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Xue Wang
Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China, and also with the Key Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Mining and Technology, Xuzhou 221116, China

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Journal article
Published: 18 August 2020 in IEEE Transactions on Geoscience and Remote Sensing
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The visible-shortwave infrared Advanced Hyperspectral Imager (AHSI) is a payload onboard the Gaofen-5 satellite, which is China's first hyperspectral satellite and is part of the Chinese High-Resolution Earth Observation System. As a supplement to the onboard radiometric calibration of the AHSI instrument, vicarious calibration is also required, which is independent of the instrument-based calibration. In this article, a reflectance-based vicarious calibration approach is presented, which takes surface reflectance data, aerosol data, and atmospheric water vapor data into account. The Dunhuang test site, which is one of the China Radiometric Calibration Sites (CRCS) for the vicarious calibration of spaceborne sensors, possesses stable, uniform, and measurable surface objects, so it was chosen as the radiation source to replace the laboratory and onboard calibrators. A Spectra Vista Corporation (SVC) spectral radiometer and a CE318 sun photometer were utilized for the measurement of the surface reflectance and the condition of the aerosol, respectively. The radiance at the entrance pupil at the top of atmosphere was then obtained through the MODerate resolution atmospheric TRANsmission (MODTRAN) atmospheric transmission model. The surface reflectance was obtained using the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) atmospheric model for validation. The results show that, with regard to the calibration coefficients, the calibrated AHSI instrument presents a stable radiometric performance among different land-cover types. The ratios on all the bands are between 0.8 and 1.2 and are consistent with the reflectance data from the Dunhuang test site. The R² values are all greater than 0.95 and the spectral angle is all less than 2°. The standard deviations of the ratios are less than 3% for each chosen band, which proves that the calibrated data have a high consistency with the in situ measurements. When compared with Landsat 8 and Sentinel-2, the mean errors of the surface reflectance are all under 0.06, which further demonstrates that the calibrated reflectance has a high accuracy.

ACS Style

Kun Tan; Xue Wang; Chao Niu; Feng Wang; Peijun Du; De-Xin Sun; Juan Yuan; Jing Zhang. Vicarious Calibration for the AHSI Instrument of Gaofen-5 With Reference to the CRCS Dunhuang Test Site. IEEE Transactions on Geoscience and Remote Sensing 2020, PP, 1 -11.

AMA Style

Kun Tan, Xue Wang, Chao Niu, Feng Wang, Peijun Du, De-Xin Sun, Juan Yuan, Jing Zhang. Vicarious Calibration for the AHSI Instrument of Gaofen-5 With Reference to the CRCS Dunhuang Test Site. IEEE Transactions on Geoscience and Remote Sensing. 2020; PP (99):1-11.

Chicago/Turabian Style

Kun Tan; Xue Wang; Chao Niu; Feng Wang; Peijun Du; De-Xin Sun; Juan Yuan; Jing Zhang. 2020. "Vicarious Calibration for the AHSI Instrument of Gaofen-5 With Reference to the CRCS Dunhuang Test Site." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-11.

Journal article
Published: 10 February 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Deep generative models such as the generative adversarial network (GAN) and the variational autoencoder (VAE) have obtained increasing attention in a wide variety of applications. Nevertheless, the existing methods cannot fully consider the inherent features of the spectral information, which leads to the applications being of low practical performance. In this article, in order to better handle this problem, a novel generative model named the conditional variational autoencoder with an adversarial training process (CVA²E) is proposed for hyperspectral imagery classification by combining variational inference and an adversarial training process in the spectral sample generation. Moreover, two penalty terms are added to promote the diversity and optimize the spectral shape features of the generated samples. The performance on three different real hyperspectral data sets confirms the superiority of the proposed method.

ACS Style

Xue Wang; Kun Tan; Qian Du; Yu Chen; Peijun Du. CVA2E: A Conditional Variational Autoencoder With an Adversarial Training Process for Hyperspectral Imagery Classification. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 5676 -5692.

AMA Style

Xue Wang, Kun Tan, Qian Du, Yu Chen, Peijun Du. CVA2E: A Conditional Variational Autoencoder With an Adversarial Training Process for Hyperspectral Imagery Classification. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (8):5676-5692.

Chicago/Turabian Style

Xue Wang; Kun Tan; Qian Du; Yu Chen; Peijun Du. 2020. "CVA2E: A Conditional Variational Autoencoder With an Adversarial Training Process for Hyperspectral Imagery Classification." IEEE Transactions on Geoscience and Remote Sensing 58, no. 8: 5676-5692.

Journal article
Published: 07 January 2020 in Remote Sensing
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Information extraction from multi-sensor remote sensing images has increasingly attracted attention with the development of remote sensing sensors. In this study, a supervised change detection method, based on the deep Siamese convolutional network with hybrid convolutional feature extraction module (OB-DSCNH), has been proposed using multi-sensor images. The proposed architecture, which is based on dilated convolution, can extract the deep change features effectively, and the character of “network in network” increases the depth and width of the network while keeping the computational budget constant. The change decision model is utilized to detect changes through the difference of extracted features. Finally, a change detection map is obtained via an uncertainty analysis, which combines the multi-resolution segmentation, with the output from the Siamese network. To validate the effectiveness of the proposed approach, we conducted experiments on multispectral images collected by the ZY-3 and GF-2 satellites. Experimental results demonstrate that our proposed method achieves comparable and better performance than mainstream methods in multi-sensor images change detection.

ACS Style

Moyang Wang; Kun Tan; Xiuping Jia; Xue Wang; Yu Chen. A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images. Remote Sensing 2020, 12, 205 .

AMA Style

Moyang Wang, Kun Tan, Xiuping Jia, Xue Wang, Yu Chen. A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images. Remote Sensing. 2020; 12 (2):205.

Chicago/Turabian Style

Moyang Wang; Kun Tan; Xiuping Jia; Xue Wang; Yu Chen. 2020. "A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images." Remote Sensing 12, no. 2: 205.

Journal article
Published: 09 May 2019 in IEEE Transactions on Geoscience and Remote Sensing
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The increase in the spectral and spatial information of hyperspectral imagery poses challenges in classification due to the fact that spectral bands are highly correlated, training samples may be limited, and high resolution may increase intraclass difference and interclass similarity. In this paper, in order to better handle these problems, a Caps-TripleGAN framework is proposed by exploring the 1-D structure triple generative adversarial network (TripleGAN) for sample generation and integrating CapsNet for hyperspectral image classification. Moreover, spatial information is utilized to verify the learning capacity and discriminative ability of the Caps-TripleGAN framework. The experimental results obtained with three real hyperspectral data sets confirm that the proposed method outperforms most of the state-of-the-art methods.

ACS Style

Xue Wang; Kun Tan; Qian Du; Yu Chen; Peijun Du. Caps-TripleGAN: GAN-Assisted CapsNet for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2019, 57, 7232 -7245.

AMA Style

Xue Wang, Kun Tan, Qian Du, Yu Chen, Peijun Du. Caps-TripleGAN: GAN-Assisted CapsNet for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2019; 57 (9):7232-7245.

Chicago/Turabian Style

Xue Wang; Kun Tan; Qian Du; Yu Chen; Peijun Du. 2019. "Caps-TripleGAN: GAN-Assisted CapsNet for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing 57, no. 9: 7232-7245.

Journal article
Published: 18 March 2019 in Remote Sensing
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This paper introduces a novel semi-supervised tri-training classification algorithm based on regularized local discriminant embedding (RLDE) for hyperspectral imagery. In this algorithm, the RLDE method is used for optimal feature information extraction, to solve the problems of singular values and over-fitting, which are the main problems in the local discriminant embedding (LDE) and local Fisher discriminant analysis (LFDA) methods. An active learning method is then used to select the most useful and informative samples from the candidate set. In the experiments undertaken in this study, the three base classifiers were multinomial logistic regression (MLR), k-nearest neighbor (KNN), and random forest (RF). To confirm the effectiveness of the proposed RLDE method, experiments were conducted on two real hyperspectral datasets (Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS)), and the proposed RLDE tri-training algorithm was compared with its counterparts of tri-training alone, LDE, and LFDA. The experiments confirmed that the proposed approach can effectively improve the classification accuracy for hyperspectral imagery.

ACS Style

Depin Ou; Kun Tan; Qian Du; Jishuai Zhu; Xue Wang; Yu Chen. A Novel Tri-Training Technique for the Semi-Supervised Classification of Hyperspectral Images Based on Regularized Local Discriminant Embedding Feature Extraction. Remote Sensing 2019, 11, 654 .

AMA Style

Depin Ou, Kun Tan, Qian Du, Jishuai Zhu, Xue Wang, Yu Chen. A Novel Tri-Training Technique for the Semi-Supervised Classification of Hyperspectral Images Based on Regularized Local Discriminant Embedding Feature Extraction. Remote Sensing. 2019; 11 (6):654.

Chicago/Turabian Style

Depin Ou; Kun Tan; Qian Du; Jishuai Zhu; Xue Wang; Yu Chen. 2019. "A Novel Tri-Training Technique for the Semi-Supervised Classification of Hyperspectral Images Based on Regularized Local Discriminant Embedding Feature Extraction." Remote Sensing 11, no. 6: 654.

Journal article
Published: 12 February 2019 in International Journal of Environmental Research and Public Health
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With the exploitation of coalfields, the eco-environment around the coalfields can become badly damaged. To address this issue, “mine greening” has been proposed by the Ministry of Land and Resources of China. The sustainable development of mine environments has now become one of the most prominent issues in China. In this study, we aimed to make use of Landsat 7 ETM+ and Landsat 8 OLI images obtained between 2005 and 2016 to analyze the eco-environment in a coalfield. Land cover was implemented as the basic evaluation factor to establish the evaluation model for the eco-environment. Analysis and investigation of the eco-environment in the Yuxian coalfield was conducted using a novel evaluation model, based on the biological abundance index, vegetation coverage index, water density index, and natural geographical factors. The weight of each indicator was determined by an analytic hierarchy process. Meanwhile, we also used the classic ecological footprint to calculate the ecological carrying capacity in order to verify the effectiveness of the evaluation model. Results showed that the eco-environment index illustrated a slowly increasing tendency over the study period, and the ecological quality could be considered as “good”. The results of the evaluation model showed a strong correlation with the ecological carrying capacity with a correlation coefficient of 0.9734. In conclusion, the evaluation method is a supplement to the time-series quantitative evaluation of the eco-environment, and also helps us to explore the eco-environment in the mining area.

ACS Style

Xue Wang; Kun Tan; Kailei Xu; Yu Chen; Jianwei Ding. Quantitative Evaluation of the Eco-Environment in a Coalfield Based on Multi-Temporal Remote Sensing Imagery: A Case Study of Yuxian, China. International Journal of Environmental Research and Public Health 2019, 16, 511 .

AMA Style

Xue Wang, Kun Tan, Kailei Xu, Yu Chen, Jianwei Ding. Quantitative Evaluation of the Eco-Environment in a Coalfield Based on Multi-Temporal Remote Sensing Imagery: A Case Study of Yuxian, China. International Journal of Environmental Research and Public Health. 2019; 16 (3):511.

Chicago/Turabian Style

Xue Wang; Kun Tan; Kailei Xu; Yu Chen; Jianwei Ding. 2019. "Quantitative Evaluation of the Eco-Environment in a Coalfield Based on Multi-Temporal Remote Sensing Imagery: A Case Study of Yuxian, China." International Journal of Environmental Research and Public Health 16, no. 3: 511.

Journal article
Published: 11 February 2019 in Remote Sensing
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The drawback of pixel-based change detection is that it neglects the spatial correlation with neighboring pixels and has a high commission ratio. In contrast, object-based change detection (OBCD) depends on the accuracy of the segmentation scale, which is of great significance in image analysis. Accordingly, an object-based approach for automatic change detection using multiple classifiers and multi-scale uncertainty analysis (OB-MMUA) in high-resolution (HR) remote sensing images is proposed in this paper. In this algorithm, the gray-level co-occurrence matrix (GLCM), morphological, and Gabor filter texture features are extracted to construct the input data, along with the spectral features, to utilize the respective advantages of the features and to compensate for the insufficient spectral information. In addition, random forest is used to select the features and determine the optimal feature vectors for the change detection. Change vector analysis (CVA) based on uncertainty analysis is then implemented to select the initial training samples. According to the diversity, support vector machine (SVM), k-nearest neighbor (KNN), and extra-trees (ExT) classifiers are then chosen as the base classifiers for Dempster-Shafer (D-S) evidence theory fusion, and unlabeled samples are selected using an active learning method with spatial information. Finally, multi-scale object-based D-S evidence theory fusion and uncertainty analysis is used to classify the difference image. To validate the proposed approach, we conducted experiments using multispectral images collected by the ZY-3 and GF-2 satellites. The experimental results confirmed the effectiveness and superiority of the proposed approach, which integrates the respective advantages of the pixel-based and object-based methods.

ACS Style

Kun Tan; Yusha Zhang; Xue Wang; Yu Chen. Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis. Remote Sensing 2019, 11, 359 .

AMA Style

Kun Tan, Yusha Zhang, Xue Wang, Yu Chen. Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis. Remote Sensing. 2019; 11 (3):359.

Chicago/Turabian Style

Kun Tan; Yusha Zhang; Xue Wang; Yu Chen. 2019. "Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis." Remote Sensing 11, no. 3: 359.

Research article
Published: 07 August 2018 in Mathematical Problems in Engineering
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Understanding the development of cracks in masonry walls can provide insight into their capability for earthquake resistance. The crack development is characterized by the displacement difference of the adjacent positions on masonry walls. In seismic oscillation, the instantaneous dynamic displacements of multiple positions on masonry walls can warn of crack development and reflect the propagation of the seismic waves. For this reason, we proposed a monocular digital photography technique based on the PST-TBP (photographing scale transformation-time baseline parallax) method to monitor the instantaneous dynamic displacements of a masonry wall in seismic oscillation outdoors. The seismic oscillation was simulated by impacting a suspended steel plate with a hammer and by simulation software ANSYS (analysis system), for comparative analysis. The results show that it is feasible to use a hammer to impact a suspended steel plate to simulate the seismic oscillation as the stress concentration zones of the masonry wall model in ANSYS are consistent with the positions of destruction on the masonry wall, and that the crack development of the masonry wall in the X-direction could be characterized by a sinusoid-like curve, which is consistent with previous studies. The PST-TBP method can improve the measurement accuracy as it corrects the parallax errors caused by the change of intrinsic and extrinsic parameters of a digital camera. South of the test masonry wall, the measurement errors of the PST-TBP method were shown to be 0.83mm and 0.84mm in the X- and Z-directions, respectively, and in the west, the measurement errors in the X- and Z-directions were 0.49mm and 0.44mm, respectively. This study provides a technical basis for monitoring the crack development of the real masonry structures in seismic oscillation outdoors to assess their safety and has significant implications for improving the construction of masonry structures in earthquake-prone areas.

ACS Style

Guojian Zhang; Guangli Guo; Chengxin Yu; Long Li; Sai Hu; Xue Wang. Monitoring Instantaneous Dynamic Displacements of Masonry Walls in Seismic Oscillation Outdoors by Monocular Digital Photography. Mathematical Problems in Engineering 2018, 2018, 1 -15.

AMA Style

Guojian Zhang, Guangli Guo, Chengxin Yu, Long Li, Sai Hu, Xue Wang. Monitoring Instantaneous Dynamic Displacements of Masonry Walls in Seismic Oscillation Outdoors by Monocular Digital Photography. Mathematical Problems in Engineering. 2018; 2018 ():1-15.

Chicago/Turabian Style

Guojian Zhang; Guangli Guo; Chengxin Yu; Long Li; Sai Hu; Xue Wang. 2018. "Monitoring Instantaneous Dynamic Displacements of Masonry Walls in Seismic Oscillation Outdoors by Monocular Digital Photography." Mathematical Problems in Engineering 2018, no. : 1-15.

Journal article
Published: 07 February 2018 in International Journal of Remote Sensing
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ACS Style

Kun Tan; Xue Wang; Jishuai Zhu; Jun Hu; Jun Li. A novel active learning approach for the classification of hyperspectral imagery using quasi-Newton multinomial logistic regression. International Journal of Remote Sensing 2018, 39, 3029 -3054.

AMA Style

Kun Tan, Xue Wang, Jishuai Zhu, Jun Hu, Jun Li. A novel active learning approach for the classification of hyperspectral imagery using quasi-Newton multinomial logistic regression. International Journal of Remote Sensing. 2018; 39 (10):3029-3054.

Chicago/Turabian Style

Kun Tan; Xue Wang; Jishuai Zhu; Jun Hu; Jun Li. 2018. "A novel active learning approach for the classification of hyperspectral imagery using quasi-Newton multinomial logistic regression." International Journal of Remote Sensing 39, no. 10: 3029-3054.

Journal article
Published: 10 March 2017 in Scientific Reports
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In this study, the net primary productivity (NPP) in China from 2001 to 2012 was estimated based on the Carnegie-Ames-Stanford Approach (CASA) model using Moderate Resolution Imaging Spectroradiometer (MODIS) and meteorological datasets, and the accuracy was verified by a ChinaFLUX dataset. It was found that the spatiotemporal variations in NPP present a downward trend with the increase of latitude and longitude. Moreover, the influence of climate change on the evolution of NPP shows that NPP has had different impact factors in different regions and periods over the 12 years. The eastern region has shown the largest increase in gross regional product (GRP) and a significant fluctuation in NPP over the 12 years. Meanwhile, NPP in the eastern and central regions is significantly positively correlated with annual solar radiation, while NPP in these two regions is significantly negatively correlated with the growth rate of GRP. It is concluded that both the development of the economy and climate change have influenced NPP evolution in China. In addition, NPP has shown a steadily rising trend over the 12 years as a result of the great importance attributed to ecological issues when developing the economy.

ACS Style

Xue Wang; Kun Tan; Baozhang Chen; Peijun Du. Assessing the Spatiotemporal Variation and Impact Factors of Net Primary Productivity in China. Scientific Reports 2017, 7, srep44415 .

AMA Style

Xue Wang, Kun Tan, Baozhang Chen, Peijun Du. Assessing the Spatiotemporal Variation and Impact Factors of Net Primary Productivity in China. Scientific Reports. 2017; 7 (1):srep44415.

Chicago/Turabian Style

Xue Wang; Kun Tan; Baozhang Chen; Peijun Du. 2017. "Assessing the Spatiotemporal Variation and Impact Factors of Net Primary Productivity in China." Scientific Reports 7, no. 1: srep44415.