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On-orbit attitude planning for Earth observation imaging with star-based geometric calibration

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Science China Technological Sciences 2025年

作者： Di HUANG Zhifu WANG Jianya GONG Haoran JI Institute of Aerospace Science and Technology School of Remote Sensing and Information Engineering Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing School of Remote Sensing and Information Engineering Wuhan University National Key Laboratory of Air-based Information Perception and Fusion Intelligent Game and Decision Laboratory

To improve the quality of remote sensing images, a novel on-orbit attitude planning method for Earth observation imaging with star-based geometric calibration is presented in this study. Unlike traditional imaging processes, the proposed method includes both pre-calibration and post-calibration stages to enhance the accuracy of satellite geometric positioning. A multiple-constraint attitude planning algorithm was developed to ensure star-based calibration, wherein the satellite camera and star sensors simultaneously capture the images of stars. This integration of cameras and multiple star sensors enables effective attitude determination. During the Earth imaging stage, a fast on-orbit attitude planning algorithm was developed to determine the satellite attitude and the optimal imaging times for ground targets. Compared with existing methods, the fast on-orbit attitude planning algorithm can significantly reduce computational time and resource consumption via initial value selection and *** demonstrate the effectiveness of the proposed attitude planning method, which was successfully applied to Wuhan 1satellite.

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remote sensing data classification using tolerant rough set and neural networks

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Science China Earth Sciences 2005年第12期48卷 2251-2259页

作者： MA Jianwen HASI Bagan State Key Laboratory of remote sensing Science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China State Key Laboratory of Remote Sensing Science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China

BP algorithm of neural net is used more in remote sensing data classification. One of drawbacks of BP algorithm is the overall low function when the net is training. To avoid this kind of problem, the paper introduces the tolerant rough set for classification-preprocessing the training data to reduce the influence elements of the training convergence in order to improve the net training successful rate. ETM+ data of Beijing in May 2003 is selected in the study. ETM+ data before and after classification preprocessing, respectively, are used for BP (Back propaga-tion) training. The result shows that such a preprocessing not only compensates the drawback of BP algorithm when processing ETM+ data but also improves classification accuracy.

关键词： tolerant rough set, neural network, BP algorithm, classification.

MFVNet: a deep adaptive fusion network with multiple field-of-views for remote sensing image semantic segmentation

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Science China(Information Sciences) 2023年第4期66卷 93-106页

作者： Yansheng LI Wei CHEN Xin HUANG Zhi GAO Siwei LI Tao HE Yongjun ZHANG School of remote sensing and Information Engineering Wuhan University

In recent years, the remote sensing image(RSI) semantic segmentation attracts increasing research interest due to its wide application. RSIs are difficult to be processed holistically on current GPU cards on account of their large field-of-views(FOVs). However, the prevailing practices such as downsampling and cropping will inevitably decrease the quality of semantic segmentation. To address this conflict,this paper proposes a new deep adaptive fusion network with multiple FOVs(MFVNet), which is specially designed for RSI semantic segmentation. Different from existing methods, MFVNet takes into consideration the differences among multiple FOVs. By pyramid sampling the RSI, we first obtain images on different scales with multiple FOVs. Images on the high scale with a large FOV can capture larger spatial contexts and complete object contours, while images on the low scale with a small FOV can keep the higher spatial resolution and more detailed information. Then scale-specific models are chosen to make the best predictions for all scales. Next, the output feature maps and score maps are aligned through the scale alignment module to overcome spatial misregistration among scales. Finally, the aligned score maps are fused with the help of adaptive weight maps generated by the adaptive fusion module, producing the fused prediction. The performance of MFVNet surpasses the previous state-of-the-art semantic segmentation models on three typical RSI datasets, demonstrating the effectiveness of the proposed MFVNet.

关键词： semantic segmentation remote sensing image (RSI) field-of-view (FOV) adaptive fusion convolutional neural network

Direct inversion of shallow-water bathymetry from EO-1 hyperspectral remote sensing data

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Chinese Optics Letters 2011年第6期9卷 5-8页

作者：刘智深周燕 Ocean remote sensing Institute Ocean University of China

Using the US National Aeronautics and space Administration （NASA） Earth Observing-1 Mission （EO-1） hyperion hyperspectral remote sensing data, we study the shallow-water bathymetry inversion in Smith Island Bay. The fast line-of-sight atmospheric analysis of spectral hypercubes module is applied for atmospheric correction, and principal component analysis method combined with scatter diagram and maximum likelihood classification is used for seabed classification. The diffuse attenuation coefficient Kd is derived using quasi-analytical algorithm （QAA）, which performs well in optically deep water. Kd obtained from QAA requires correction, particularly those derived in some coastal areas with optically shallow water and calculated by direct inversion based on radiative transfer theory to obtain the bathymetry. The direct inversion method derives the water depth quickly, and matches the results from optimized algorithm.

关键词： Algorithms Coastal zones Earth (planet) Maximum likelihood estimation NASA Principal component analysis remote sensing Space optics

Deep neural network for remote-sensing image interpretation: status and perspectives

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National Science Review 2019年第6期6卷 1082-1086页

作者： Jiayi Li Xin Huang Jianya Gong School of remote sensing and Information Engineering Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan University

Deep neural networks(DNNs) refer to end-to-end mappings(i.e. from data to information) by stacking a large number of filters learned from massive samples. By courtesy of the comprehensive Earth observation platforms and convenient data access, remote-sensing practitioners are dealing with very large and ever-growing data volumes, which call for fast and transferrable machine-learning

关键词： status and perspectives image Deep neural network for remote-sensing image interpretation

Analysis and solution of streak effect in high dynamic range CMOS image sensors

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Science China(Information Sciences) 2025年第1期68卷 402-403页

作者： Wanbin ZHA Jiangtao XU Jinghua AO Kaiming NIE Zhiyuan GAO School of Microelectronics Tianjin University Tianjin Key Laboratory of Imaging and sensing Microelectronic Technology Tianjin University

The high dynamic range (HDR) CMOS image sensor has been widely used in many fields,such as autonomous driving and security guarding [1].The dual conversion gain (DCG)structure,which adjusts the conversion gain (CG) by changing the capacitors connected to the floating diffusion (FD)nodes in the pixel,is an effective and prevalent method for realizing HDR imaging [2].While the DCG structure can extend the dynamic range,in the practical HDR imaging process,the substantial number of photo-generated electrons in bright areas can cause significant voltage variations in the pixel *** variations affect the gray values in darker areas of the same image,leading to the streak effect [3].This study primarily investigates the streak effect caused by forward conduction current of the PN junction in DCG pixels,and proposes a circuit to suppress this effect.

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Spatio-temporal-spectral observation model for urban remote sensing

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Geo-Spatial Information Science 2021年第3期24卷 372-386页

作者： Zhenfeng Shao Wenfu Wu Deren Li State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of remote sensing and Information Engineering Wuhan UniversityWuhanChina

Taking cities as objects being observed,urban remote sensing is an important branch of remote *** the complexity of the urban scenes,urban remote sensing observation requires data with a high temporal resolution,high spatial resolution,and high spectral *** the best of our knowledge,however,no satellite owns all the above ***,it is necessary to coordinate data from existing remote sensing satellites to meet the needs of urban *** this study,we abstracted the urban remote sensing observation process and proposed an urban spatio-temporal-spectral observation model,filling the gap of no existing urban remote sensing *** this study,we present four applications to elaborate on the specific applications of the proposed model:1)a spatiotemporal fusion model for synthesizing ideal data,2)a spatio-spectral observation model for urban vegetation biomass estimation,3)a temporal-spectral observation model for urban flood mapping,and 4)a spatio-temporal-spectral model for impervious surface *** believe that the proposed model,although in a conceptual stage,can largely benefit urban observation by providing a new data fusion paradigm.

关键词： Urban remote sensing spatio-temporal-spectral observation model remote sensing data fusion Earth observation programs

On utilization of a priori knowledge in inversion of remote sensing models

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Science China Earth Sciences 1998年第6期41卷 580-585页

作者：李小文王锦地胡宝新 AlanH.Strahler Institute of remote sensing Application Chinese Academy of Sciences Beijing China Center for remote sensing and Department of Geography Boston University Boston USA

Satellite remote sensing deals with a complex system coupling atmosphere and surface. Any physical model with reasonable precision needs several to tens of parameters. Without a priori knowledge of these parameters, Proposition 3 of Verstraete et al. requires the number of independent observations to be greater than the number of unknown parameters. This requirement can hardly be satisfied even in the coming EOS era. As Tarantola pointed out, the inversion problems in geoscience are always underdetermined in some sense. In order to make good use of every kind of a priori knowledge for effectively extracting information from remote sensing observations, the right question to set is as follows: Given an imperfect model and a certain amount of a priori information on model parameters, in which sense should one modify the a priori information, given the actual observation with noise? A priori knowledge of physical parameters can be presented in different ways such as physical limits, global statistical means and variance for a certain landcover type, or previous statistics and temporal variation of a specific target. When such a priori knowledge can be expressed as joint probability density, Bayessian theorem can be used in the inversion to obtain posterior probability densities of parameters using newly acquired observations. There is no prerequirement on how many independent observations must be made, and the knowledge gained merely depends on the information content of the new observations. Some specific problems about knowledge accumulation and renewal are also discussed.

关键词： quantitative remote sensing a priori knowledge model inversion.

Advances in spaceborne hyperspectral remote sensing in China

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Geo-Spatial Information Science 2021年第1期24卷 95-120,I0012页

作者： Yanfei Zhong Xinyu Wang Shaoyu Wang Liangpei Zhang State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of remote sensing and Information Engineering Wuhan UniversityWuhanChina

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 *** 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 *** Chinese spaceborne HRS platforms have various new characteristics,such as the wide swath width,high spatial resolution,wide spectral range,hyperspectral satellite networks,and *** paper focuses on the recent progress in Chinese spaceborne HRS,from the aspects of the typical satellite systems,the data processing,and the *** addition,the future development trends of HRS in China are also discussed and analyzed.

关键词： Hyperspectral remote sensing spaceborne HRS hyperspectral image processing and remote sensing applications