检索结果-内蒙古大学图书馆

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

学校读者我要写书评

暂无评论

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

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

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

学校读者我要写书评

暂无评论

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

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

Advancing Few-Shot Instance Segmentation of Military Aircraft in Remote Sensing Imagery with Large Vision Models

学校读者我要写书评

暂无评论

Advancing Few-Shot Instance Segmentation of Military Aircraf...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xiyu Qi Yifan Wu Wenhui Zhang Zhan Chen Yidan Zhang Lei Wang Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Target Cognition and Application Technology (TCAT) Key Laboratory of Network Information System Technology (NIST) Key Laboratory of Technology in Geo-Spatial Information Processing and Application System

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This study introduces a novel few-shot instance segmentation framework MAR FSIS for identifying and segmenting aircraft in high-resolution remote sensing imagery under stringent few-shot conditions. Utilizing large vision models in conjunction with a sophisticated detector and a novel object prompter, MAR FSIS demonstrates superior performance in discerning intricate details of aircraft with minimal training examples. The effectiveness of this approach is confirmed through experimental evaluations on the proposed MAR20IS dataset, achieving remarkable precision in both detection and segmentation tasks.

关键词： Instance segmentation Training Atmospheric modeling Detectors Military aircraft Data models Proposals

SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Chen, Chen Zhao, Liangjin He, Yuanchun Long, Yingxuan Chen, Kaiqiang Wang, Zhirui Hu, Yanfeng 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

Semantic segmentation and 3D reconstruction are two fundamental tasks in remote sensing, typically treated as separate or loosely coupled tasks. Despite attempts to integrate them into a unified network, the constraints between the two heterogeneous tasks are not explicitly modeled, since the pioneering studies either utilize a loosely coupled parallel structure or engage in only implicit interactions, failing to capture the inherent connections. In this work, we explore the connections between the two tasks and propose a new network that imposes semantic constraints on the stereo matching task, both implicitly and explicitly. Implicitly, we transform the traditional parallel structure to a new cascade structure termed Semantic-Guided Cascade structure, where the deep features enriched with semantic information are utilized for the computation of initial disparity maps, enhancing semantic guidance. Explicitly, we propose a Semantic Selective Refinement (SSR) module and a Left-Right Semantic Consistency (LRSC) module. The SSR refines the initial disparity map under the guidance of the semantic map. The LRSC ensures semantic consistency between two views via reducing the semantic divergence after transforming the semantic map from one view to the other using the disparity map. Experiments on the US3D and WHU datasets demonstrate that our method achieves state-of-the-art performance for both semantic segmentation and stereo matching. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

SA-Occ: Satellite-Assisted 3D Occupancy Prediction in Real World

学校读者我要写书评

暂无评论

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

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

学校读者我要写书评

暂无评论

arXiv 2024年

关键词： Cameras

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

学校读者我要写书评

暂无评论

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

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

学校读者我要写书评

暂无评论

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

UNetMamba: An Efficient UNet-Like Mamba for Semantic Segmentation of High-Resolution Remote Sensing Images

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhu, Enze Chen, Zhan Wang, Dingkai Shi, Hanru Liu, Xiaoxuan Wang, Lei Beijing100094 China Key Laboratory of Target Cognition and Application Technology CAS Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Semantic segmentation of high-resolution remote sensing images is vital in downstream applications such as land-cover mapping, urban planning and disaster assessment. Existing Transformer-based methods suffer from the constraint between accuracy and efficiency, while the recently proposed Mamba is renowned for being efficient. Therefore, to overcome the dilemma, we propose UNetMamba, a UNet-like semantic segmentation model based on Mamba. It incorporates a mamba segmentation decoder (MSD) that can efficiently decode the complex information within high-resolution images, and a local supervision module (LSM), which is train-only but can significantly enhance the perception of local contents. Extensive experiments demonstrate that UNetMamba outperforms the state-of-the-art methods with mIoU increased by 0.87% on LoveDA and 0.39% on ISPRS Vaihingen, while achieving high efficiency through the lightweight design, less memory footprint and reduced computational cost. The source code is available at https://***/EnzeZhu2001/UNetMamba. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation