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| 1 | Current issues in high-resolution earth observation technology显示文摘This paper reviewed the developments of the last ten years in the field of international high-resolution earth observation, and introduced the developmental status and plans for China's high-resolution earth observation program. In addition, this paper expounded the transformation mechanism and procedure from earth observation data to geospatial information and geographical knowledge, and examined the key scientific and technological issues, including earth observation networks, high-precision image positioning, image understanding, automatic spatial information extraction, and focus services. These analyses provide a new impetus for pushing the application of China's high-resolution earth observation system from a 'quantity' to 'quality' change, from China to the world, from providing products to providing online service. | LI DeRen TONG QingXi LI RongXing GONG JianYa ZHANG LiangPei | 2012 | Science China Earth Sciences2012，55，7： | 18 |
| 2 | Splitting and Merging Based Multi-model Fitting for Point Cloud Segmentation显示文摘This paper deals with the massive point cloud segmentation processing technology on the basis of machine vision, which is the second essential factor for the intelligent data processing of three dimensional conformation in digital photogrammetry. In this paper, multi-model fitting method is used to segment the point cloud according to the spatial distribution and spatial geometric structure of point clouds by fitting the point cloud into different geometric primitives models. Because point cloud usually possesses large amount of 3D points, which are uneven distributed over various complex structures, this paper proposes a point cloud segmentation method based on multi-model fitting. Firstly, the pre-segmentation of point cloud is conducted by using the clustering method based on density distribution. And then the follow fitting and segmentation are carried out by using the multi-model fitting method based on split and merging. For the plane and the arc surface, this paper uses different fitting methods, and finally realizing the indoor dense point cloud segmentation. The experimental results show that this method can achieve the automatic segmentation of the point cloud without setting the number of models in advance. Compared with the existing point cloud segmentation methods, this method has obvious advantages in segmentation effect and time cost, and can achieve higher segmentation accuracy. After processed by method proposed in this paper, the point cloud even with large-scale and complex structures can often be segmented into 3D geometric elements with finer and accurate model parameters, which can give rise to an accurate 3D conformation. | Liangpei ZHANG Yun ZHANG Zhenzhong CHEN Peipei XIAO Bin LUO | 2019 | Journal of Geodesy and Geoinformation Science2019，2，2： | 5 |
| 3 | Advances in spaceborne hyperspectral remote sensing in China显示文摘With the maturation of satellite technology,Hyperspectral Remote Sensing(HRS)platforms have developed from the initial ground-based and airborne platforms into spaceborne platforms,which greatly promotes the civil application of HRS imagery in the fields of agriculture,forestry,and environmental monitoring.China is playing an important role in this evolution,especially in recent years,with the successful launch and operation of a series of civil hyper-spectral spacecraft and satellites,including the Shenzhou-3 spacecraft,the Gaofen-5 satellite,the SPARK satellite,the Zhuhai-1 satellite network for environmental and resources monitoring,the FengYun series of satellites for meteorological observation,and the Chang’E series of spacecraft for planetary exploration.The Chinese spaceborne HRS platforms have various new characteristics,such as the wide swath width,high spatial resolution,wide spectral range,hyperspectral satellite networks,and microsatellites.This paper focuses on the recent progress in Chinese spaceborne HRS,from the aspects of the typical satellite systems,the data processing,and the applications.In addition,the future development trends of HRS in China are also discussed and analyzed. | Yanfei Zhong Xinyu Wang Shaoyu Wang Liangpei Zhang | 2021 | Geo-Spatial Information Science2021，24，1： | 5 |
| 4 | A survey on vision-based UAV navigation显示文摘Research on unmanned aerial vehicles(UAV)has been increasingly popular in the past decades,and UAVs have been widely used in industrial inspection,remote sensing for mapping&surveying,rescuing,and so on.Nevertheless,the limited autonomous navigation capability severely hampers the application of UAVs in complex environments,such as GPS-denied areas.Previously,researchers mainly focused on the use of laser or radar sensors for UAV navigation.With the rapid development of computer vision,vision-based methods,which utilize cheaper and more flexible visual sensors,have shown great advantages in the field of UAV navigation.The purpose of this article is to present a comprehensive literature review of the vision-based methods for UAV navigation.Specifically on visual localization and mapping,obstacle avoidance and path planning,which compose the essential parts of visual navigation.Furthermore,throughout this article,we will have an insight into the prospect of the UAV navigation and the challenges to be faced. | Yuncheng Lu Zhucun Xue Gui-Song Xia Liangpei Zhang | 2018 | Geo-Spatial Information Science2018，21，1： | 4 |
| 5 | Review on graph learning for dimensionality reduction of hyperspectral image显示文摘Graph learning is an effective manner to analyze the intrinsic properties of data.It has been widely used in the fields of dimensionality reduction and classification for data.In this paper,we focus on the graph learning-based dimensionality reduction for a hyperspectral image.Firstly,we review the development of graph learning and its application in a hyperspectral image.Then,we mainly discuss several representative graph methods including two manifold learning methods,two sparse graph learning methods,and two hypergraph learning methods.For manifold learning,we analyze neighborhood preserving embedding and locality preserving projections which are two classic manifold learning methods and can be transformed into the form of a graph.For sparse graph,we introduce sparsity preserving graph embedding and sparse graph-based discriminant analysis which can adaptively reveal data structure to construct a graph.For hypergraph learning,we review binary hypergraph and discriminant hyper-Laplacian projection which can represent the high-order relationship of data. | Liangpei Zhang Fulin Luo | 2020 | Geo-Spatial Information Science2020，23，1： | 3 |
| 6 | Spatiotemporal estimation of hourly 2-km ground-level ozone over China based on Himawari-8 using a self-adaptive geospatially local model显示文摘Ground-level ozone(O_(3))is a primary air pollutant,which can greatly harm human health and ecosystems.At present,data fusion frameworks only provided ground-level O_(3) concentrations at coarse spatial(e.g.,10 km)or temporal(e.g.,daily)resolutions.As photochemical pollution continues increasing over China in the last few years,a high-spatial–temporal-resolution product is required to enhance the comprehension of ground-level O_(3) formation mechanisms.To address this issue,our study creatively explores a brand-new framework for estimating hourly 2-km ground-level O_(3) concentrations across China(except Xinjiang and Tibet)using the brightness temperature at multiple thermal infrared bands.Considering the spatial heterogeneity of ground-level O_(3),a novel Self-adaptive Geospatially Local scheme based on Categorical boosting(SGLboost)is developed to train the estimation models.Validation results show that SGLboost performs well in the study area,with the R2 s/RMSEs of 0.85/19.041 lg/m^(3) and 0.72/25.112 lg/m^(3) for the space-based cross-validation(CV)(2017–2019)and historical space-based CV(2019),respectively.Meanwhile,SGLboost achieves distinctly better metrics than those of some widely used machine learning methods,such as e Xtreme Gradient boosting and Random Forest.Compared to recent related works over China,the performance of SGLboost is also more desired.Regarding the spatial distribution,the estimated results present continuous spatial patterns without a significantly partitioned boundary effect.In addition,accurate hourly and seasonal variations of ground-level O_(3) concentrations can be observed in the estimated results over the study area.It is believed that the hourly 2-km results estimated by SGLboost will help further understand the formation mechanisms of ground-level O_(3) in China. | Yuan Wang Qiangqiang Yuan Liye Zhu Liangpei Zhang | 2022 | Geoscience Frontiers2022，13，1： | 2 |
| 7 | Dimensionality Reduction Based on Clonal Selection for Hyperspectral Satellite Imagery 显示文摘 | Zhang Liangpei Zhong Yanfei Huang B | 2007 | IEEE Transactions on Geoscience and Remote Sensing2007，45，12： | 1 |
| 8 | Object-oriented change detection based on the Kolmogorov–Smirnov test using high-resolution multispectral imagery显示文摘 | Yuqi Tang Liangpei Zhang Xin Huang | 2011 | International Journal of Remote Sensing2011，，20： | 1 |
| 9 | An Adaptive Multiscale Information Fusion Approach for Feature Extraction and Classification of IKONOS Multispectral Imagery over Urban Areas显示文摘 | HUANG Xin ZHANG Liangpei LI Pingxiang | 2007 | IEEE Geoscience and Remote Sensing Letters2007，4，4： | 1 |
| 10 | Multiframe Super-resolution Employing a Spatially Weighted Total Variation Model显示文摘 | Yuan Qiangqiang Zhang Liangpei Shen Huanfeng | 2012 | IEEE Transactions on Circuits and Systems for Video Technology2012，22，3： | 1 |
| 11 | A nonlinear multiple feature Iearn-ing classifier for hyperspectral ima- ges with limited training samples 显示文摘 | Li Jiayi Zhang Hongyan Zhang Liangpei | 2015 | IEEE Journal of Se- lected Topics in Applied Earth Observations and Remote Sensing2015，8，： | 1 |
| 12 | A Pixel Shape Index Coupled with Spectral Information for Classification of High Spatial Resolution Remotely Sensed Imagery显示文摘 | ZHANG Liangpei HUANG Xin HUANG Bo | 2006 | IEEE Transactions on Geoscience and Remote Sensing2006，44，10： | 1 |
| 13 | Classification and Extraction of Spatial Features in Urban Areas Using High-resolution Multispectral Imagery显示文摘 | HUANG Xin ZHANG Liangpei LI Pingxiang | 2007 | IEEE Geoscience and Remote Sensing Letters2007，4，2： | 1 |
| 14 | An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery显示文摘 | HUANG Xin ZHANG Liangpei | 2013 | IEEE Transactions on Geoscience and Remote Sensing2013，51，1： | 1 |
| 15 | An Unsupervised Artificial Immune Classifier for Multi- hyperspeetral Remote Sensing Imagery 显示文摘 | ZHONG Yanfei ZHANG Liangpei HUANG Bo | 2006 | IEEE Transactions on Geoseience and Remote Sensing2006，44，2： | 1 |
| 16 | A Multifeature Tensor for Remote-Sensing Target Recognition显示文摘 | Zhang Lefei Zhang Liangpei | | 0，，02： | 1 |
| 17 | Object-oriented Subspace Analysis for Airborne Hyperspectral Remote Sensing Imagery显示文摘 | ZHANG Liangpei HUANG Xin | 2010 | Neurocomputing2010，73，46： | 1 |
| 18 | A pix- el shape index coupled with spectral information for classifi- cation of high spatial resolution remotely sensed imagery 显示文摘 | ZHANG Liangpei HUANG Xin HUANG Bo | 2006 | IEEE Transactions on Geoscience and Remote Sensing2006，44，10： | 1 |
| 19 | Classification and extraction of spatial features in urban areas using high resolution multispectral imagery显示文摘 | Huang Xin Zhang Liangpei Li Piangxiang | 2007 | IEEE Geoscience and Remote Sensing Letters2007，4，2： | 1 |
| 20 | Inpainting for remotely sensed images with a multichannel nonlocal total variation model显示文摘 | Cheng Qing Shen Huanfeng Zhang Liangpei | 2014 | IEEE Trans on Geoscience and Remote Sensing2014，52，1： | 1 |
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