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Airborne hyperspectral remote sensing has the characteristics of high spatial and spectral resolutions, and provides an opportunity for accurate and efficient inland water qauality monitoring. Many studies have focused on evaluating and quantifying the concentrations of the optically active water quality parameters, for parameters such as chlorophyll-a (Chla), cyanobacteria, and colored dissolved organic matter (CDOM). For the optically inactive parameters, such as the permanganate index (CODMn), total nitrogen (TN), total phosphorus (TP), ammoniacal nitrogen (NH3–N), and heavy metals, it is difficult to estimate the concentrations directly, and the traditional indirect estimation models cannot meet the accuracy requirements, especially in heavily polluted inland waters. In this study, 60 water samples were collected at a depth of 50 cm from the Guanhe River in China, at the same time as the airborne data acquisition. We also developed and investigated two deep learning based regression models—a pixel-based deep neural network regression (pixel_DNNR) model and a patch-based deep neural network regression (patch_DNNR) model—to estimate seven optically inactive water quality parameters. Compared with the partial least squares regression (PLSR) and support vector regression (SVR) models, the deep learning based regression models can obtain a superior accuracy, especially the patch_DNNR model, which obtained a superior prediction accuracy for all parameters, with the prediction dataset coefficient of determination (Rp2) and the residual prediction deviation (RPD) values being greater than 0.6 and 1.6, respectively. In addition, thematic maps of the water quality classification results and water parameter concentrations were generated and the overall water quality and pollution sources were analyzed in the study area. The experimental results demonstrate that the deep learning based regression models show a good performance in the feature extraction and image understanding of high-dimensional data, and they provide us with a new approach for optically inactive inland water quality parameter estimation.
Chao Niu; Kun Tan; Xiuping Jia; Xue Wang. Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery. Environmental Pollution 2021, 286, 117534 .
AMA StyleChao Niu, Kun Tan, Xiuping Jia, Xue Wang. Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery. Environmental Pollution. 2021; 286 ():117534.
Chicago/Turabian StyleChao Niu; Kun Tan; Xiuping Jia; Xue Wang. 2021. "Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery." Environmental Pollution 286, no. : 117534.
With satellite platforms gazing at a target territory, the captured satellite videos exhibit local misalignment and local intensity variation on some stationary objects that can be mistakenly extracted as moving objects and increase false alarm rates.Typical approaches for mitigating the effect of moving cameras in Moving Object Detection (MOD) follow domain transformation technique, where the misalignment between consecutive frames is restricted to the image planar.However, such technique cannot properly handle satellite videos, as the local misalignment on them is caused by the varying projections from the 3D objects on the Earth's surface to 2D image planar. In order to suppress the effect of moving satellite platform in MOD, we propose a Moving-Confidence-Assisted Matrix Decomposition (MCMD) model, where foreground regularization is designed to promote real moving objects and ignore system movements with the assistance of a moving-confidence score estimated from dense optical flows. For solving the convex optimization problem in MCMD, both batch processing and online solutions are developed in this study, by adopting the alternating direction method and the stochastic optimization strategy, respectively. Experimental results on the videos captured by SkySat and Jilin-1 show that MCMD outperforms the state-of-the-art techniques with improved precision by suppressing effect of nonstationary satellite platforms.
Junpeng Zhang; Xiuping Jia; Jiankun Hu; Kun Tan. Moving Vehicle Detection for Remote Sensing Video Surveillance with Nonstationary Satellite Platform. IEEE Transactions on Pattern Analysis and Machine Intelligence 2021, PP, 1 -1.
AMA StyleJunpeng Zhang, Xiuping Jia, Jiankun Hu, Kun Tan. Moving Vehicle Detection for Remote Sensing Video Surveillance with Nonstationary Satellite Platform. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2021; PP (99):1-1.
Chicago/Turabian StyleJunpeng Zhang; Xiuping Jia; Jiankun Hu; Kun Tan. 2021. "Moving Vehicle Detection for Remote Sensing Video Surveillance with Nonstationary Satellite Platform." IEEE Transactions on Pattern Analysis and Machine Intelligence PP, no. 99: 1-1.
A number of algorithms have been developed for soil organic matter (SOM) or soil heavy metal detection in airborne hyperspectral imagery with high spatial and spectral resolutions. However, to achieve improved land management, the problems of the inconsistent features and low accuracy still need to be solved. In this paper, we propose a novel regression model to estimate the concentrations of SOM, arsenic (As), and chromium (Cr) in soil. Firstly, a hyperspectral unmixing technique is utilized to extract the bare soil pixels. We then combine the absorption depth feature after continuum removal, the original absorption feature, the band ratio feature, and the first-order differential feature, to form a set of features for parameter inversion. To solve the over-fitting problem caused by the small number of samples and the weak expression problem, the semi-supervised deep neural network regression (Semi-DNNR) model is introduced. The experimental were conducted using several datasets collected by HyMap, which is an airborne hyperspectral imaging sensor in VNIR-SWIR spectral range in Yitong county, Jilin province, China. The proposed Semi-DNNR model shows a good performance in this study, with the prediction Rp2 values for SOM, As, and Cr being 0.71, 0.82, and 0.63, respectively. After the spatial distribution map of the soil components of the study area was overlaid with the stream network, which was obtained from the digital elevation model (DEM). It was found that snowmelt, the melting of frozen soil, and surface rainfall can transport SOM to low-lying areas. A similar phenomenon was also observed for As, due to SOM adsorption and dissolved organic matter (DOM) complexation. A comparison of the proposed method with both feature selection methods (competitive adaptive reweighted sampling (CARS), genetic algorithm (GA)) and regression methods (partial least squares regression (PLSR), support vector regression (SVR)) shows that the proposed feature selection method is more robust than the CARS and GA methods. The proposed Semi-DNNR model was found to be at least 18.80% higher in prediction accuracy for As than the SVR or PLSR methods, at least 25.71% higher for Cr, and at least 19.73% higher for SOM.
Depin Ou; Kun Tan; Jian Lai; Xiuping Jia; Xue Wang; Yu Chen; Jie Li. Semi-supervised DNN regression on airborne hyperspectral imagery for improved spatial soil properties prediction. Geoderma 2020, 385, 114875 .
AMA StyleDepin Ou, Kun Tan, Jian Lai, Xiuping Jia, Xue Wang, Yu Chen, Jie Li. Semi-supervised DNN regression on airborne hyperspectral imagery for improved spatial soil properties prediction. Geoderma. 2020; 385 ():114875.
Chicago/Turabian StyleDepin Ou; Kun Tan; Jian Lai; Xiuping Jia; Xue Wang; Yu Chen; Jie Li. 2020. "Semi-supervised DNN regression on airborne hyperspectral imagery for improved spatial soil properties prediction." Geoderma 385, no. : 114875.
Ground deformation caused by underground coal mining has a large subsidence gradient and high non-linearity and may cause continuous destruction to the surface structure after mining. Synthetic aperture radar interferometry (InSAR) is a powerful method for measuring and reconstructing ground displacement. In this paper, a total of 19 descending orbit X-band TerraSAR-X and 16 ascending orbit C-band Sentinel-1 images were integrated to monitor the ground displacement of a coal mining area in Yu County (Hebei Province, China) using a small baseline subset (SBAS-InSAR) algorithm. We found that the SBAS-InSAR technology can obtain reliable results in regions without high gradient deformation by combining the deformation rates derived from both the TerraSAR-X and Sentinel-1 datasets. The maximum subsidence rate was approximately 125 mm/y between 17 June 2015 and 07 January 2016. In the case of large gradient deformation, it is indeed difficult to obtain mining-induced surface subsidence information accurately using conventional multi-temporal (MT-InSAR) techniques owing to the limitations of SAR wavelength. In response to this problem, a new decision-making fusion method based on SBAS-InSAR and offset tracking was developed to monitor large gradient settlement in mining areas. The results show that the method developed compensates for the shortcomings of traditional MT-InSAR technologies in the field of large-gradient deformation and obtains reliable results.
Yu Chen; Yunxiao Tong; Kun Tan. Coal Mining Deformation Monitoring Using SBAS-InSAR and Offset Tracking: A Case Study of Yu County, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 6077 -6087.
AMA StyleYu Chen, Yunxiao Tong, Kun Tan. Coal Mining Deformation Monitoring Using SBAS-InSAR and Offset Tracking: A Case Study of Yu County, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):6077-6087.
Chicago/Turabian StyleYu Chen; Yunxiao Tong; Kun Tan. 2020. "Coal Mining Deformation Monitoring Using SBAS-InSAR and Offset Tracking: A Case Study of Yu County, China." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 6077-6087.
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.
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 StyleKun 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 StyleKun 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.
Soil moisture is the crucial carrier of the global hydrologic cycle and the dynamic energy balance regulation process. Therefore, it is of great significance to monitor surface soil moisture content (SMC) accurately for the study of the natural ecological environment. The Hapke model is the most widely used photometric model in soil remote sensing research, but the development of this model is limited by the lack of valid multi–angular data. The main innovations of this paper have two aspects: (1) A novel soil moisture retrieval approach based on the Hapke (SMR–Hapke) model is derived by exploring the relationship between single scattering albedo (SSA) and SMC on the optical bands from 400 to 2400 nm. The performance of the proposed model was verified on a dataset consisting of four different soil samples, and the experimental results indicated that the inverted soil moisture from SMR–Hapke model coincided with the measurement values, with the R2 being generally more than 0.9 in the solar domain. (2) The SMR–Hapke model has been reduced to a linear form on the SWIR field and a physically-based normalized difference soil moisture index N D S M I H a p k e has been proposed. Based on the laboratory-based hyperspectral data, we compared the performance of N D S M I H a p k e with other traditional soil moisture indices using linear regression analysis, and the results demonstrate that the proposed N D S M I H a p k e had a great potential for estimating SMC with R2 values of 0.88. Finally, high–resolution SMC map was produced by combining the Sentinel–2 MSI data with N D S M I H a p k e . This study provides a novel extended Hapke model for the estimation of surface soil moisture content.
Yuan Zhang; Kun Tan; Xue Wang; Yu Chen. Retrieval of Soil Moisture Content Based on a Modified Hapke Photometric Model: A Novel Method Applied to Laboratory Hyperspectral and Sentinel-2 MSI Data. Remote Sensing 2020, 12, 2239 .
AMA StyleYuan Zhang, Kun Tan, Xue Wang, Yu Chen. Retrieval of Soil Moisture Content Based on a Modified Hapke Photometric Model: A Novel Method Applied to Laboratory Hyperspectral and Sentinel-2 MSI Data. Remote Sensing. 2020; 12 (14):2239.
Chicago/Turabian StyleYuan Zhang; Kun Tan; Xue Wang; Yu Chen. 2020. "Retrieval of Soil Moisture Content Based on a Modified Hapke Photometric Model: A Novel Method Applied to Laboratory Hyperspectral and Sentinel-2 MSI Data." Remote Sensing 12, no. 14: 2239.
The problem of heavy metal pollution of soils in China is severe. The traditional spectral methods for soil heavy metal monitoring and assessment cannot meet the needs for large-scale areas. Therefore, in this study, we used HyMap-C airborne hyperspectral imagery to explore the estimation of soil heavy metal concentration. Ninety five soil samples were collected synchronously with airborne image acquisition in the mining area of Yitong County, China. The pre-processed spectrum of airborne images at the sampling point was then selected by the competitive adaptive reweighted sampling (CARS) method. The selected spectral features and the heavy metal data of soil samples were inverted to establish the inversion model. An ensemble learning method based on a stacking strategy is proposed for the inversion modeling of soil samples and image data. The experimental results show that this CARS-Stacking method can better predict the four heavy metals in the study area than other methods. For arsenic (As), chromium (Cr), lead (Pb), and zinc (Zn), the determination coefficients of the test data set (RP2) are 0.73, 0.63, 0.60, and 0.71, respectively. It was found that the estimated results and the distribution trend of heavy metals are almost the same as in actual ground measurements.
Kun Tan; Weibo Ma; Lihan Chen; Huimin Wang; Qian Du; Peijun Du; Bokun Yan; Rongyuan Liu; Haidong Li. Estimating the distribution trend of soil heavy metals in mining area from HyMap airborne hyperspectral imagery based on ensemble learning. Journal of Hazardous Materials 2020, 401, 123288 .
AMA StyleKun Tan, Weibo Ma, Lihan Chen, Huimin Wang, Qian Du, Peijun Du, Bokun Yan, Rongyuan Liu, Haidong Li. Estimating the distribution trend of soil heavy metals in mining area from HyMap airborne hyperspectral imagery based on ensemble learning. Journal of Hazardous Materials. 2020; 401 ():123288.
Chicago/Turabian StyleKun Tan; Weibo Ma; Lihan Chen; Huimin Wang; Qian Du; Peijun Du; Bokun Yan; Rongyuan Liu; Haidong Li. 2020. "Estimating the distribution trend of soil heavy metals in mining area from HyMap airborne hyperspectral imagery based on ensemble learning." Journal of Hazardous Materials 401, no. : 123288.
Airborne hyperspectral remote sensing is an important application in the ecological monitoring of the environment in mining areas, and accurate preprocessing of the original images is the key to quantitative information retrieval. The original image data need radiation correction to acquire surface reflectance data. Due to the impact of the field angle, incidental radiance, and the bidirectional reflectance distribution function (BRDF), there can be a brightness gradient between adjacent strips, which leads to radiance difference and obvious chromatic aberration of the mosaicked images. We propose a novel data correction method for seamless mosaicking of airborne hyperspectral images. Firstly, visible and near-infrared (VNIR) and shortwave infrared (SWIR) sensors are calibrated in the laboratory, and the radiation calibration model of the sensor is established by an integrating-sphere system. A correction function is then established by combining the BRDF effect and the radiation attenuation coefficients. We also normalize the exposure time, sun altitude angle, and sensor altitude angle according to the flight strip. The results showed that this method is able to eliminate the signal distortion, allowing the seamless mosaicking of 37 strip images which were taken in different date and conditions in the study area. After the atmospheric correction of the imagery was completed, the accuracy of the preprocessing results was evaluated by field-measured ASD spectroradiometer data. The coefficient of determination R2 of the results for the reflectance was greater than 0.9. The experiments show that the proposed method has a good performance in radiation accuracy, and can provide high-quality hyperspectral data for the follow-up application of the ecological monitoring of a mining area.
Kun Tan; Chao Niu; Xiuping Jia; Depin Ou; Yu Chen; Shaogang Lei. Complete and accurate data correction for seamless mosaicking of airborne hyperspectral images: A case study at a mining site in Inner Mongolia, China. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 165, 1 -15.
AMA StyleKun Tan, Chao Niu, Xiuping Jia, Depin Ou, Yu Chen, Shaogang Lei. Complete and accurate data correction for seamless mosaicking of airborne hyperspectral images: A case study at a mining site in Inner Mongolia, China. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 165 ():1-15.
Chicago/Turabian StyleKun Tan; Chao Niu; Xiuping Jia; Depin Ou; Yu Chen; Shaogang Lei. 2020. "Complete and accurate data correction for seamless mosaicking of airborne hyperspectral images: A case study at a mining site in Inner Mongolia, China." ISPRS Journal of Photogrammetry and Remote Sensing 165, no. : 1-15.
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.
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 StyleXue 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 StyleXue 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.
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.
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 StyleMoyang 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 StyleMoyang 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.
Hyperspectral imaging, with the hundreds of bands and high spectral resolution, offers a promising approach for estimation of heavy metal concentration in agricultural soils. Using airborne imagery over a large-scale area for fast retrieval is of great importance for environmental monitoring and further decision support. However, few studies have focused on the estimation of soil heavy metal concentration by airborne hyperspectral imaging. In this study, we utilized the airborne hyperspectral data in LiuXin Mine of China obtained from HySpex VNIR-1600 and HySpex SWIR-384 sensor to establish the spectral-analysis-based model for retrieval of heavy metals concentration. Firstly, sixty soil samples were collected in situ, and their heavy metal concentrations (Cr, Cu, Pb) were determined by inductively coupled plasma-mass spectrometry analysis. Due to mixed pixels widespread in airborne hyperspectral images, spectral unmixing was conducted to obtain purer spectra of the soil and to improve the estimation accuracy. Ten of estimated models, including four different random forest models (RF)—standard random forest (SRF), regularized random forest (RRF), guided random forest (GRF), and guided regularized random forest (GRRF)—were introduced for hyperspectral estimated model in this paper. Compared with the estimation results, the best accuracy for Cr, Cu, and Pb is obtained by RF. It shows that RF can predict the three heavy metals better than other models in this area. For Cr, Cu, Pb, the best model of RF yields Rp2 values of 0.75,0.68 and 0.74 respectively, and the values of RMSEp are 5.62, 8.24, and 2.81 (mg/kg), respectively. The experiments show the average estimated values are close to the truth condition and the high estimated values concentrated near several industries, valifating the effectiveness of the presented method.
Kun Tan; Huimin Wang; Lihan Chen; Qian Du; Peijun Du; Cencen Pan. Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. Journal of Hazardous Materials 2019, 382, 120987 .
AMA StyleKun Tan, Huimin Wang, Lihan Chen, Qian Du, Peijun Du, Cencen Pan. Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. Journal of Hazardous Materials. 2019; 382 ():120987.
Chicago/Turabian StyleKun Tan; Huimin Wang; Lihan Chen; Qian Du; Peijun Du; Cencen Pan. 2019. "Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest." Journal of Hazardous Materials 382, no. : 120987.
Hyperspectral imagery contains abundant spectral information. Each band contains some specific characteristics closely related to target objects. Therefore, using these characteristics, hyperspectral imagery can be used for anomaly detection. Recently, with the development of compressed sensing, low-rank-representation-based methods have been applied to hyperspectral anomaly detection. In this study, novel low-rank representation methods were developed for anomaly detection from hyperspectral images based on the assumption that hyperspectral pixels can be effectively decomposed into a low-rank component (for background) and a sparse component (for anomalies). In order to improve detection performance, we imposed a spatial constraint on the low-rank representation coefficients, and single or multiple local window strategies was applied to smooth the coefficients. Experiments on both simulated and real hyperspectral datasets demonstrated that the proposed approaches can effectively improve hyperspectral anomaly detection performance.
Kun Tan; Zengfu Hou; Donglei Ma; Yu Chen; Qian Du. Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint. Remote Sensing 2019, 11, 1578 .
AMA StyleKun Tan, Zengfu Hou, Donglei Ma, Yu Chen, Qian Du. Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint. Remote Sensing. 2019; 11 (13):1578.
Chicago/Turabian StyleKun Tan; Zengfu Hou; Donglei Ma; Yu Chen; Qian Du. 2019. "Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint." Remote Sensing 11, no. 13: 1578.
Land surface deformation in metropolitan areas, which can cause varying degrees of hazard to both human lives and to properties, has been documented for decades in cities worldwide. Xuzhou, is one of the most important energy and industrial bases in eastern China, and has experienced significant land subsidence due to both excessive extraction of karst underground water and exploitation of mineral resources in recent decades. Furthermore, Xuzhou has recently undergone rapid urbanization in terms of urban expansion and underground construction, which could induce additional pressure on the urban land surface. However, most previous research on land surface deformation in the Xuzhou urban areas has been conducted based on traditional ground-based deformation monitoring techniques with sparse measurements. Little is known about the regional spatiotemporal behavior of land surface displacement in Xuzhou. In this study, a detailed interferometric synthetic aperture radar (InSAR) time series analysis was performed to characterize the spatial pattern and temporal evolution of land surface deformation in central areas of Xuzhou during 2015–2018. A method based on principal component analysis was adopted to correct artifacts in the InSAR signal. Results showed the correction strategy markedly reduced the discrepancy between global navigation satellite systems and InSAR measurements. Noticeable land subsidence (−5 to −41 mm/yr) was revealed widely within the Xuzhou urban areas, particularly along subway lines under construction, newly developed districts, and in old coal goafs. Remarkable consistent land uplift (up to +25 mm/yr) was found to have significantly affected two long narrow areas within the old goafs since 2015. The possible principal influencing factors contributing to the land surface displacements such as subway tunneling, building construction, mining, underground water levels and geological conditions are then discussed.
Yu Chen; Kun Tan; Shiyong Yan; Kefei Zhang; Xiaoyang Liu; Huaizhan Li; Yaqin Sun. Monitoring Land Surface Displacement over Xuzhou (China) in 2015–2018 through PCA-Based Correction Applied to SAR Interferometry. Remote Sensing 2019, 11, 1494 .
AMA StyleYu Chen, Kun Tan, Shiyong Yan, Kefei Zhang, Xiaoyang Liu, Huaizhan Li, Yaqin Sun. Monitoring Land Surface Displacement over Xuzhou (China) in 2015–2018 through PCA-Based Correction Applied to SAR Interferometry. Remote Sensing. 2019; 11 (12):1494.
Chicago/Turabian StyleYu Chen; Kun Tan; Shiyong Yan; Kefei Zhang; Xiaoyang Liu; Huaizhan Li; Yaqin Sun. 2019. "Monitoring Land Surface Displacement over Xuzhou (China) in 2015–2018 through PCA-Based Correction Applied to SAR Interferometry." Remote Sensing 11, no. 12: 1494.
Heavy metals in the agricultural soils of reclaimed mining areas can contaminate food and endanger human health. The objective of this study is to effectively estimate the concentrations of heavy metals, such as zinc, chromium, arsenic, and lead, using hyperspectral sensor data and the random forest (RF) algorithm in the study area of Xuzhou, China. The RF’s built-in feature selection ability and modeling expressive ability in heavy metal estimation of soil were explored. After the preprocessing of the spectrum obtained by an ASD (analytical spectral device) field spectrometer, the random forest algorithm was carried out to establish the estimation model based on the correlation-selected features and the full-spectrum features respectively. Results of all the different processes were compared with classical approaches, such as partial least squares (PLS) regression and support vector machine (SVM). In all the experimental results, from the perspective of models, the best estimation model for Zn (R2 = 0.9061; RMSE = 6.5008) is based on the full-spectrum data of continuum removal (CR) pretreatment, and the best models for Cr (R2 = 0.9110; RMSE = 4.5683), As (R2 = 0.9912; RMSE = 0.5327), and Pb (R2 = 0.9756; RMSE = 1.1694) are all derived from the correlation-selected features. And these best models of these heavy metals are all established by the RF method. The experiments in this paper show that random forests can make full use of the input spectral data in the estimation of four kinds of heavy metals, and the obtained models are superior to those established by traditional methods.
Kun Tan; Weibo Ma; Fuyu Wu; Qian Du. Random forest–based estimation of heavy metal concentration in agricultural soils with hyperspectral sensor data. Environmental Monitoring and Assessment 2019, 191, 446 .
AMA StyleKun Tan, Weibo Ma, Fuyu Wu, Qian Du. Random forest–based estimation of heavy metal concentration in agricultural soils with hyperspectral sensor data. Environmental Monitoring and Assessment. 2019; 191 (7):446.
Chicago/Turabian StyleKun Tan; Weibo Ma; Fuyu Wu; Qian Du. 2019. "Random forest–based estimation of heavy metal concentration in agricultural soils with hyperspectral sensor data." Environmental Monitoring and Assessment 191, no. 7: 446.
Most of the conventional anomaly detectors only take advantage of the spectral information and do not consider the spatial information within neighboring pixels. Recently, the spectral-spatial based local summation anomaly detection (LSAD) algorithm has achieved excellent detection performances. In order to obtain various local spatial distributions with the neighboring pixels of the pixels under test, the LSAD algorithm exploits a multiple-window sliding filter, which can be computationally expensive and time-consuming. In this paper, to address these issues, two modified LSAD-based methods are proposed. The first method, called local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector (LSUNRSORAD), is based on the concept that each pixel in the background can be approximately represented by its spatial neighborhood. The second method, called local summation anomaly detection based on collaborative representation and inverse distance weight (LSAD-CR-IDW), uses the surrounding pixels collected inside the outer window, while outside the inner window, to linearly represent the test pixel and introduces collaborative representation and inverse distance weight to further improve the computational speed and detection precision, respectively. The proposed methods were applied to a synthetic dataset and three real datasets. The experimental results show that the proposed methods have a better detection accuracy and computational speed when compared with the LSAD algorithm and others.
Kun Tan; Zengfu Hou; Fuyu Wu; Qian Du; Yu Chen. Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation. Remote Sensing 2019, 11, 1318 .
AMA StyleKun Tan, Zengfu Hou, Fuyu Wu, Qian Du, Yu Chen. Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation. Remote Sensing. 2019; 11 (11):1318.
Chicago/Turabian StyleKun Tan; Zengfu Hou; Fuyu Wu; Qian Du; Yu Chen. 2019. "Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation." Remote Sensing 11, no. 11: 1318.
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.
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 StyleXue 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 StyleXue 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.
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.
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 StyleDepin 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 StyleDepin 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.
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.
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 StyleXue 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 StyleXue 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.
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.
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 StyleKun 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 StyleKun 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.
In this paper, a novel feature extraction method is proposed for hyperspectral image classification using a Gaussian–Bernoulli restricted Boltzmann machine (GBRBM) in parallel. The proposed approach employs several GBRBMs with different hidden layers to extract deep features from hyperspectral images, which are nonlinear and local invariant. Based on the learned deep features, a logistic regression layer is trained for classification. The proposed approaches are carried out on two public hyperspectral datasets: Pavia University dataset and Salinas dataset, and a new dataset obtained by HySpex imaging spectrometer in the mining area in Xuzhou. The obtained results reveal that the proposed approach offers superior performance compared to traditional classifiers. The advantage of the proposed GBRBM is that it can extract deep features in an unsupervised way and reduce the prediction time by using GPU. In particular, the classification results of the mining area provide valuable suggestions to improve environmental protection.
Kun Tan; Fuyu Wu; Qian Du; Peijun Du; Yu Chen. A Parallel Gaussian–Bernoulli Restricted Boltzmann Machine for Mining Area Classification With Hyperspectral Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2019, 12, 627 -636.
AMA StyleKun Tan, Fuyu Wu, Qian Du, Peijun Du, Yu Chen. A Parallel Gaussian–Bernoulli Restricted Boltzmann Machine for Mining Area Classification With Hyperspectral Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019; 12 (2):627-636.
Chicago/Turabian StyleKun Tan; Fuyu Wu; Qian Du; Peijun Du; Yu Chen. 2019. "A Parallel Gaussian–Bernoulli Restricted Boltzmann Machine for Mining Area Classification With Hyperspectral Imagery." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 2: 627-636.