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arXiv 2025年

作者： Chen, Chen Wang, Zhirui Sheng, Taowei Jiang, Yi Li, Yundu Cheng, Peirui Zhang, Luning Chen, Kaiqiang Hu, Yanfeng Yang, Xue Sun, Xian Key Laboratory of Target Cognition and Application Technology Aerospace Information Research Institute Chinese Academy of Sciences China University of Chinese Academy of Sciences China Shanghai Jiao Tong University China

Existing vision-based 3D occupancy prediction methods are inherently limited in accuracy due to their exclusive reliance on street-view imagery, neglecting the potential benefits of incorporating satellite views. We propose SA-Occ, the first Satellite-Assisted 3D occupancy prediction model, which leverages GPS & IMU to integrate historical yet readily available satellite imagery into real-time applications, effectively mitigating limitations of ego-vehicle perceptions, involving occlusions and degraded performance in distant regions. To address the core challenges of cross-view perception, we propose: 1) Dynamic-Decoupling Fusion, which resolves inconsistencies in dynamic regions caused by the temporal asynchrony between satellite and street views;2) 3D-Proj Guidance, a module that enhances 3D feature extraction from inherently 2D satellite imagery;and 3) Uniform Sampling Alignment, which aligns the sampling density between street and satellite views. Evaluated on Occ3D-nuScenes, SA-Occ achieves state-of-the-art performance, especially among single-frame methods, with a 39.05% mIoU (a 6.97% improvement), while incurring only 6.93 ms of additional latency per frame. Our code and newly curated dataset are available at https://***/chenchen235/SA-Occ. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite imagery

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A SAR Deceptive Jamming Suppression Method Based on PRI Variation Design and Multi-Channel Principle

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Shi, Haixu Xu, Zhongqiu Li, Guangzuo Lin, Kuan Liu, Tianqu Hong, Wen Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China Key Laboratory of Spatial Information Processing and Application System Technology Beijing100094 China Key Laboratory of Target Cognition and Application Technology Beijing100094 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing101499 China

The synthetic aperture radar (SAR) can be affected by various types of jamming during operation. Among them, the deceptive jamming generated by digital radio frequency memory (DRFM) jammers poses a serious threat to SAR imaging by creating highly realistic false targets. Moreover, with advancements in deceptive jamming technology, the generation speed of deceptive jamming has increased, rendering existing methods less effective. To address this issue, an anti-deceptive jamming method based on pulse repetition interval (PRI) variation design and multi-channel principle is proposed to mitigate the effects of deceptive jamming. First, a PRI variation strategy that will not cause the loss of echo signals in the imaging area is designed. By utilizing this strategy for imaging, deceptive jamming signals are dispersed across different ranges, resulting in preliminary suppression of the jamming. Subsequently, after azimuth non-uniform sampling reconstruction and range processing, most of the jamming signals are suppressed due to the azimuth timing differences between SAR and jamming signals. However, when the jammer uses specific retransmission intervals, such as the average PRI of the PRI sequence, the jamming signals may be concentrated at certain ranges, retaining some coherence and posing a threat to SAR imaging. To overcome this challenge, a residual jamming detection and suppression algorithm based on multi-channel principle is proposed, which can detect and filter out the channels affected by jamming. Finally, an azimuth sparse reconstruction is introduced for azimuth processing. Since the anti-jamming principle of this method relies on the differences in azimuth timing between SAR and jamming, it can suppress deceptive jamming even when the generation speed of deceptive jamming is rapid, which some other anti-deceptive jamming methods cannot achieve. Simulations of SAR imaging under deceptive jamming conditions are conducted for point target scene and complex target

关键词： Synthetic aperture radar

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Monitoring Changes to Small-Sized Lakes Using Medium Spatial and Temporal Satellite Imagery in the Badain Jaran Desert from 2015 to 2020

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International Journal of Crowd Science 2025年第2期9卷 96-109页

作者： Qinyu Zhao Luyan Ji Yonggang Su Kai Yu Yongchao Zhao College of Electronic and Information Engineering Hebei University Baoding China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial information Processing and Application System Beijing China Key Laboratory of Target Cognition and Application Technology (TCAT) Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

The Badain Jaran Desert is the second-largest desert in China, and its lakes, which are generally small-sized and highly dynamic, play a significant role for plants and animals in this arid region. Therefore, long-term monitoring of the distribution of lakes in the Badain Jaran Desert with high spatial and temporal resolution is of great importance. However, due to the tradeoff between pixel size and swath width, currently no single satellite sensor can provide such a time series. Thereby, in this study, we focus on applying the deep learning based spatiotemporal fusion method (super-resolution based spatial fusion with Generative Adversarial Network (GAN)) to a low spatial yet high temporal resolution data (i.e., MODIS 250 m daily reflectance time series) and a high spatial yet low temporal resolution data (i.e., Landsat 30 m 16-day reflectance time series) to generate a daily 30 m time series for 37 selected lakes in the Badain Jaran Desert. Then, an automatic water extraction algorithm is proposed, and a daily 30 m water mapping production is generated for our study area from 2015 to 2020. The overall accuracy can reach 0.92, while the average error of lake areas is less than 9.21%, which is much higher than that derived from the MODIS time series. Moreover, based on our daily high spatial resolution results, it is possible to analyze the water phenology for all sizes of lakes in the Badain Jaran Desert. We have performed a detailed analysis of interannual variability and seasonal changes for the selected 37 lakes in the Badain Jaran Desert. The results show that from 2015 to 2020, the shrinkage of the small lakes (<0.5 km 2 ) is more severe than lakes with a larger size. As for seasonal changes, the lake area can be divided into four stages: quick increase due to ice melting from winter to spring, slow decrease due to evaporation from spring to summer, moderate recovery due to the arrival of the rainy season from summer to autumn, and quick decrease due to lake

关键词： Reflectivity Heuristic algorithms Time series analysis Landsat Lakes Deserts Spatiotemporal phenomena Spatial resolution MODIS Monitoring

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Undistorted and Consistent Enhancement of Automotive SAR Image via Multi-Segment-Reweighted Regularization

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Remote Sensing 2025年第9期17卷 1483-1483页

作者： Zhang, Yan Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Technology in Geo-Spatial Information and Processing Application System Beijing100190 China Key Laboratory of Target Cognition and Application Technology Beijing100190 China

In recent years, synthetic aperture radar (SAR) technology has been increasingly explored for automotive applications. However, automotive SAR images generated via matched filter (MF) often exhibit challenges such as noisy backgrounds, sidelobe artifacts, and limited resolution. Sparse regularization methods have the potential to enhance image quality. Nevertheless, conventional unweighted (Formula presented.) regularization methods struggle to address cases with radar cross section (RCS) distributed over a wide dynamic range, often resulting in insufficient sidelobe suppression, amplitude distortion, and inconsistent super-resolution performance. In this paper, we propose a novel reweighted regularization method, termed multi-segment-reweighted regularization (MSR), for automotive SAR image restoration. By introducing a novel weighting scheme, MSR localizes the global scattering point enhancement problem to the mainlobe scale, effectively mitigating sidelobe interference. This localization ensures consistent enhancement capability independent of RCS variations. Furthermore, MSR employs multi-segment regularization to constrain amplitude within the mainlobes, preserving the characteristics of the original response. Correspondingly, a new thresholding function, named Thinner Response Undistorted THresholding (TRUTH), is introduced. An iterative algorithm for enhancing automotive SAR images using MSR is also presented. Real data experiments validate the feasibility and effectiveness of the proposed method. © 2025 by the authors.

关键词： Image segmentation

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High-Performance On-Orbit Intelligent Computing and Real-Time Services for Remote Sensing Satellites Based on Large-Scale Computing Power in Space

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IEEE Access 2025年 13卷 92114-92133页

作者： Guoping Wang Gang Wan Zhijuan Su Yang Wang Yutong Jia Gong Li Shi Liang Department of Surveying and Mapping and Space Environment Space Engineering University Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Target Cognition and Application Technology Beijing China Beijing Institute of Space Mechanics and Electricity Beijing China School of Artificial Intelligence Xidian University Xi’an China

The rapid advancement in Earth observation systems and satellite networks driven by commercial space has led to a significant increase in the number of remote sensing satellites, thereby establishing the groundwork for all-weather, continuous, cluster-collaborative observation. Following anti-irradiation hardening measures, low-cost commercial off-the-shelf (COTS) devices are effectively operated within intricate space environments, leading to substantial enhancements of the on-orbit computing capabilities of individual satellites. The onboard-distributed processing framework can be leveraged to fully utilize wide-area scattered space computing resources; accordingly, it is possible to automatically establish a network of neighboring satellites through inter-satellite communication links to construct a spatially distributed and computationally cooperative cloud computing environment in space. This enables the sharing of space computing resources and facilitates satellite-based mission coordination, reducing reliance on ground-based remote sensing satellite services while enhancing the response efficiency of remote sensing application services. The present paper provides a comprehensive review of the current development status, existing challenges, and problems faced by space-based, on-orbit remote sensing processing platforms and onboard intelligent processing algorithms; we start with the immediate application requirements of the remote sensing satellites used in in-orbit processing. In view of the anticipated surge in large-scale computing resources in space, a distributed processing architecture for remote sensing satellite data, based on large-scale onboard computing power, is constructed. The architecture is designed in detail, with attention paid to the physical architecture, the software architecture, the workflow, and the key technologies. Finally, three real-time service scenarios that align with the construction content of an ultra-low-orbit satellite cons

关键词： Satellites Remote sensing Space vehicles Real-time systems Satellite broadcasting Satellite constellations Earth Software Hyperspectral imaging Artificial intelligence

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Ringmo-SenseV2: Remote Sensing Foundation Model for Spatiotemporal Prediction Based on Multisource Heterogeneous Time-Series Data

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Xu, Liangyu Lu, Wanxuan Hu, Leiyi Yang, Heming Jiang, Yi Liu, Chenglong Yu, Hongfeng Deng, Chubo Sun, Xian Fu, Kun Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Target Cognition and Application Technology Beijing100190 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China

The rapid development of remote sensing (RS) technology has generated a vast amount of heterogeneous time-series data from various sources, including drone videos, satellite time-series images, and multiobject trajectories. Effectively processing and analyzing this multisource heterogeneous data for accurate spatiotemporal prediction is crucial in fields such as environmental protection and disaster response. In this article, we propose a universal predictive foundation model named Ringmo-SenseV2 to learn the general evolutionary patterns of RS elements from massive heterogeneous data. Ringmo-SenseV2 features a mixture-of-heterogeneous-experts (MoHE) Transformer, which unifies the modeling of multisource heterogeneous time-series data. Additionally, to better capture the complex dependencies across different spatiotemporal locations, we introduce a hypergraph translator (HT), treating embeddings of different spatiotemporal locations as nodes and employing hypergraph convolution for information propagation. Furthermore, to enhance the model’s adaptability to different evolution speeds during pretraining, we implement the adaptive tube masking (AM) strategy, which controls prediction difficulty by adaptively setting mask proportions for sequences with varying evolution speeds. Extensive experiments demonstrate that Ringmo-SenseV2 exhibits outstanding performance across various RS prediction tasks. Further tests on scene graph generation for RS images showcase the model’s ability to extract image features, thereby enhancing image perception tasks. © 1980-2012 IEEE.

关键词： Image enhancement

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Efficient Reconstruction of Spatial Features for Remote Sensing Image-Text Retrieval

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Transactions of Nanjing University of Aeronautics and Astronautics 2025年第1期42卷 101-111页

作者： ZHANG Weihang CHEN Jialiang ZHANG Wenkai LI Xinming GAO Xin SUN Xian Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100190P.R.China Key Laboratory of Target Cognition and Application Technology(TCAT) Chinese Academy of SciencesBeijing 100190P.R.China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100190P.R.China School of Computer Science and Artificial Intelligence Aerospace Information Technology UniversityJinan 250299P.R.China

Remote sensing cross-modal image-text retrieval(RSCIR)can flexibly and subjectively retrieve remote sensing images utilizing query text,which has received more researchers’attention ***,with the increasing volume of visual-language pre-training model parameters,direct transfer learning consumes a substantial amount of computational and storage ***,recently proposed parameter-efficient transfer learning methods mainly focus on the reconstruction of channel features,ignoring the spatial features which are vital for modeling key entity *** address these issues,we design an efficient transfer learning framework for RSCIR,which is based on spatial feature efficient reconstruction(SPER).A concise and efficient spatial adapter is introduced to enhance the extraction of spatial *** spatial adapter is able to spatially reconstruct the features in the backbone with few parameters while incorporating the prior information from the channel *** conduct quantitative and qualitative experiments on two different commonly used RSCIR *** with traditional methods,our approach achieves an improvement of 3%-11% in sumR *** with methods finetuning all parameters,our proposed method only trains less than 1% of the parameters,while maintaining an overall performance of about 96%.

关键词： remote sensing cross-modal image-text retrieval(RSCIR) spatial features channel features contrastive learning parameter effective transfer learning

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Multi-document reading comprehension model based on electra and document sliding window

Multi-document reading comprehension model based on electra ...

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2023 International Workshop on Signal Processing and Machine Learning, WSPML 2023

作者： Yang, Zheyu Zhang, Benkui Jiang, Nan Key Lab of Target Cognition and Application Technology Beijing China Aerospace Information Research Institute CAS Beijing China

ISBN: (纸本)9781510671928

Multi-document reading comprehension is an important and difficult task in natural language processing. To address the issue that ELECTRA pre-training model has length limitation and cannot be directly adapt to multi-document reading comprehension task, this paper proposes a novel model based on ELECTRA and document sliding windows. In the model multiple documents are split and merged through document sliding windows, new segmentation embedding is introduced, answer position in documents is modelled as a learning target, and ELECTRA is used for joint training in each window. After obtaining all prediction outcomes of each window, the results are comprehensively sorted to achieve the optimal answer. The experiments show that Rouge-L of this model reaches 51.28% on the multi-document reading comprehension dataset MS-MARCO, ranking the current best result. © 2023 SPIE. All rights reserved.

关键词： Natural language processing systems

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Hybrid bundle-adjusting 3D Gaussians for view consistent rendering with pose optimization 11

Hybrid bundle-adjusting 3D Gaussians for view consistent ren...

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Optoelectronic Imaging and Multimedia technology XI 2024

作者： Guo, Yanan Xie, Ying Chang, Ying Zhang, Benkui Jia, Bo Cao, Lin Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Target Cognition and Application Technology Aerospace Information Research Institute Beijing China

ISBN: (纸本)9781510682061

Novel view synthesis has made significant progress in the field of 3D computer vision. However, the rendering of view-consistent novel views from imperfect camera poses remains challenging. In this paper, we introduce a hybrid bundle-adjusting 3D Gaussians model that enables view-consistent rendering with pose optimization. This model jointly extract image-based and neural 3D representations to simultaneously generate view-consistent images and camera poses within forward-facing scenes. The effective of our model is demonstrated through extensive experiments conducted on both real and synthetic datasets. These experiments clearly illustrate that our model can effectively optimize neural scene representations while simultaneously resolving significant camera pose misalignments. © 2024 SPIE.

关键词： Cameras

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Rotation Correction Based Neural Radiance Fields for Multi-view Satellite Images 11

Rotation Correction Based Neural Radiance Fields for Multi-v...

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Optoelectronic Imaging and Multimedia technology XI 2024

作者： Liu, Yangfan Guo, Yanan Bian, Huanyu Zhang, Benkui Du, Kangning Xie, Ying Cao, Lin Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Target Cognition and Application Technology Aerospace Information Research Institute Beijing China

ISBN: (纸本)9781510682061

To address the issue of reconstruction distortions caused by using non-unified RPC models in 3D reconstruction with multi-view satellite imagery. In this paper, we propose a method termed Rotation Correction Based Neural Radiance Fields for Multi-view Satellite Images. Our method optimizes the satellite camera model and neural radiance field by integrating multi-axial rotational correction into the reconstruction task. By separately correcting the attitude angles along the X, Y, and Z axes, the geometric distortions in multi-view satellite images can be corrected. Furthermore, by setting the ray projection in the neural radiance field under the effect of variable correction rotation variables, our method allows simultaneous optimization of the correction rotation variables and the neural radiance field during back propagation. We conducted experiments using WorldView-3 images from different locations, comparing the reconstruction results of images corrected with traditional BA and our method. Our method achieved the best results. © 2024 SPIE.

关键词： Satellite imagery

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