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Landsat remote sensing images are widely used in fields such as land surface temperature retrieval, urban expansion, and urban heat islands, due to their high spatial resolution and the availability of long time series data. Long-term land surface temperature (LST) change analysis usually requires comprehensive utilization of remote sensing data from different sensors, such as Landsat 7, Landsat 8. A LST retrieval algorithm with generalization for Landsat thermal infrared image can effectively improve the reliability of long-term analysis using multi-source images. In order to evaluate the performances of different LST retrieval methods for Landsat images, a new strategy based on regional consistency was proposed in this paper so that different LST retrieval algorithms can be compared with each other without utilizing reference data from ground observation. The general hypothesis is that there is a significant positive correlation between the obtained Landsat 7 and Landsat 8 LST products with adjacent imaging time, similar imaging environment for the identical area. Firstly, the Landsat 7 and Landsat 8 image pairs from Shenzhen were selected under aforementioned constraints. Secondly, four representative LST retrieval methods, radiative transfer equation method (RTEM), image-based method (IBM), mono-window algorithm (MWA) and single-channel algorithm (SCA) were used to generate the LST products from the Landsat 7 and Landsat 8 image pairs respectively. Lastly, the correlation between the Landsat 7 and Landsat 8 LST products from different methods can be calculated as different indexes, including the goodness of fit, Pearson correlation coefficient and Euclidean distance. It is convincing that the optimal LST retrieval method should exhibit a higher regional consistency between the Landsat 7 and Landsat 8 LST product pair given the certain area. The experimental results show that the radiative transfer equation method generates the highest correlation and it is considered as a suitable option for long-term LST research with multi-source Landsat datasets.
Xu Qingyu; Xu Xiong; Xie Huan; Zhang Xiaochun; Huang Yuting. A New Strategy for Comparison of Land Surface Temperature Retrieval Methods with Landsat Remote Sensing Images Considering Regional Consistency. IOP Conference Series: Earth and Environmental Science 2021, 687, 012166 .
AMA StyleXu Qingyu, Xu Xiong, Xie Huan, Zhang Xiaochun, Huang Yuting. A New Strategy for Comparison of Land Surface Temperature Retrieval Methods with Landsat Remote Sensing Images Considering Regional Consistency. IOP Conference Series: Earth and Environmental Science. 2021; 687 (1):012166.
Chicago/Turabian StyleXu Qingyu; Xu Xiong; Xie Huan; Zhang Xiaochun; Huang Yuting. 2021. "A New Strategy for Comparison of Land Surface Temperature Retrieval Methods with Landsat Remote Sensing Images Considering Regional Consistency." IOP Conference Series: Earth and Environmental Science 687, no. 1: 012166.
In large‐scale block adjustment of high‐resolution satellite imagery (HRSI) without ground control points (GCPs), the selection of virtual control points (VCPs) is an important factor in determining the block adjustment accuracy. The traditional VCP generation method is based on a regular grid and uses the initial rational polynomial coefficient (RPC) files but without considering topographic factors. To further improve the accuracy, this paper proposes an approach for the optimal selection of VCPs with planar constraints, which ensures that the VCPs are mostly in areas with small elevation differences and are not influenced by vegetation and buildings. A three‐step approach generates the 3D coordinates of tie points based on forward intersection, calculates their mean values and finally selects optimal VCPs using a threshold value. Experiments conducted using ZY3‐01 satellite images show the accuracy of the proposed method without GCPs outperforms the existing method.
Xiaohua Tong; Qing Fu; Shijie Liu; Hanyu Wang; Zhen Ye; Yanmin Jin; Peng Chen; Xiong Xu; Chao Wang; Sicong Liu; Zhonghua Hong; Kuifeng Luan. Optimal selection of virtual control points with planar constraints for large‐scale block adjustment of satellite imagery. The Photogrammetric Record 2020, 35, 487 -508.
AMA StyleXiaohua Tong, Qing Fu, Shijie Liu, Hanyu Wang, Zhen Ye, Yanmin Jin, Peng Chen, Xiong Xu, Chao Wang, Sicong Liu, Zhonghua Hong, Kuifeng Luan. Optimal selection of virtual control points with planar constraints for large‐scale block adjustment of satellite imagery. The Photogrammetric Record. 2020; 35 (172):487-508.
Chicago/Turabian StyleXiaohua Tong; Qing Fu; Shijie Liu; Hanyu Wang; Zhen Ye; Yanmin Jin; Peng Chen; Xiong Xu; Chao Wang; Sicong Liu; Zhonghua Hong; Kuifeng Luan. 2020. "Optimal selection of virtual control points with planar constraints for large‐scale block adjustment of satellite imagery." The Photogrammetric Record 35, no. 172: 487-508.
Subpixel mapping (SPM) is a useful technique that can interpret the spatial distribution inside mixed pixels and produce a finer-resolution classification map for hyperspectral remote-sensing imagery. However, SPM is essentially an ill-posed problem that requires additional information to produce the unique solution. The limited information of a single image is insufficient to make the mapping problem well posed, whereas the complementary spatial information of multiple shifted images is able to reduce the uncertainty and generate an accurate map. The maximum a posteriori model is a feasible way to incorporate auxiliary information for SPM with multiple shifted images, but it introduces a sensitive regularization parameter, which is difficult to preset. Furthermore, the fixed parameter in the iterations influences the incorporation of the multiple images and the spatial prior. In this article, to address these issues, a multiobjective SPM framework for use with multiple shifted hyperspectral images (MOMSM) is proposed. In the proposed algorithm, a multiobjective model consisting of two objective functions, i.e., data fidelity and spatial prior terms, is constructed to transform the SPM into a multiobjective optimization problem, to get rid of the sensitive regularization parameter. To simultaneously optimize the two objective functions, a multiobjective memetic algorithm with a local search operator and an adaptive global replacement strategy is proposed. The multiple images and spatial information can be dynamically fused and the optimal mapping solution with a good balance between the two objectives can be finally obtained. Experiments conducted on both synthetic and real data sets confirm that the proposed method outperforms the other tested SPM algorithms.
Mi Song; Yanfei Zhong; Ailong Ma; Xiong Xu; Liangpei Zhang. Multiobjective Subpixel Mapping With Multiple Shifted Hyperspectral Images. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 8176 -8191.
AMA StyleMi Song, Yanfei Zhong, Ailong Ma, Xiong Xu, Liangpei Zhang. Multiobjective Subpixel Mapping With Multiple Shifted Hyperspectral Images. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (11):8176-8191.
Chicago/Turabian StyleMi Song; Yanfei Zhong; Ailong Ma; Xiong Xu; Liangpei Zhang. 2020. "Multiobjective Subpixel Mapping With Multiple Shifted Hyperspectral Images." IEEE Transactions on Geoscience and Remote Sensing 58, no. 11: 8176-8191.
Accurate land cover mapping and change analysis is essential for natural resource management and ecosystem monitoring. GlobeLand30 is a global land cover product from China with 30 m resolution that provides reliable data for many international scientific programs. Few studies have focused on systematically implementing this global land cover product in regional studies. Therefore, this paper presents an object-based extended change vector analysis (ECVA_OB) and transfer learning method to update the reginal land cover map using GlobeLand30 product. The method is designed to highlight small and subtle changes through the concept of uncertain area analysis. Updating is carried out by classifying changed objects using a change-detection-based transfer learning method. Land cover changes are analyzed and the factors affecting updating results are explored. The method was tested with data from Shanghai, China, a city that has experienced significant changes in the past decade. The experimental results show that: (1) the change detection and classification accuracy of the proposed method are 83.30% and 78.77%, respectively, which are significantly better than the values obtained for the multithreshold change vector analysis (MCVA) and the multithreshold change vector analysis and support vector machine (MCVA + SVM) methods; (2) the updated results agree well with GlobeLand30 2010, especially for cultivated land and artificial surfaces, indicating the effectiveness of the proposed method; (3) the most significant changes over the past decade in Shanghai were from cultivated land to artificial surfaces, and the total area containing artificial surfaces in Shanghai increased by about 55% from 2000 to 2011. The factors affecting the updating results are also discussed, which be attributed to the classification accuracy of the base image, extended change vector analysis, and object-based image analysis.
Haiyan Pan; Xiaohua Tong; Xiong Xu; Xin Luo; Yanmin Jin; Huan Xie; Binbin Li. Updating of Land Cover Maps and Change Analysis Using GlobeLand30 Product: A Case Study in Shanghai Metropolitan Area, China. Remote Sensing 2020, 12, 3147 .
AMA StyleHaiyan Pan, Xiaohua Tong, Xiong Xu, Xin Luo, Yanmin Jin, Huan Xie, Binbin Li. Updating of Land Cover Maps and Change Analysis Using GlobeLand30 Product: A Case Study in Shanghai Metropolitan Area, China. Remote Sensing. 2020; 12 (19):3147.
Chicago/Turabian StyleHaiyan Pan; Xiaohua Tong; Xiong Xu; Xin Luo; Yanmin Jin; Huan Xie; Binbin Li. 2020. "Updating of Land Cover Maps and Change Analysis Using GlobeLand30 Product: A Case Study in Shanghai Metropolitan Area, China." Remote Sensing 12, no. 19: 3147.
Crop type mapping visualizes the spatial distribution patterns and proportions of the cultivated areas with different crop types, and is the basis for subsequent agricultural applications. Understanding the effectiveness of different temporal and spectral features in detailed crop classification can help users optimize temporal window selection and spectral feature space construction in crop type mapping applications. Therefore, in this study, we used time-series Sentinel-2 image data from Yi’an County, Heilongjiang province, China, to analyze the effectiveness of the temporal and spectral features used in three common machine learning classification methods: classification and regression tree (CART) decision tree, Support Vector Machine (SVM), and random forest (RF). For CART and SVM classifiers, the relative importance of the features was reflected by the order and frequency of attributes selected as the node and the square of the model weight. In RF, the change in prediction error as calculated by out of bag data is taken as the measure of feature importance. The standard deviation of the average value of all labeled pixels was used to evaluate the correctness of the unanimous conclusions drawn by these three methodologies. The quantitative evaluation results given by the confusion matrix show that random forest achieved the best overall accuracy, while support vector machine ranked second, and the decision tree algorithm yielded the least accurate classification results. From the perspective of feature importance, making full use of the discriminative information between different crops, and constructing a rational feature space, can help to improve classification accuracy significantly. In detail, the discriminative information between the different crop types is as follows: 1) images at the peak of the crop growth period are crucial in the classification of different crops; 2) the short-wave infrared bands are particularly suitable for fine crop classification; and 3) the red edge bands can effectively assist classification. Finally, our study achieved crop type mapping in the study area with an overall accuracy of 97.85% and a Kappa coefficient of 0.95.
Hongyan Zhang; Jinzhong Kang; Xiong Xu; Liangpei Zhang. Accessing the temporal and spectral features in crop type mapping using multi-temporal Sentinel-2 imagery: A case study of Yi’an County, Heilongjiang province, China. Computers and Electronics in Agriculture 2020, 176, 105618 .
AMA StyleHongyan Zhang, Jinzhong Kang, Xiong Xu, Liangpei Zhang. Accessing the temporal and spectral features in crop type mapping using multi-temporal Sentinel-2 imagery: A case study of Yi’an County, Heilongjiang province, China. Computers and Electronics in Agriculture. 2020; 176 ():105618.
Chicago/Turabian StyleHongyan Zhang; Jinzhong Kang; Xiong Xu; Liangpei Zhang. 2020. "Accessing the temporal and spectral features in crop type mapping using multi-temporal Sentinel-2 imagery: A case study of Yi’an County, Heilongjiang province, China." Computers and Electronics in Agriculture 176, no. : 105618.
In satellite laser altimetry, it is a challenging task to accurately extract peak positions from full waveforms due to the overlapped or weak peaks within the large laser footprints, which substantially affects the subsequent applications. In this paper, to improve the laser ranging resolution and accuracy, we propose a novel approach by combining deconvolution with Gaussian decomposition. The approach is applied in two main phases: 1) the deconvolution is first used to remove the system contribution (the transmit pulse spreading over several nanoseconds, system noise); and 2) Gaussian decomposition is then adopted to extract the peak parameters of each object. Experiments using simulated and ICESat waveforms were conducted to validate and evaluate the proposed approach by comparing it to the benchmark Gaussian decomposition technique. The results indicated that: 1) the combined approach can significantly improve the peak detection rate: the four combined methods found at least 15.8% more echoes in simulated forested areas; and 2) for ICESat waveforms, the quantitative evaluation and visual assessment of the Blind -Gaussian combination obtained more echoes (on average, approximately 2.5 components) than the other combinations (on average, approximately 1.5 and 1.2 components), and the derived relative object heights were very close to the results obtained from airborne LiDAR data. These results confirmed that the Blind -Gaussian combination is more accurate for the range retrieval of vegetated and urbanized landscapes.
Zhijie Zhang; Huan Xie; Xiaohua Tong; Hanwei Zhang; Hong Tang; Binbin Li; Di Wu; Xiaolong Hao; Shijie Liu; Xiong Xu; Sicong Liu; Peng Chen; Yongjiu Feng; Chao Wang; Yanmin Jin. A Combined Deconvolution and Gaussian Decomposition Approach for Overlapped Peak Position Extraction From Large-Footprint Satellite Laser Altimeter Waveforms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 2286 -2303.
AMA StyleZhijie Zhang, Huan Xie, Xiaohua Tong, Hanwei Zhang, Hong Tang, Binbin Li, Di Wu, Xiaolong Hao, Shijie Liu, Xiong Xu, Sicong Liu, Peng Chen, Yongjiu Feng, Chao Wang, Yanmin Jin. A Combined Deconvolution and Gaussian Decomposition Approach for Overlapped Peak Position Extraction From Large-Footprint Satellite Laser Altimeter Waveforms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):2286-2303.
Chicago/Turabian StyleZhijie Zhang; Huan Xie; Xiaohua Tong; Hanwei Zhang; Hong Tang; Binbin Li; Di Wu; Xiaolong Hao; Shijie Liu; Xiong Xu; Sicong Liu; Peng Chen; Yongjiu Feng; Chao Wang; Yanmin Jin. 2020. "A Combined Deconvolution and Gaussian Decomposition Approach for Overlapped Peak Position Extraction From Large-Footprint Satellite Laser Altimeter Waveforms." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 2286-2303.
Xiaohua Tong; Haiyan Pan; Sicong Liu; Binbin Li; Xin Luo; Huan Xie; Xiong Xu. A Novel Approach for Hyperspectral Change Detection Based on Uncertain Area Analysis and Improved Transfer Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 2056 -2069.
AMA StyleXiaohua Tong, Haiyan Pan, Sicong Liu, Binbin Li, Xin Luo, Huan Xie, Xiong Xu. A Novel Approach for Hyperspectral Change Detection Based on Uncertain Area Analysis and Improved Transfer Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 ():2056-2069.
Chicago/Turabian StyleXiaohua Tong; Haiyan Pan; Sicong Liu; Binbin Li; Xin Luo; Huan Xie; Xiong Xu. 2020. "A Novel Approach for Hyperspectral Change Detection Based on Uncertain Area Analysis and Improved Transfer Learning." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. : 2056-2069.
Fourier-based image correlation is a powerful area-based image registration technique, which involves aligning images based on a translation model or similarity model by means of the image information and operation in the frequency domain. In recent years, Fourier-based image correlation has made significant progress and attracted extensive research interest in a variety of applications, especially in the field of photogrammetry and remote sensing, leading to the development of a number of subpixel methods that have improved the accuracy and robustness. However, to date, a detailed review of the literature related to Fourier-based image correlation is still lacking. In this review, we aim at providing a comprehensive overview of the fundamentals, developments, and applications of image registration with Fourier-based image correlation methods. Specifically, this review introduces the principal laws underlying these methods, presents a survey of the existing subpixel methods calculated both in the spatial domain and in the frequency domain, summarizes the major applications from three aspects, and discusses the challenges and possible directions of future research. This review is expected to be beneficial for researchers working in the relevant fields to obtain an insight into the current state of the art, to develop new variants, to explore potential applications, and to suggest promising future trends of image registration with Fourier-based image correlation.
Xiaohua Tong; Zhen Ye; Yusheng Xu; Sa Gao; Huan Xie; Qian Du; Shijie Liu; Xiong Xu; Sicong Liu; Kuifeng Luan; Uwe Stilla. Image Registration With Fourier-Based Image Correlation: A Comprehensive Review of Developments and Applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2019, 12, 4062 -4081.
AMA StyleXiaohua Tong, Zhen Ye, Yusheng Xu, Sa Gao, Huan Xie, Qian Du, Shijie Liu, Xiong Xu, Sicong Liu, Kuifeng Luan, Uwe Stilla. Image Registration With Fourier-Based Image Correlation: A Comprehensive Review of Developments and Applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019; 12 (10):4062-4081.
Chicago/Turabian StyleXiaohua Tong; Zhen Ye; Yusheng Xu; Sa Gao; Huan Xie; Qian Du; Shijie Liu; Xiong Xu; Sicong Liu; Kuifeng Luan; Uwe Stilla. 2019. "Image Registration With Fourier-Based Image Correlation: A Comprehensive Review of Developments and Applications." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 10: 4062-4081.
High‐speed cameras are able to effectively and efficiently obtain the location of moving objects over time in many practical applications. In order to meet the requirement for fast computing and processing in the object‐tracking process, this paper adopts parallel computing to process multiple video‐image sequences by using open multi‐processing (OpenMP) and single‐instruction multiple data (SIMD) simultaneously, combined with a coarse‐to‐fine matching method. Experimental results showed that the proposed method: (1) has a high accuracy compared with other methods; (2) demonstrates a higher computational efficiency than other methods; and (3) has an efficiency running on a laptop equivalent to other methods running on a computer workstation.
Xiaohua Tong; Shouzhu Zheng; Sa Gao; Sicong Liu; Peng Chen; Chengcheng Guo; Shijie Liu; Huan Xie; Yanmin Jin; Zhen Ye; Xiong Xu; Chao Wei; Lintao Hu. Acceleration of object tracking in high‐speed videogrammetry using a parallel OpenMP and SIMD strategy. The Photogrammetric Record 2019, 34, 174 -197.
AMA StyleXiaohua Tong, Shouzhu Zheng, Sa Gao, Sicong Liu, Peng Chen, Chengcheng Guo, Shijie Liu, Huan Xie, Yanmin Jin, Zhen Ye, Xiong Xu, Chao Wei, Lintao Hu. Acceleration of object tracking in high‐speed videogrammetry using a parallel OpenMP and SIMD strategy. The Photogrammetric Record. 2019; 34 (166):174-197.
Chicago/Turabian StyleXiaohua Tong; Shouzhu Zheng; Sa Gao; Sicong Liu; Peng Chen; Chengcheng Guo; Shijie Liu; Huan Xie; Yanmin Jin; Zhen Ye; Xiong Xu; Chao Wei; Lintao Hu. 2019. "Acceleration of object tracking in high‐speed videogrammetry using a parallel OpenMP and SIMD strategy." The Photogrammetric Record 34, no. 166: 174-197.
Full‐field deformation measurement of specific materials is an important issue in civil engineering materials testing. This paper presents a flexible videogrammetric scheme to measure full‐field deformation of an artificial rock‐like material under uniaxial compression. In this scheme, two high‐speed cameras were used to measure the spatial morphological changes of the material surface, which was sprayed with a speckle pattern. A robust self‐adaptive window matching strategy is then proposed to extract accurate displacements of tracking points. Finally, three‐dimensional (3D) point clouds and full‐field deformation can be calculated by photogrammetric and spatiotemporal analysis. Simulation and empirical tests demonstrated that the self‐adaptive window matching strategy used with high‐speed videogrammetric speckle image sequences can detect more subtle deformation and avoid more mismatches than a fixed‐window matching strategy.
Sa Gao; Xiaohua Tong; Peng Chen; Zhen Ye; Ouling Hu; Benkang Wang; Cheng Zhao; Shijie Liu; Huan Xie; Yanmin Jin; Xiong Xu; Sicong Liu; Chao Wei. Full‐field deformation measurement by videogrammetry using self‐adaptive window matching. The Photogrammetric Record 2019, 34, 36 -62.
AMA StyleSa Gao, Xiaohua Tong, Peng Chen, Zhen Ye, Ouling Hu, Benkang Wang, Cheng Zhao, Shijie Liu, Huan Xie, Yanmin Jin, Xiong Xu, Sicong Liu, Chao Wei. Full‐field deformation measurement by videogrammetry using self‐adaptive window matching. The Photogrammetric Record. 2019; 34 (165):36-62.
Chicago/Turabian StyleSa Gao; Xiaohua Tong; Peng Chen; Zhen Ye; Ouling Hu; Benkang Wang; Cheng Zhao; Shijie Liu; Huan Xie; Yanmin Jin; Xiong Xu; Sicong Liu; Chao Wei. 2019. "Full‐field deformation measurement by videogrammetry using self‐adaptive window matching." The Photogrammetric Record 34, no. 165: 36-62.
The Fourier-based image correlation technique has been widely concerned due to its accuracy, efficiency, and robustness to image contrast and brightness. Accordingly, a variety of subpixel methods have been proposed. However, the detailed subpixel-level influence of the complicated radiometric variations has yet to be investigated, and few corresponding improvements have been made. This paper presents a novel illumination-robust subpixel Fourier-based image correlation method based on phase congruency. Both the magnitude and orientation information of the phase congruency features are adopted to construct a structural image representation. The image representation is then embedded into the correlation scheme of the subpixel methods, either by linear phase estimation in the frequency domain or by kernel fitting in the spatial domain, achieving two improved subpixel methods. The proposed methods integrate the advantages of the structural image representation and the original correlation scheme, and make full use of both global and local phase information to achieve illumination-robust correlation. Experiments undertaken with both simulated and real radiometric differences were carried out with ground-truth subpixel shifts. The performances of the proposed methods and the other state-of-the-art subpixel Fourier-based correlation methods were evaluated and compared. The experimental results indicate that the proposed methods outperform the other methods in the presence of diverse radiometric variations, in both accuracy and robustness.
Zhen Ye; Xiaohua Tong; Shouzhu Zheng; Chengcheng Guo; Sa Gao; Shijie Liu; Xiong Xu; Yanmin Jin; Huan Xie; Sicong Liu; Peng Chen. Illumination-Robust Subpixel Fourier-Based Image Correlation Methods Based on Phase Congruency. IEEE Transactions on Geoscience and Remote Sensing 2018, 57, 1995 -2008.
AMA StyleZhen Ye, Xiaohua Tong, Shouzhu Zheng, Chengcheng Guo, Sa Gao, Shijie Liu, Xiong Xu, Yanmin Jin, Huan Xie, Sicong Liu, Peng Chen. Illumination-Robust Subpixel Fourier-Based Image Correlation Methods Based on Phase Congruency. IEEE Transactions on Geoscience and Remote Sensing. 2018; 57 (4):1995-2008.
Chicago/Turabian StyleZhen Ye; Xiaohua Tong; Shouzhu Zheng; Chengcheng Guo; Sa Gao; Shijie Liu; Xiong Xu; Yanmin Jin; Huan Xie; Sicong Liu; Peng Chen. 2018. "Illumination-Robust Subpixel Fourier-Based Image Correlation Methods Based on Phase Congruency." IEEE Transactions on Geoscience and Remote Sensing 57, no. 4: 1995-2008.
Xiong Xu; Xiaohua Tong; Antonio Plaza; Jun Li; Yanfei Zhong; Huan Xie; Liangpei Zhang. A New Spectral-Spatial Sub-Pixel Mapping Model for Remotely Sensed Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing 2018, 56, 6763 -6778.
AMA StyleXiong Xu, Xiaohua Tong, Antonio Plaza, Jun Li, Yanfei Zhong, Huan Xie, Liangpei Zhang. A New Spectral-Spatial Sub-Pixel Mapping Model for Remotely Sensed Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing. 2018; 56 (11):6763-6778.
Chicago/Turabian StyleXiong Xu; Xiaohua Tong; Antonio Plaza; Jun Li; Yanfei Zhong; Huan Xie; Liangpei Zhang. 2018. "A New Spectral-Spatial Sub-Pixel Mapping Model for Remotely Sensed Hyperspectral Imagery." IEEE Transactions on Geoscience and Remote Sensing 56, no. 11: 6763-6778.
During recent years, many regularization techniques have been proposed to deal with ill-posed problems related to hyperspectral image classification, in which the limited number of training samples contrasts with the very high spectral dimensionality. However, the intrinsic structure of a hyperspectral image often depends on the specific scene and spectrometer, although regularizers like Ridge, LASSO, etc, have been widely used in practical applications. Instead of imposing these regularizers to the probabilistic output of a classifier, this work evaluates the use of extreme learning machines (ELM) with output weights of a single-hidden layer feed-forward neural network (SLFN) regularized with Ridge and LASSO priors, respectively. Experimental results with several real hyperspectral images are conducted to compare the performance and adaptation of these two regularizers with the the original ELM in classification scenarios.
Juan M. Haut; Yi Liu; Mercedes E. Paoletti; Xiong Xu; Javier Plaza; Antonio Plaza. Evaluation of Different Regularization Methods for the Extreme Learning Machine Applied to Hyperspectral Images. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 3603 -3606.
AMA StyleJuan M. Haut, Yi Liu, Mercedes E. Paoletti, Xiong Xu, Javier Plaza, Antonio Plaza. Evaluation of Different Regularization Methods for the Extreme Learning Machine Applied to Hyperspectral Images. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():3603-3606.
Chicago/Turabian StyleJuan M. Haut; Yi Liu; Mercedes E. Paoletti; Xiong Xu; Javier Plaza; Antonio Plaza. 2018. "Evaluation of Different Regularization Methods for the Extreme Learning Machine Applied to Hyperspectral Images." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 3603-3606.
Hyperspectral remote sensing image (HSI) clustering can be defined as the process of segmenting pixels into different sets that satisfy the requirement that the differences between sets are much greater than the differences within sets. According to the fast density peak-based clustering algorithm, we propose an unsupervised HSI clustering method based on the density of pixels in the spectral space and the distance between pixels. For the metric of the density, we present an adaptive-bandwidth probability density function using pixel numbers as the input and the calculated pixel local density as the output, which determines the bandwidth on the basis of the Gaussian assumption. For the metric of the distance, in order to obtain a pixel-level spectral distance, we calculate the Euclidean distance between pixel vectors from the multiple bands. In the proposed approach: 1) use the least-squares method for the curve fitting of the two results; 2) eliminate outliers based on the Pauta criterion; 3) adopt regression calculation; and 4) obtain the cluster centers according to the classification criteria of the local density and the distance between pixel vectors. The other noncluster center points are clustered based on their similarities with the cluster centers by iteration. Finally, we compare the results with those of other unsupervised clustering methods and the reference data sets.
Huan Xie; Ang Zhao; Shengyu Huang; Jie Han; Sicong Liu; Xiong Xu; Xin Luo; Haiyan Pan; Qian Du; Xiaohua Tong. Unsupervised Hyperspectral Remote Sensing Image Clustering Based on Adaptive Density. IEEE Geoscience and Remote Sensing Letters 2018, 15, 632 -636.
AMA StyleHuan Xie, Ang Zhao, Shengyu Huang, Jie Han, Sicong Liu, Xiong Xu, Xin Luo, Haiyan Pan, Qian Du, Xiaohua Tong. Unsupervised Hyperspectral Remote Sensing Image Clustering Based on Adaptive Density. IEEE Geoscience and Remote Sensing Letters. 2018; 15 (4):632-636.
Chicago/Turabian StyleHuan Xie; Ang Zhao; Shengyu Huang; Jie Han; Sicong Liu; Xiong Xu; Xin Luo; Haiyan Pan; Qian Du; Xiaohua Tong. 2018. "Unsupervised Hyperspectral Remote Sensing Image Clustering Based on Adaptive Density." IEEE Geoscience and Remote Sensing Letters 15, no. 4: 632-636.
The performance of scene classification relies heavily on the spatial and structural features that are extracted from high spatial resolution remote-sensing images. Existing approaches, however, are limited in adequately exploiting latent relationships between scene images. Aiming to decrease the distances between intraclass images and increase the distances between interclass images, we propose a latent relationship learning framework that integrates an adaptive graph with the constraints of the feature space and label propagation for high-resolution aerial image classification. To describe the latent relationships among scene images in the framework, we construct an adaptive graph that is embedded into the constrained joint space for features and labels. To remove redundant information and improve the computational efficiency, subspace learning is introduced to assist in the latent relationship learning. To address out-of-sample data, linear regression is adopted to project the semisupervised classification results onto a linear classifier. Learning efficiency is improved by minimizing the objective function via the linearized alternating direction method with an adaptive penalty. We test our method on three widely used aerial scene image data sets. The experimental results demonstrate the superior performance of our method over the state-of-the-art algorithms in aerial scene image classification.
Yuebing Wang; Liqiang Zhang; Xiaohua Tong; Feiping Nie; Haiyang Huang; Jie Mei. LRAGE: Learning Latent Relationships With Adaptive Graph Embedding for Aerial Scene Classification. IEEE Transactions on Geoscience and Remote Sensing 2017, 56, 621 -634.
AMA StyleYuebing Wang, Liqiang Zhang, Xiaohua Tong, Feiping Nie, Haiyang Huang, Jie Mei. LRAGE: Learning Latent Relationships With Adaptive Graph Embedding for Aerial Scene Classification. IEEE Transactions on Geoscience and Remote Sensing. 2017; 56 (2):621-634.
Chicago/Turabian StyleYuebing Wang; Liqiang Zhang; Xiaohua Tong; Feiping Nie; Haiyang Huang; Jie Mei. 2017. "LRAGE: Learning Latent Relationships With Adaptive Graph Embedding for Aerial Scene Classification." IEEE Transactions on Geoscience and Remote Sensing 56, no. 2: 621-634.
High-resolution satellite images (HRSIs) obtained from linear array charge-coupled device sensors always suffer from geometric instability in the presence of attitude jitter. Therefore, detection and compensation of spacecraft attitude jitter in both the cross-track and along-track directions are crucial to improve the geometric accuracy of HRSIs. A number of reports have been made on the detection and estimation of cross-track attitude jitter. However, the detection of the attitude jitter in the along-track direction is more complicated due to the impact of topographic change. This paper presents a novel approach to achieve accurate estimation of the along-track attitude jitter by eliminating the influence of topographic information based on the back-projection residuals of three-line-array (TLA) images. The principle of detection and estimation of along-track attitude jitter is described, and the proposed approach consists of three main components as follows: 1) dense image matching of the TLA images using a comprehensive matching strategy; 2) detection of the back-projection residuals in the line direction caused by attitude jitter; and 3) estimation of the along-track attitude jitter from the back-projection residuals using a genetic algorithm. Experiments were conducted using China's Ziyuan-3 (ZY-3) TLA images, and the experimental results reveal that the frequency of the attitude jitter in the along-track direction ranges between 0.6 and 0.7 Hz, which is consistent with the frequency in the cross-track direction observed in our previous study. In addition, a comparison of the results of the proposed approach with those from direct attitude observations shows good consistency, with as little as 0.1-pixel disparity, which demonstrates the feasibility and reliability of the proposed approach. Furthermore, the geometric accuracy is further improved from a pixel level to a subpixel level and the periodic trend is removed with the compensation of the estimated attitude jitter in addition to the conventional affine compensation, which validates the potential of the proposed approach for geometric accuracy improvement with ZY-3 TLA images.
Xiaohua Tong; Zhen Ye; Lingyun Li; Shijie Liu; Yanmin Jin; Peng Chen; Huan Xie; Songlin Zhang. Detection and Estimation of Along-Track Attitude Jitter From Ziyuan-3 Three-Line-Array Images Based on Back-Projection Residuals. IEEE Transactions on Geoscience and Remote Sensing 2017, 55, 4272 -4284.
AMA StyleXiaohua Tong, Zhen Ye, Lingyun Li, Shijie Liu, Yanmin Jin, Peng Chen, Huan Xie, Songlin Zhang. Detection and Estimation of Along-Track Attitude Jitter From Ziyuan-3 Three-Line-Array Images Based on Back-Projection Residuals. IEEE Transactions on Geoscience and Remote Sensing. 2017; 55 (8):4272-4284.
Chicago/Turabian StyleXiaohua Tong; Zhen Ye; Lingyun Li; Shijie Liu; Yanmin Jin; Peng Chen; Huan Xie; Songlin Zhang. 2017. "Detection and Estimation of Along-Track Attitude Jitter From Ziyuan-3 Three-Line-Array Images Based on Back-Projection Residuals." IEEE Transactions on Geoscience and Remote Sensing 55, no. 8: 4272-4284.
Hyperspectral image (HSI) clustering is generally a challenging task because of the complex spectral-spatial structure. Based on the assumption that all the pixels are sampled from the union of subspaces, recent works have introduced a robust technique—the sparse subspace clustering (SSC) algorithm and its enhanced versions (SSC models incorporating spatial information)—to cluster HSIs, achieving excellent performances. However, these methods are all based on the linear representation model, which conflicts with the well-known nonlinear structure of HSIs and limits their performance to a large degree. In this paper, to overcome these obstacles, we present a new kernel sparse subspace clustering algorithm with a spatial max pooling operation (KSSC-SMP) for hyperspectral remote sensing data interpretation. The proposed approach maps the feature points into a much higher dimensional kernel space to extend the linear sparse subspace clustering model to nonlinear manifolds, which can better fit the complex nonlinear structure of HSIs. With the help of the kernel sparse representation, a more accurate representation coefficient matrix can be obtained. A spatial max pooling operation is utilized for the representation coefficients to generate more discriminant features by integrating the spatial-contextual information, which is essential for the accurate modeling of HSIs. This paper is an extension of our previous conference paper, and a number of enhancements are put forward. The proposed algorithm was evaluated on two well-known hyperspectral data sets—the Salinas image and the University of Pavia image—and the experimental results clearly indicate that the newly developed KSSC-SMP algorithm can obtain very competitive clustering results for HSIs, outperforming the current state-of-the-art clustering methods.
Han Zhai; Hongyan Zhang; Xiong Xu; Liangpei Zhang; Pingxiang Li. Kernel Sparse Subspace Clustering with a Spatial Max Pooling Operation for Hyperspectral Remote Sensing Data Interpretation. Remote Sensing 2017, 9, 335 .
AMA StyleHan Zhai, Hongyan Zhang, Xiong Xu, Liangpei Zhang, Pingxiang Li. Kernel Sparse Subspace Clustering with a Spatial Max Pooling Operation for Hyperspectral Remote Sensing Data Interpretation. Remote Sensing. 2017; 9 (4):335.
Chicago/Turabian StyleHan Zhai; Hongyan Zhang; Xiong Xu; Liangpei Zhang; Pingxiang Li. 2017. "Kernel Sparse Subspace Clustering with a Spatial Max Pooling Operation for Hyperspectral Remote Sensing Data Interpretation." Remote Sensing 9, no. 4: 335.
Rongxing Li; Wenkai Ye; Gang Qiao; Xiaohua Tong; Shijie Liu; Fansi Kong; Xuwen Ma. A New Analytical Method for Estimating Antarctic Ice Flow in the 1960s From Historical Optical Satellite Imagery. IEEE Transactions on Geoscience and Remote Sensing 2017, 55, 2771 -2785.
AMA StyleRongxing Li, Wenkai Ye, Gang Qiao, Xiaohua Tong, Shijie Liu, Fansi Kong, Xuwen Ma. A New Analytical Method for Estimating Antarctic Ice Flow in the 1960s From Historical Optical Satellite Imagery. IEEE Transactions on Geoscience and Remote Sensing. 2017; 55 (5):2771-2785.
Chicago/Turabian StyleRongxing Li; Wenkai Ye; Gang Qiao; Xiaohua Tong; Shijie Liu; Fansi Kong; Xuwen Ma. 2017. "A New Analytical Method for Estimating Antarctic Ice Flow in the 1960s From Historical Optical Satellite Imagery." IEEE Transactions on Geoscience and Remote Sensing 55, no. 5: 2771-2785.
Spectral unmixing and sub-pixel mapping have been used to estimate the proportion and spatial distribution of the different land-cover classes in mixed pixels at a sub-pixel scale. In the past decades, several algorithms were proposed in both categories; however, these two techniques are generally regarded as independent procedures, with most sub-pixel mapping methods using abundance maps generated by spectral unmixing techniques. It should be noted that the utilized abundance map has a strong impact on the performance of the subsequent sub-pixel mapping process. Recently, we built a novel sub-pixel mapping model in combination with the linear spectral mixture model. Therefore, a joint sub-pixel mapping model was established that connects an original (coarser resolution) remotely sensed image with the final sub-pixel result directly. However, this approach focuses on incorporating the spectral information contained in the original image without addressing the spectral endmember variability resulting from variable illumination and environmental conditions. To address this important issue, in this paper we designed a new joint sparse sub-pixel mapping method under the assumption that various representative spectra for each endmember are known a priori and available in a library. In addition, the total variation (TV) regularization was also adopted to exploit the spatial information. The proposed approach was experimentally evaluated using both synthetic and real hyperspectral images, and the obtained results demonstrate that the method can achieve better results by considering the impact of endmember variability when compared with other sub-pixel mapping methods.
Xiong Xu; Xiaohua Tong; Antonio Plaza; Yanfei Zhong; Huan Xie; Liangpei Zhang. Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery. Remote Sensing 2016, 9, 15 .
AMA StyleXiong Xu, Xiaohua Tong, Antonio Plaza, Yanfei Zhong, Huan Xie, Liangpei Zhang. Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery. Remote Sensing. 2016; 9 (1):15.
Chicago/Turabian StyleXiong Xu; Xiaohua Tong; Antonio Plaza; Yanfei Zhong; Huan Xie; Liangpei Zhang. 2016. "Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery." Remote Sensing 9, no. 1: 15.
Water body is a fundamental element in urban ecosystems and water mapping is critical for urban and landscape planning and management. As remote sensing has increasingly been used for water mapping in rural areas, this spatially explicit approach applied in urban area is also a challenging work due to the water bodies mainly distributed in a small size and the spectral confusion widely exists between water and complex features in the urban environment. Water index is the most common method for water extraction at pixel level, and spectral mixture analysis (SMA) has been widely employed in analyzing urban environment at subpixel level recently. In this paper, we introduce an automatic subpixel water mapping method in urban areas using multispectral remote sensing data. The objectives of this research consist of: (1) developing an automatic land-water mixed pixels extraction technique by water index; (2) deriving the most representative endmembers of water and land by utilizing neighboring water pixels and adaptive iterative optimal neighboring land pixel for respectively; (3) applying a linear unmixing model for subpixel water fraction estimation. Specifically, to automatically extract land-water pixels, the locally weighted scatter plot smoothing is firstly used to the original histogram curve of WI image . And then the Ostu threshold is derived as the start point to select land-water pixels based on histogram of the WI image with the land threshold and water threshold determination through the slopes of histogram curve . Based on the previous process at pixel level, the image is divided into three parts: water pixels, land pixels, and mixed land-water pixels. Then the spectral mixture analysis (SMA) is applied to land-water mixed pixels for water fraction estimation at subpixel level. With the assumption that the endmember signature of a target pixel should be more similar to adjacent pixels due to spatial dependence, the endmember of water and land are determined by neighboring pure land or pure water pixels within a distance. To obtaining the most representative endmembers in SMA, we designed an adaptive iterative endmember selection method based on the spatial similarity of adjacent pixels. According to the spectral similarity in a spatial adjacent region, the spectrum of land endmember is determined by selecting the most representative land pixel in a local window, and the spectrum of water endmember is determined by calculating an average of the water pixels in the local window. The proposed hierarchical processing method based on WI and SMA (WISMA) is applied to urban areas for reliability evaluation using the Landsat-8 Operational Land Imager (OLI) images. For comparison, four methods at pixel level and subpixel level were chosen respectively. Results indicate that the water maps generated by the proposed method correspond as closely with the truth water maps with subpixel precision. And the results showed that the WISMA achieved the best performance in water mapping with comprehensive analysis of different accuracy evaluation indexes (RMSE and SE).
Huan Xie; Xin Luo; Xiong Xu; Chen Wang; Haiyan Pan; Xiaohua Tong; Shijie Liu. Estimation of urban surface water at subpixel level from neighborhood pixels using multispectral remote sensing image (Conference Presentation). SPIE Remote Sensing 2016, 10004, 1 .
AMA StyleHuan Xie, Xin Luo, Xiong Xu, Chen Wang, Haiyan Pan, Xiaohua Tong, Shijie Liu. Estimation of urban surface water at subpixel level from neighborhood pixels using multispectral remote sensing image (Conference Presentation). SPIE Remote Sensing. 2016; 10004 ():1.
Chicago/Turabian StyleHuan Xie; Xin Luo; Xiong Xu; Chen Wang; Haiyan Pan; Xiaohua Tong; Shijie Liu. 2016. "Estimation of urban surface water at subpixel level from neighborhood pixels using multispectral remote sensing image (Conference Presentation)." SPIE Remote Sensing 10004, no. : 1.