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Liqin Cao
School of Printing and Packaging, Wuhan University, Wuhan 430079, China

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Journal article
Published: 24 July 2021 in Remote Sensing
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Hyperspectral imagery has been widely used in precision agriculture due to its rich spectral characteristics. With the rapid development of remote sensing technology, the airborne hyperspectral imagery shows detailed spatial information and temporal flexibility, which open a new way to accurate agricultural monitoring. To extract crop types from the airborne hyperspectral images, we propose a fine classification method based on multi-feature fusion and deep learning. In this research, the morphological profiles, GLCM texture and endmember abundance features are leveraged to exploit the spatial information of the hyperspectral imagery. Then, the multiple spatial information is fused with the original spectral information to generate classification result by using the deep neural network with conditional random field (DNN+CRF) model. Specifically, the deep neural network (DNN) is a deep recognition model which can extract depth features and mine the potential information of data. As a discriminant model, conditional random field (CRF) considers both spatial and contextual information to reduce the misclassification noises while keeping the object boundaries. Moreover, three multiple feature fusion approaches, namely feature stacking, decision fusion and probability fusion, are taken into account. In the experiments, two airborne hyperspectral remote sensing datasets (Honghu dataset and Xiong’an dataset) are used. The experimental results show that the classification performance of the proposed method is satisfactory, where the salt and pepper noise is decreased, and the boundary of the ground object is preserved.

ACS Style

Lifei Wei; Kun Wang; Qikai Lu; Yajing Liang; Haibo Li; Zhengxiang Wang; Run Wang; Liqin Cao. Crops Fine Classification in Airborne Hyperspectral Imagery Based on Multi-Feature Fusion and Deep Learning. Remote Sensing 2021, 13, 2917 .

AMA Style

Lifei Wei, Kun Wang, Qikai Lu, Yajing Liang, Haibo Li, Zhengxiang Wang, Run Wang, Liqin Cao. Crops Fine Classification in Airborne Hyperspectral Imagery Based on Multi-Feature Fusion and Deep Learning. Remote Sensing. 2021; 13 (15):2917.

Chicago/Turabian Style

Lifei Wei; Kun Wang; Qikai Lu; Yajing Liang; Haibo Li; Zhengxiang Wang; Run Wang; Liqin Cao. 2021. "Crops Fine Classification in Airborne Hyperspectral Imagery Based on Multi-Feature Fusion and Deep Learning." Remote Sensing 13, no. 15: 2917.

Conference paper
Published: 26 May 2021 in Lecture Notes in Electrical Engineering
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With the development of deep learning technology, the automated grayscale image colorization methods have been widely used. As color space is very important in colorization, we analyzed 3 different color spaces, named YUV, Lab, and HSV. We used deep learning methods to evaluate the effect of different color spaces to image colorization methods. The colorization method was based on the VGG16 and the Residual Encoder model. The results demonstrated that the quality of color transfer in the YUV and Lab color spaces is better than that in the HSV color space.

ACS Style

Wenqian Yu; Liqin Cao; Zhijiang Li; Shengqing Xia. Analysis of the Influence of Color Space Selection on Color Transfer. Lecture Notes in Electrical Engineering 2021, 162 -169.

AMA Style

Wenqian Yu, Liqin Cao, Zhijiang Li, Shengqing Xia. Analysis of the Influence of Color Space Selection on Color Transfer. Lecture Notes in Electrical Engineering. 2021; ():162-169.

Chicago/Turabian Style

Wenqian Yu; Liqin Cao; Zhijiang Li; Shengqing Xia. 2021. "Analysis of the Influence of Color Space Selection on Color Transfer." Lecture Notes in Electrical Engineering , no. : 162-169.

Journal article
Published: 18 February 2021 in Remote Sensing
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Although hyperspectral anomaly detection is commonly conducted in the visible, near-infrared, and shortwave infrared spectral regions, there has been less research on hyperspectral anomaly detection in the longwave infrared (LWIR) hyperspectral region. The radiance of thermal infrared hyperspectral imagery is determined by the temperature and emissivity. To avoid the detection uncertainty caused by the single factor of temperature, emissivity can be introduced to detect anomalies. However, in the emissivity domain, the spectral contrast and signal-to-noise ratio (SNR) are low, which makes it difficult to separate the anomalies from the background. In this paper, an anomaly detection method combining emissivity and a segmented low-rank prior (EaSLRP) is proposed for use with thermal infrared hyperspectral imagery. The EaSLRP method is divided into three parts—1) temperature/emissivity retrieval, 2) extraction of the thermal infrared hyperspectral background information, and 3) Mahalanobis distance detection. A homogeneous region generation method is also proposed to solve the problem of the complex global background leading to inaccurate background estimation. The GoDec method is used for matrix decomposition and background information extraction and to remove some of the noise. The proposed Mahalanobis distance detector then uses the background component and original image for anomaly detection, while highlighting the spectral difference between the anomalies and background. This method can also suppress the influence of noise, to some extent. The experimental results obtained with airborne Fourier transform thermal infrared spectrometer hyperspectral images demonstrate that the EaSLRP method is effective when compared with the Reed–Xiaoli detector (RXD), the segmented RX detector (SegRX), the low-rank and sparse representation-based detector (LRASR), the low-rank and sparse matrix decomposition (LRaSMD)-based Mahalanobis distance method (LSMAD), and the locally enhanced low-rank prior method (LELRP-AD).

ACS Style

Xuhe Zhu; Liqin Cao; Shaoyu Wang; Lyuzhou Gao; Yanfei Zhong. Anomaly Detection in Airborne Fourier Transform Thermal Infrared Spectrometer Images Based on Emissivity and a Segmented Low-Rank Prior. Remote Sensing 2021, 13, 754 .

AMA Style

Xuhe Zhu, Liqin Cao, Shaoyu Wang, Lyuzhou Gao, Yanfei Zhong. Anomaly Detection in Airborne Fourier Transform Thermal Infrared Spectrometer Images Based on Emissivity and a Segmented Low-Rank Prior. Remote Sensing. 2021; 13 (4):754.

Chicago/Turabian Style

Xuhe Zhu; Liqin Cao; Shaoyu Wang; Lyuzhou Gao; Yanfei Zhong. 2021. "Anomaly Detection in Airborne Fourier Transform Thermal Infrared Spectrometer Images Based on Emissivity and a Segmented Low-Rank Prior." Remote Sensing 13, no. 4: 754.

Journal article
Published: 14 September 2020 in IEEE Access
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Urban rivers are often narrow, and general remote sensing data cannot meet the needs of water quality monitoring. In the process of monitoring of river water quality by remote sensing, the spectral and spatial dimension of satellite-borne images cannot be taken into consideration at the same time, making fine pollution monitoring of urban rivers difficult. Transparency is one of the core indicators for evaluating water quality, and hyperspectral remote sensing data are rich in spectral information and can be used for quantitative transparency estimation. The application of unmanned aerial vehicles (UAV)remote sensing effectively makes up for the deficiencies in satellite remote sensing monitoring. Aiming at this problem, this paper proposed the use of the eXtreme Gradient Boosting (XGBoost) regression algorithm for the quantitative inversion of urban river transparency. The spatial resolution of the collected imagery is 18.5 cm, which is suitable for urban rivers that are almost ten meters wide. Compared with five traditional empirical models, integrated algorithms such as gradient regression and random forest get much better results. Moreover, the accuracy of transparency estimation using the XGBoost regression algorithm was significantly improved, and the inversion model R2 in both study areas reached over 0.97. Finally, the established transparency inversion models were used to generate transparency distribution maps of the two study areas. The results showed that the distribution of the water transparency was consistent with the results of the field monitoring, indicating that it is feasible to use the XGBoost algorithm for the inversion of urban river transparency in UAV-borne hyperspectral imagery.

ACS Style

Lifei Wei; Zhou Wang; Can Huang; Yu Zhang; Zhengxiang Wang; Huiqiong Xia; Liqin Cao. Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery. IEEE Access 2020, 8, 168137 -168153.

AMA Style

Lifei Wei, Zhou Wang, Can Huang, Yu Zhang, Zhengxiang Wang, Huiqiong Xia, Liqin Cao. Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery. IEEE Access. 2020; 8 (99):168137-168153.

Chicago/Turabian Style

Lifei Wei; Zhou Wang; Can Huang; Yu Zhang; Zhengxiang Wang; Huiqiong Xia; Liqin Cao. 2020. "Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery." IEEE Access 8, no. 99: 168137-168153.

Journal article
Published: 21 July 2020 in Sensors
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With the continuous application of arsenic-containing chemicals, arsenic pollution in soil has become a serious problem worldwide. The detection of arsenic pollution in soil is of great significance to the protection and restoration of soil. Hyperspectral remote sensing is able to effectively monitor heavy metal pollution in soil. However, due to the possible complex nonlinear relationship between soil arsenic (As) content and the spectrum and data redundancy, an estimation model with high efficiency and accuracy is urgently needed. In response to this situation, 62 samples and 27 samples were collected in Daye and Honghu, Hubei Province, respectively. Spectral measurement and physical and chemical analysis were performed in the laboratory to obtain the As content and spectral reflectance. After the continuum removal (CR) was performed, the stable competitive adaptive reweighting sampling algorithm coupled the successive projections algorithm (sCARS-SPA) was used for characteristic band selection, which effectively solves the problem of data redundancy and collinearity. Partial least squares regression (PLSR), radial basis function neural network (RBFNN), and shuffled frog leaping algorithm optimization of the RBFNN (SFLA-RBFNN) were established in the characteristic wavelengths to predict soil As content. These results show that the sCARS-SPA-SFLA-RBFNN model has the best universality and high prediction accuracy in different land-use types, which is a scientific and effective method for estimating the soil As content.

ACS Style

Lifei Wei; Haochen Pu; Zhengxiang Wang; Ziran Yuan; Xinru Yan; Liqin Cao. Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing. Sensors 2020, 20, 4056 .

AMA Style

Lifei Wei, Haochen Pu, Zhengxiang Wang, Ziran Yuan, Xinru Yan, Liqin Cao. Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing. Sensors. 2020; 20 (14):4056.

Chicago/Turabian Style

Lifei Wei; Haochen Pu; Zhengxiang Wang; Ziran Yuan; Xinru Yan; Liqin Cao. 2020. "Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing." Sensors 20, no. 14: 4056.

Cover information
Published: 10 June 2020 in Color Research & Application
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ACS Style

Zhijiang Li; Yingping Zheng; Liqin Cao; Lei Jiao; Yanfei Zhong; Caiyi Zhang. Cover Information. Color Research & Application 2020, 45, 1 .

AMA Style

Zhijiang Li, Yingping Zheng, Liqin Cao, Lei Jiao, Yanfei Zhong, Caiyi Zhang. Cover Information. Color Research & Application. 2020; 45 (4):1.

Chicago/Turabian Style

Zhijiang Li; Yingping Zheng; Liqin Cao; Lei Jiao; Yanfei Zhong; Caiyi Zhang. 2020. "Cover Information." Color Research & Application 45, no. 4: 1.

Journal article
Published: 13 May 2020 in Sensors
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Soil organic matter (SOM) refers to all carbon-containing organic matter in soil and is one of the most important indicators of soil fertility. The hyperspectral inversion analysis of SOM traditionally relies on laboratory chemical testing methods, which have the disadvantages of being inefficient and time-consuming. In this study, 69 soil samples were collected from the Honghu farmland area and a mining area in northwest China. After pretreatment, 10 spectral indicators were obtained. Ridge regression, kernel ridge regression, Bayesian ridge regression, and AdaBoost algorithms were then used to construct the SOM hyperspectral inversion model based on the characteristic bands, and the accuracy of the models was compared. The results showed that the AdaBoost algorithm based on a grid search had the best accuracy in the different regions. For the mining area in northwest China, R p 2 = 0.91, R M S E p = 0.22, and M A E p = 0.2. For the Honghu farmland area, R p 2 = 0.86, R M S E p = 0.72, and M A E p = 0.56. The detection of SOM content using hyperspectral technology has the characteristics of a high detection precision and high speed, which will be of great significance for the rapid development of precision agriculture.

ACS Style

Lifei Wei; Ziran Yuan; Zhengxiang Wang; Liya Zhao; Yangxi Zhang; XianYou Lu; Liqin Cao. Hyperspectral Inversion of Soil Organic Matter Content Based on a Combined Spectral Index Model. Sensors 2020, 20, 2777 .

AMA Style

Lifei Wei, Ziran Yuan, Zhengxiang Wang, Liya Zhao, Yangxi Zhang, XianYou Lu, Liqin Cao. Hyperspectral Inversion of Soil Organic Matter Content Based on a Combined Spectral Index Model. Sensors. 2020; 20 (10):2777.

Chicago/Turabian Style

Lifei Wei; Ziran Yuan; Zhengxiang Wang; Liya Zhao; Yangxi Zhang; XianYou Lu; Liqin Cao. 2020. "Hyperspectral Inversion of Soil Organic Matter Content Based on a Combined Spectral Index Model." Sensors 20, no. 10: 2777.

Journal article
Published: 24 April 2020 in Applied Sciences
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Soil total arsenic (TAs) contamination caused by human activities—such as mining, smelting, and agriculture—is a problem of global concern. Visible/near-infrared (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) do not need too much sample preparation and utilization of chemicals to evaluate total arsenic (TAs) concentration in soil. VNIR with hyperspectral imaging has the potential to predict TAs concentration in soil. In this study, 59 soil samples were collected from the Daye City mining area of China, and hyperspectral imaging of the soil samples was undertaken using a visible/near-infrared hyperspectral imaging system (wavelength range 470–900 nm). Spectral preprocessing included standard normal variate (SNV) transformation, multivariate scatter correction (MSC), first derivative (FD) preprocessing, and second derivative (SD) preprocessing. Characteristic bands were then identified based on Spearman’s rank correlation coefficients. Four regression models were used for the modeling prediction: partial least squares regression (PLSR) (R2 = 0.71, RMSE = 0.48), support vector machine regression (SVMR) (R2 = 0.78, RMSE = 0.42), random forest (RF) (R2 = 0.78, RMSE = 0.42), and extremely randomized trees regression (ETR) (R2 = 0.81, RMSE = 0.38). The prediction results were compared with the results of atomic fluorescence spectrometry methods. In the prediction results of the models, the accuracy of ETR using FD preprocessing was the highest. The results confirmed that hyperspectral imaging combined with Spearman’s rank correlation with machine learning models can be used to estimate soil TAs content.

ACS Style

Lifei Wei; Yangxi Zhang; Ziran Yuan; Zhengxiang Wang; Feng Yin; Liqin Cao. Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil. Applied Sciences 2020, 10, 2941 .

AMA Style

Lifei Wei, Yangxi Zhang, Ziran Yuan, Zhengxiang Wang, Feng Yin, Liqin Cao. Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil. Applied Sciences. 2020; 10 (8):2941.

Chicago/Turabian Style

Lifei Wei; Yangxi Zhang; Ziran Yuan; Zhengxiang Wang; Feng Yin; Liqin Cao. 2020. "Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil." Applied Sciences 10, no. 8: 2941.

Conference paper
Published: 10 April 2020 in Lecture Notes in Electrical Engineering
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Dun Huang mural is an important part of Dun Huang art. Digital restoration for Dun Huang mural is significant and meaningful. At present, the restoration of Dun Huang mural mainly relies on manual work, which takes a lot of time and is limited by work experience. Although, the existing restoration methods perform well in painting for small missing region, there are disadvantages for large missing regions with semantic information. To solve these problems, Auto-encoder Generative Adversarial Nets (AGAN) was proposed for image restoration of Dun Huang Murals in this paper. In AGAN, the fully connected layer was replaced by the dilated convolution layer, which benefits the reconstruction of image with large scale missing. The experiments are executed over the Dun Huang dataset, and the results show that the restoration images based on AGAN were more realistic and natural. Compared with other traditional methods, our method achieved a better performance quantitatively and qualitatively.

ACS Style

Zhengguang Song; Wenjie Xuan; Jia Liu; Yudan Li; Liqin Cao. Image Restoration of Dun Huang Murals Based on Auto-encoder Generative Adversarial Neural Network. Lecture Notes in Electrical Engineering 2020, 186 -194.

AMA Style

Zhengguang Song, Wenjie Xuan, Jia Liu, Yudan Li, Liqin Cao. Image Restoration of Dun Huang Murals Based on Auto-encoder Generative Adversarial Neural Network. Lecture Notes in Electrical Engineering. 2020; ():186-194.

Chicago/Turabian Style

Zhengguang Song; Wenjie Xuan; Jia Liu; Yudan Li; Liqin Cao. 2020. "Image Restoration of Dun Huang Murals Based on Auto-encoder Generative Adversarial Neural Network." Lecture Notes in Electrical Engineering , no. : 186-194.

Journal article
Published: 03 April 2020 in Remote Sensing
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The HY-1C satellite, as part of China’s optical satellite constellation for global ocean monitoring, monitors the ocean and coastal environment by the three broad visible bands of the Coastal Zone Imager (CZI) instrument. However, as a result of the sensor instrument noise, the atmospheric environment during imaging, and the shooting angle, the satellite images often show uneven illumination and inconsistent color between neighboring images. In this paper, according to the characteristics of the HY-1C CZI instrument, we propose a color consistency processing framework for coastal zone images of Antarctica. First of all, the high-frequency and low-frequency information of the image is separated by a statistical filter with simple clustering. The uneven lighting is then replaced by artificial lighting, which is globally uniform. Finally, the color difference between images is corrected by a color transfer method. In order to evaluate the color consistency results quantitatively, a new quantitative evaluation method is proposed. The experimental results for the coastal zone images of Antarctica show that the new processing framework can effectively eliminate the unevenness in the lighting and color. The mosaic results show a good performance in consistent lighting and tones, and the lack of visible mosaic lines proves the effectiveness of the proposed method. The quantitative evaluation analysis confirms the superiority of the proposed method over the Wallis method.

ACS Style

Zhijiang Li; Haonan Zhu; Chunxia Zhou; Liqin Cao; Yanfei Zhong; Tao Zeng; Jianqiang Liu. A Color Consistency Processing Method for HY-1C Images of Antarctica. Remote Sensing 2020, 12, 1143 .

AMA Style

Zhijiang Li, Haonan Zhu, Chunxia Zhou, Liqin Cao, Yanfei Zhong, Tao Zeng, Jianqiang Liu. A Color Consistency Processing Method for HY-1C Images of Antarctica. Remote Sensing. 2020; 12 (7):1143.

Chicago/Turabian Style

Zhijiang Li; Haonan Zhu; Chunxia Zhou; Liqin Cao; Yanfei Zhong; Tao Zeng; Jianqiang Liu. 2020. "A Color Consistency Processing Method for HY-1C Images of Antarctica." Remote Sensing 12, no. 7: 1143.

Journal article
Published: 29 February 2020 in Sensors
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The sustainable development of water resources is always emphasized in China, and a set of perfect standards for the division of inland water environment quality have been established to monitor water quality. However, most of the 24 indicators that determine the water quality level in the standards are non-optically active parameters. The weak optical characteristics make it difficult to find significant correlations between the single parameters and the remote sensing imagery. In addition, traditional on-site testing methods have been unable to meet the increasingly extensive water-quality monitoring requirements. Based on the above questions, it’s meaningful that the supervised classification process of a detail-preserving smoothing classifier based on conditional random field (CRF) and Landsat-8 data was proposed in the two study areas around Wuhan and Huangshi in Hubei Province. The random forest classifier was selected to model the association potential of the CRF. The results (the first study area: OA = 89.50%, Kappa = 0.841; the second study area: OA = 90.35%, Kappa = 0.868) showed that the water-quality monitoring based on CRF model is feasible, and this approach can provide a reference for water-quality mapping of inland lakes. In the future, it may only require a small amount of on-site sampling to achieve the identification of the water quality levels of inland lakes across a large area of China.

ACS Style

Lifei Wei; Yu Zhang; Can Huang; Zhengxiang Wang; Qingbin Huang; Feng Yin; Yue Guo; Liqin Cao. Inland Lakes Mapping for Monitoring Water Quality Using a Detail/Smoothing-Balanced Conditional Random Field Based on Landsat-8/Levels Data. Sensors 2020, 20, 1345 .

AMA Style

Lifei Wei, Yu Zhang, Can Huang, Zhengxiang Wang, Qingbin Huang, Feng Yin, Yue Guo, Liqin Cao. Inland Lakes Mapping for Monitoring Water Quality Using a Detail/Smoothing-Balanced Conditional Random Field Based on Landsat-8/Levels Data. Sensors. 2020; 20 (5):1345.

Chicago/Turabian Style

Lifei Wei; Yu Zhang; Can Huang; Zhengxiang Wang; Qingbin Huang; Feng Yin; Yue Guo; Liqin Cao. 2020. "Inland Lakes Mapping for Monitoring Water Quality Using a Detail/Smoothing-Balanced Conditional Random Field Based on Landsat-8/Levels Data." Sensors 20, no. 5: 1345.

Research article
Published: 20 February 2020 in Color Research & Application
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Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named student's t‐based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non‐iterative clustering, to automatically decrease the image data volume. A student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k‐means, fuzzy c‐means, mean‐shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.

ACS Style

Zhijiang Li; Yingping Zheng; Liqin Cao; Lei Jiao; Yanfei Zhong; Caiyi Zhang. A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering. Color Research & Application 2020, 45, 656 -670.

AMA Style

Zhijiang Li, Yingping Zheng, Liqin Cao, Lei Jiao, Yanfei Zhong, Caiyi Zhang. A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering. Color Research & Application. 2020; 45 (4):656-670.

Chicago/Turabian Style

Zhijiang Li; Yingping Zheng; Liqin Cao; Lei Jiao; Yanfei Zhong; Caiyi Zhang. 2020. "A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering." Color Research & Application 45, no. 4: 656-670.

Journal article
Published: 28 November 2019 in Remote Sensing
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Local climate zones (LCZ) have become a generic criterion for climate analysis among global cities, as they can describe not only the urban climate but also the morphology inside the city. LCZ mapping based on the remote sensing classification method is a fundamental task, and the protocol proposed by the World Urban Database and Access Portal Tools (WUDAPT) project, which consists of random forest classification and filter-based spatial smoothing, is the most common approach. However, the classification and spatial smoothing lack a unified framework, which causes the appearance of small, isolated areas in the LCZ maps. In this paper, a spatial-contextual information-based self-training classification framework (SCSF) is proposed to solve this LCZ classification problem. In SCSF, conditional random field (CRF) is used to integrate the classification and spatial smoothing processing into one model and a self-training method is adopted, considering that the lack of sufficient expert-labeled training samples is always a big issue, especially for the complex LCZ scheme. Moreover, in the unary potentials of CRF modeling, pseudo-label selection using a self-training process is used to train the classifier, which fuses the regional spatial information through segmentation and the local neighborhood information through moving windows to provide a more reliable probabilistic classification map. In the pairwise potential function, SCSF can effectively improve the classification accuracy by integrating the spatial-contextual information through CRF. The experimental results prove that the proposed framework is efficient when compared to the traditional mapping product of WUDAPT in LCZ classification.

ACS Style

Nan Zhao; Ailong Ma; Yanfei Zhong; Liqin Cao. Self-Training Classification Framework with Spatial-Contextual Information for Local Climate Zones. Remote Sensing 2019, 11, 2828 .

AMA Style

Nan Zhao, Ailong Ma, Yanfei Zhong, Liqin Cao. Self-Training Classification Framework with Spatial-Contextual Information for Local Climate Zones. Remote Sensing. 2019; 11 (23):2828.

Chicago/Turabian Style

Nan Zhao; Ailong Ma; Yanfei Zhong; Liqin Cao. 2019. "Self-Training Classification Framework with Spatial-Contextual Information for Local Climate Zones." Remote Sensing 11, no. 23: 2828.

Journal article
Published: 25 October 2019 in IEEE Access
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ACS Style

Zhijiang Li; Haonan Zhu; Liqin Cao; Lei Jiao; Yanfei Zhong; Ailong Ma. Face Inpainting via Nested Generative Adversarial Networks. IEEE Access 2019, 7, 155462 -155471.

AMA Style

Zhijiang Li, Haonan Zhu, Liqin Cao, Lei Jiao, Yanfei Zhong, Ailong Ma. Face Inpainting via Nested Generative Adversarial Networks. IEEE Access. 2019; 7 ():155462-155471.

Chicago/Turabian Style

Zhijiang Li; Haonan Zhu; Liqin Cao; Lei Jiao; Yanfei Zhong; Ailong Ma. 2019. "Face Inpainting via Nested Generative Adversarial Networks." IEEE Access 7, no. : 155462-155471.

Journal article
Published: 16 October 2019 in Remote Sensing
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The formation of black-odor water in urban rivers has a long history. It not only seriously affects the image of the city, but also easily breeds germs and damages the urban habitat. The prevention and treatment of urban black-odor water have long been important topics nationwide. “Action Plan for Prevention and Control of Water Pollution” issued by the State Council shows Chinese government’s high attention to this issue. However, treatment and monitoring are inextricably linked. There are few studies on the large-scale monitoring of black-odor water, especially the cases of using unmanned aerial vehicle (UAV) to efficiently and accurately monitor the spatial distribution of urban river pollution. Therefore, in order to get rid of the limitations of traditional ground sampling to evaluate the point source pollution of rivers, the UAV-borne hyperspectral imagery was applied in this paper. It is hoped to grasp the pollution status of the entire river as soon as possible from the surface. However, the retrieval of multiple water quality parameters will lead to cumulative errors, so the Nemerow comprehensive pollution index (NCPI) is introduced to characterize the pollution level of urban water. In the paper, the retrieval results of six regression models including gradient boosting decision tree regression (GBDTR) were compared, trying to find a regression model for the retrieval NCPI in the current scenario. In the first study area, the retrieval accuracy of the training dataset (adjusted_R2 = 0.978), and test dataset (adjusted_R2 = 0.974) was higher than that of the other regression models. Although the retrieval effect of random forest is similar to that of GBDTR in both training accuracy and image inversion, it is more computationally expensive. Finally, the spatial distribution graphs of NCPI and its technical feasibility in monitoring pollution sources were investigated, in combination with field observations.

ACS Style

Lifei Wei; Can Huang; Zhengxiang Wang; Xiaocheng Zhou; Liqin Cao. Monitoring of Urban Black-Odor Water Based on Nemerow Index and Gradient Boosting Decision Tree Regression Using UAV-Borne Hyperspectral Imagery. Remote Sensing 2019, 11, 2402 .

AMA Style

Lifei Wei, Can Huang, Zhengxiang Wang, Xiaocheng Zhou, Liqin Cao. Monitoring of Urban Black-Odor Water Based on Nemerow Index and Gradient Boosting Decision Tree Regression Using UAV-Borne Hyperspectral Imagery. Remote Sensing. 2019; 11 (20):2402.

Chicago/Turabian Style

Lifei Wei; Can Huang; Zhengxiang Wang; Xiaocheng Zhou; Liqin Cao. 2019. "Monitoring of Urban Black-Odor Water Based on Nemerow Index and Gradient Boosting Decision Tree Regression Using UAV-Borne Hyperspectral Imagery." Remote Sensing 11, no. 20: 2402.

Journal article
Published: 10 September 2019 in Sensors
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: In this study, in order to solve the difficulty of the inversion of soil arsenic (As) content using laboratory and field reflectance spectroscopy, we examined the transferability of the prediction method. Sixty-three soil samples from the Daye city area of the Jianghan Plain region of China were taken and studied in this research. The characteristic wavelengths of soil As content were then extracted from the full bands based on iteratively retaining informative variables (IRIV) coupled with Spearman’s rank correlation analysis (SCA). Firstly, the IRIV algorithm was used to roughly select the original spectral data. Gaussian filtering (GF), first derivative (FD) filtering, and gaussian filtering again (GFA) pretreatments were then used to improve the correlation between the spectra and soil As content. A subset with absolute correlation values greater than 0.6 was then retained as the optimal subset after each pretreatment. Finally, partial least squares regression (PLSR), Bayesian ridge regression (BRR), ridge regression (RR), kernel ridge regression (KRR), support vector machine regression (SVMR), eXtreme gradient boosting (XGBoost) regression, and random forest regression (RFR) models were used to estimate the soil As values using the different characteristic variables. The results showed that, compared with the traditional method based on IRIV, using the characteristic bands selected by the IRIV-SCA method can effectively improve the prediction accuracy of the models. For the laboratory spectra experiment stage, the six most representative characteristic bands were selected. The performance of IRIV-SCA-SVMR was found to be the best, with the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) in the validation set being 0.97, 0.22, and 0.11, respectively. For the field spectra experiment stage, the 12 most representative characteristic bands were selected. The performance of IRIV-SCA-XGBoost was found to be the best, with the R2, RMSE, and MAE in the validation set being 0.83, 0.35, and 0.29, respectively. The accuracy and stability of the inversion of soil As content are significantly improved by the use of the proposed method, and the method could be used to provide accurate data for decision support for the treatment and recovery of As pollution over a large area.

ACS Style

Lifei Wei; Ziran Yuan; Ming Yu; Can Huang; Liqin Cao; Wei; Yuan; Yu; Cao. Estimation of Arsenic Content in Soil Based on Laboratory and Field Reflectance Spectroscopy. Sensors 2019, 19, 3904 .

AMA Style

Lifei Wei, Ziran Yuan, Ming Yu, Can Huang, Liqin Cao, Wei, Yuan, Yu, Cao. Estimation of Arsenic Content in Soil Based on Laboratory and Field Reflectance Spectroscopy. Sensors. 2019; 19 (18):3904.

Chicago/Turabian Style

Lifei Wei; Ziran Yuan; Ming Yu; Can Huang; Liqin Cao; Wei; Yuan; Yu; Cao. 2019. "Estimation of Arsenic Content in Soil Based on Laboratory and Field Reflectance Spectroscopy." Sensors 19, no. 18: 3904.

Journal article
Published: 22 August 2019 in Signal Processing: Image Communication
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Color transfer in image processing usually suffers from misleading color mapping and loss of details. This paper presents a novel directive local color transfer method based on dynamic look-up table (D-DLT) to solve these problems in two steps. First, a directive mapping between the source and the reference image is established based on the salient detection and the color clusters to obtain directive color transfer intention. Then, dynamic look-up tables are created according to the color clusters to preserve the details, which can suppress pseudo contours and avoid detail loss. Subjective and objective assessments are presented to verify the feasibility and the availability of the proposed approach. Experimental results demonstrate that our proposed method has better performance on natural color images than classical color transfer algorithms. Furthermore, the reference image can be extended to color blocks instead of images.

ACS Style

Zhijiang Li; Zhenshan Tan; Liqin Cao; Hu Chen; Lei Jiao; Yanfei Zhong. Directive local color transfer based on dynamic look-up table. Signal Processing: Image Communication 2019, 79, 1 -12.

AMA Style

Zhijiang Li, Zhenshan Tan, Liqin Cao, Hu Chen, Lei Jiao, Yanfei Zhong. Directive local color transfer based on dynamic look-up table. Signal Processing: Image Communication. 2019; 79 ():1-12.

Chicago/Turabian Style

Zhijiang Li; Zhenshan Tan; Liqin Cao; Hu Chen; Lei Jiao; Yanfei Zhong. 2019. "Directive local color transfer based on dynamic look-up table." Signal Processing: Image Communication 79, no. : 1-12.

Journal article
Published: 16 August 2017 in Remote Sensing
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The rapid development of high spatial resolution (HSR) remote sensing imagery techniques not only provide a considerable amount of datasets for scene classification tasks but also request an appropriate scene classification choice when facing with finite labeled samples. AlexNet, as a relatively simple convolutional neural network (CNN) architecture, has obtained great success in scene classification tasks and has been proven to be an excellent foundational hierarchical and automatic scene classification technique. However, current HSR remote sensing imagery scene classification datasets always have the characteristics of small quantities and simple categories, where the limited annotated labeling samples easily cause non-convergence. For HSR remote sensing imagery, multi-scale information of the same scenes can represent the scene semantics to a certain extent but lacks an efficient fusion expression manner. Meanwhile, the current pre-trained AlexNet architecture lacks a kind of appropriate supervision for enhancing the performance of this model, which easily causes overfitting. In this paper, an improved pre-trained AlexNet architecture named pre-trained AlexNet-SPP-SS has been proposed, which incorporates the scale pooling—spatial pyramid pooling (SPP) and side supervision (SS) to improve the above two situations. Extensive experimental results conducted on the UC Merced dataset and the Google Image dataset of SIRI-WHU have demonstrated that the proposed pre-trained AlexNet-SPP-SS model is superior to the original AlexNet architecture as well as the traditional scene classification methods.

ACS Style

Xiaobing Han; Yanfei Zhong; Liqin Cao; Liangpei Zhang. Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification. Remote Sensing 2017, 9, 848 .

AMA Style

Xiaobing Han, Yanfei Zhong, Liqin Cao, Liangpei Zhang. Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification. Remote Sensing. 2017; 9 (8):848.

Chicago/Turabian Style

Xiaobing Han; Yanfei Zhong; Liqin Cao; Liangpei Zhang. 2017. "Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification." Remote Sensing 9, no. 8: 848.

Conference paper
Published: 22 March 2017 in Lecture Notes in Electrical Engineering
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We present a new colorization method to assign color to a grayscale image based on a reference color image using texture descriptors and Improved Simple Linear Iterative Clustering (ISLIC). Firstly, the pixels of images are classified using Support Vector Machine (SVM) according to texture descriptors, mean luminance, entropy, homogeneity, correlation, and local binary pattern (LBP) features. Then, the grayscale image and the color image are segmented into superpixels, which are obtained by ISLIC to produce more uniform and regularly shaped superpixels than those obtained by SLIC, and the classified images are further post-processed combined with superpixles for removing erroneous classifications. Thereafter, each pixel of the grayscale image is assigned with a color obtained from the color image following a predefined matching metric based on the superpixels and the classes. Experimental results show that our proposed approach is effective and has a better colorization in naturalness compared with Welsh algorithm and unimproved SLIC strategy method.

ACS Style

Liqin Cao; Lei Jiao; Zhijiang Li. Image Colorization Method Using Texture Descriptors and ISLIC Segmentation. Lecture Notes in Electrical Engineering 2017, 9 -15.

AMA Style

Liqin Cao, Lei Jiao, Zhijiang Li. Image Colorization Method Using Texture Descriptors and ISLIC Segmentation. Lecture Notes in Electrical Engineering. 2017; ():9-15.

Chicago/Turabian Style

Liqin Cao; Lei Jiao; Zhijiang Li. 2017. "Image Colorization Method Using Texture Descriptors and ISLIC Segmentation." Lecture Notes in Electrical Engineering , no. : 9-15.