedly improved. However, supervised learning methods rely heavily on large-scale tagged examples to obtain a better prediction performance. The lack of large-scale tagged remote sensing scene images has become the primary bottleneck in scene classification. To deal with this issue, a novel scene classification method using self-supervised gated self-attention generative adversarial networks (SGSAGANs) with similarity loss is proposed. Specifically, the gated self-attention module is first introduced into GANs to focus on key scene areas and filter useless information for strengthening feature representations. Then, the pyramidal convolution (PyConv) block is introduced into the residual block of the discriminator to capture different levels of details in the image using different types of filters with varying sizes and depths for enhancing the feature representations of the discriminator. Additionally, a novel similarity loss item is integrated into the discriminator to leverage self-supervised learning. Besides, spectral normalization is introduced into both the generative network and discriminative network to stabilize training and enhance feature representations. The architecture of multi-level feature fusion is integrated into the discriminative network to achieve more discriminant representation. Experimental results on the AID and NWPURESISC45 datasets show that the proposed method achieves the best performance compared to the existing unsupervised classification methods.
Dongen Guo; Ying Xia; Xiaobo Luo. Self-Supervised GANs With Similarity Loss for Remote Sensing Image Scene Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 2508 -2521.
AMA StyleDongen Guo, Ying Xia, Xiaobo Luo. Self-Supervised GANs With Similarity Loss for Remote Sensing Image Scene Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):2508-2521.
Chicago/Turabian StyleDongen Guo; Ying Xia; Xiaobo Luo. 2021. "Self-Supervised GANs With Similarity Loss for Remote Sensing Image Scene Classification." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 2508-2521.
Recently, methods of scene classification that are based on deep learning have become increasingly mature in remote sensing. However, training an excellent deep learning model for remote sensing scene classification requires a large number of labeled samples. Therefore, scene classification with insufficient scene images becomes a challenge. The deepEMD network is currently the most popular model for solving these tasks. Although the deepEMD network obtains impressive results on common few-shot baseline datasets, it is insufficient for capturing discriminative feature information about the scene from global and local perspectives. For this reason, an efficient few-shot scene classification scheme in remote sensing is proposed by combining multiple attention mechanisms and the attention-reference mechanism into the deepEMD network in this paper. First, scene features can be extracted by the backbone that incorporates global attention module and local attention module, which enables the backbone to capture discriminative information from both the global level and the local level. Second, the attention-reference mechanism generates the weights of elements in the earth mover’s distance (EMD) formulation, which can effectively alleviate the effects of complex background and intra-class morphological differences. The experimental results on three popular remote sensing benchmark datasets, Aerial Image Dataset (AID), OPTIMAL-31, and UC Merced, illustrate that our proposed scheme obtains state-of-the-art results in few-shot remote sensing scene classification.
Zhengwu Yuan; Wendong Huang; Lin Li; Xiaobo Luo. Few-Shot Scene Classification With Multi-Attention Deepemd Network in Remote Sensing. IEEE Access 2020, 9, 19891 -19901.
AMA StyleZhengwu Yuan, Wendong Huang, Lin Li, Xiaobo Luo. Few-Shot Scene Classification With Multi-Attention Deepemd Network in Remote Sensing. IEEE Access. 2020; 9 (99):19891-19901.
Chicago/Turabian StyleZhengwu Yuan; Wendong Huang; Lin Li; Xiaobo Luo. 2020. "Few-Shot Scene Classification With Multi-Attention Deepemd Network in Remote Sensing." IEEE Access 9, no. 99: 19891-19901.
With the advent of a large number of remote sensing images (RSIs), scene classification of RSI is widely applied to many fields such as urban planning, natural disaster detection, and environmental monitoring. Compared with the natural image field, the lack of labeled RSI is a bottleneck of supervised scene classification methods based on deep learning. Meanwhile, unsupervised scene classification is difficult to meet actual needs. To this end, we propose a novel semisupervised scene classification method for RSI using generative adversarial nets (GANs), in which a gating unit, a self-attention gating (SAG) module, and a pretrained Inception V3 branch are introduced into discriminative network to enhance the feature representation capability for facilitating semisupervised classification. To be specific, the gating unit aims to learn the weights of each feature map and capture the dependence relationship between features. The SAG module aims to capture a long-range dependence for adaptively focusing on important scene regions. The Inception V3 branch aims to extract the high-level semantic representation of input images and further enhance the discriminant ability by gating unit and SAG module. Furthermore, a new optimization term is incorporated into the generator loss to indirectly drive discriminator to correctly classify scene images. To verify the effectiveness of the proposed method, extensive experimental results on UC Merced and EuroSAT data sets demonstrate that the method surpasses most of the state-of-the-art semisupervised image classification methods significantly, especially when only few samples are tagged.
Dongen Guo; Ying Xia; Xiaobo Luo. GAN-Based Semisupervised Scene Classification of Remote Sensing Image. IEEE Geoscience and Remote Sensing Letters 2020, PP, 1 -5.
AMA StyleDongen Guo, Ying Xia, Xiaobo Luo. GAN-Based Semisupervised Scene Classification of Remote Sensing Image. IEEE Geoscience and Remote Sensing Letters. 2020; PP (99):1-5.
Chicago/Turabian StyleDongen Guo; Ying Xia; Xiaobo Luo. 2020. "GAN-Based Semisupervised Scene Classification of Remote Sensing Image." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
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.
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 StyleYidong 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 StyleYidong 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.
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°).
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 StyleShumin 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 StyleShumin 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.
Scene classification of high-resolution Remote Sensing Images (RSI) is one of basic challenges in RSI interpretation. Existing scene classification methods based on deep learning have achieved impressive performances. However, since RSI commonly contain various types of ground objects and complex backgrounds, most methods cannot focus on saliency features of scene, which limits the classification performances. To address this issue, we propose a novel Saliency Dual Attention Residual Network (SDAResNet) to extract both cross-channel and spatial saliency information for scene classification of RSI. More specifically, the proposed SDAResNet consists of spatial attention and channel attention, in which spatial attention is embedded into low-level feature to emphasize saliency location information and suppress background information, while channel attention is integrated into high-level features to extract saliency meaningful information. Additionally, several image classification tricks are used to further improve classification accuracy. Finally, extensive experiments on two challenging benchmark RSI datasets are presented to demonstrate that our methods outperform most state-of-the-art approaches significantly.
Dongen Guo; Ying Xia; Xiaobo Luo. Scene Classification of Remote Sensing Images Based on Saliency Dual Attention Residual Network. IEEE Access 2020, 8, 6344 -6357.
AMA StyleDongen Guo, Ying Xia, Xiaobo Luo. Scene Classification of Remote Sensing Images Based on Saliency Dual Attention Residual Network. IEEE Access. 2020; 8 (99):6344-6357.
Chicago/Turabian StyleDongen Guo; Ying Xia; Xiaobo Luo. 2020. "Scene Classification of Remote Sensing Images Based on Saliency Dual Attention Residual Network." IEEE Access 8, no. 99: 6344-6357.
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.
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 StyleXiaobo 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 StyleXiaobo 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.
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.
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 StyleXiaobo 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 StyleXiaobo 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.