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BUILDING LANE-LEVEL MAPS FROM AERIAL imageS 49

BUILDING LANE-LEVEL MAPS FROM AERIAL IMAGES

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49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yao, Jiawei Pan, Xiaochao Wu, Tong Zhang, Xiaofeng Univ Washington Seattle WA 98195 USA Taiyuan Univ Technol Taiyuan Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Detecting lane lines from sensors is becoming an increasingly significant part of autonomous driving systems. However, less development has been made on high-definition lane-level mapping based on aerial images, which could automatically build and update offline maps for auto-driving systems. To this end, our work focuses on extracting fine-level detailed lane lines together with their topological structures. This task is challenging since it requires large amounts of data covering different lane types, terrain and regions. In this paper, we introduce for the first time a large-scale aerial image dataset built for lane detection, with high-quality polyline lane annotations on high-resolution images of around 80 kilometers of road. Moreover, we developed a baseline deep learning lane detection method from aerial images, called AerialLaneNet, consisting of two stages. The first stage is to produce coarsegrained results at point level, and the second stage exploits the coarse-grained results and feature to perform the vertexmatching task, producing fine-grained lanes with topology. The experiments show our approach achieves significant improvement compared with the state-of-the-art methods on our new dataset. Our code and new dataset are available at https://***/Jiawei-Yao0812/AerialLaneNet.

关键词： Computer Vision Lane Detection remote sensing

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Optimized Gaussian kernel regression for X-ray pulsar signal profile construction 3

Optimized Gaussian kernel regression for X-ray pulsar signal...

引用

3rd International conference on Environmental remote sensing and Geographic Information Technology, ERSGIT 2024

作者： Dong, Yan Zhuang, Yutao Wang, Ruoning National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing100071 China

ISBN: (纸本)9781510690219

With the continuous advancement of pulsar-based navigation technology, signal processing issues in X-ray pulsar navigation applications have received increasing attention. One of the key factors limiting the improvement of navigation accuracy is the low signal-to-noise ratio (SNR) of epoch-folded profiles. Pulsar profiles contain many important characteristic features, such as pulse shape symmetry and multi-pulse structures. To facilitate further study, it is necessary to reduce noise and obtain high-SNR pulsar profiles. This study addresses this issue by proposing a method for high-precision pulsar profile construction based on optimized Gaussian kernel regression. The constructed profiles are evaluated and analyzed in terms of profile quality and phase information estimation based on the profile. The results show that the SNR of the epoch-folded profiles after denoising is significantly better than that of the original profiles, providing an important reference for signal processing in X-ray pulsar navigation applications. © 2025 SPIE.

关键词： image denoising

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Numba-based Non-local Means Filter for SAR image Denoising 16

Numba-based Non-local Means Filter for SAR Image Denoising

引用

16th International conference on Computer and Automation Engineering (ICCAE)

作者： Devendhar, G. Singh, Pratibha Sharma, Rakesh Natl Inst Technol Dept Elect & Commun Engn Hamirpur Himachal Prades India

ISBN: (纸本)9798350370058;9798350370164

Synthetic Aperture Radar (SAR) images are an essential tool for earth observation and remote sensing, but speckle can degrade the quality of such images. image processing techniques such as filtering and averaging can effectively reduce the impact of noise on images. This paper evaluates the performance of the hybrid nonlocal means (NLM) filter in denoising Sentinel-1 SAR images. The proposed approach combines adaptive exponential kernel-based pre-processing with an NLM filter. The NLM algorithm is computationally intensive and time-consuming due to its pixel-by-pixel denoising based on extended neighborhood similarities. To address this challenge, this paper explores parallelization of the NLM algorithm using NUMBA, a Python compiler, to accelerate its performance. It is found that the proposed approach shows a significant improvement in terms of Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Squared Error (MSE) and performs better than conventional NLM filters and even provides the greatest speedup when compared to earlier parallel NLM techniques.

关键词： NLM denoising Multi-processing Parallelization Numba SMP

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High-resolution sliding-spotlight spaceborne SAR imaging algorithm for Ka-band

High-resolution sliding-spotlight spaceborne SAR imaging alg...

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IET International Radar conference 2023, IRC 2023

作者： Xiao, Feng Jing, Maoqiang He, Dehua Zhang, Guo Zu, JiaGuo Yu, SongBai Sun, HanWei Wang, XinMin Beijing Institute of Radio Measurement Beijing China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing of Wuhan University Wuhan China Beijing China

ISBN: (纸本)9781839539954

High-band spaceborne SAR offers image-like optics and broadens the application range of microwave remote sensing on space-based platforms. The sliding-spotlight mode fulfils the high-resolution detection requirements for target recognition in remote sensing and has gained significant attention and application in the industry. Given the differences between high-band spaceborne SAR data processing and traditional low-band spaceborne SAR data processing in terms of orbital accuracy analysis, key parameter calculation of imaging, algorithm acceleration, and parallelization, this study conducts an analysis of data accuracy, imaging, and image quality using real data from Luojia-2. The results demonstrate that the proposed imaging process method achieves high-precision imaging of the sliding-spotlight SAR real data from Luojia-2. © The Institution of Engineering & Technology 2023.

关键词： Network security

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Anomaly Interpretation Method Based on Data Mining for remote sensing Satellite Payload 10th

Anomaly Interpretation Method Based on Data Mining for Remot...

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10th International conference on signal and Information processing, Network and Computers, ICSINC 2022

作者： You, Jia Lu, Chengzhi China Academy of Space Technology No. 104 Youyi Road Haidian District Beijing China

ISBN: (纸本)9789811999673

Because of the rapid development of earth observation technology and artificial intelligence technology, high-resolution optical imaging satellites have emerged continuously, and the era of remote sensing big data has arrived. Due to the limitations of huge image size, sparse target distribution, rich variation of target time domain and insufficient labeling training samples, the current automatic detection of abnormal targets in satellite remote sensing images is inefficient and easy to generate false alarms. This paper proposes a data mining-based joint interpretation method for remote sensing satellite load data, which realizes automatic and fast accurate interpretation of remote sensing image abnormal targets and significantly improves the efficiency of camera data quality assessment. It is the first time to realize a major technical leap from visual interpretation to machine full adaptive interpretation of remote sensing image data, which can provide reference for subsequent adaptive interpretation of remote sensing satellite images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for remote sensing image Dehazing Using Satellite-to-Ground NDVI Knowledge

SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remot...

引用

2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Liu, Zepeng Yang, Zhicheng Zhu, Mingye Wong, Andy Wei, Yibing Han, Mei Yu, Jun Lai, Jui-Hsin University of Science and Technology of China China Ping An Technology China PAII Inc. United States University of Wisconsin - Madison United States

ISBN: (纸本)9798350313154

image dehazing is a meaningful low-level computer vision task and can be applied to a variety of contexts. In our industrial deployment scenario based on remote sensing (RS) images, the quality of image dehazing directly affects the grade of our crop identification and growth monitoring products. However, the widely used peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) provide ambiguous visual interpretation. In this paper, we design a new objective metric for RS image dehazing evaluation. Our proposed metric leverages a ground-based phenology observation resource to calculate the vegetation index error between RS and ground images at a hazy date. Extensive experiments validate that our metric appropriately evaluates different dehazing models and is in line with human visual perception. © 2023 IEEE.

关键词： remote sensing

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Spatio-Semantic Prompt guided Adaptive Segment Anything for remote sensing Change Detection

Spatio-Semantic Prompt guided Adaptive Segment Anything for ...

引用

2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Hu, Shenglong Han, Zhidong Dong, Gang Liang, Lingyan Wen, Dongchao Zhang, Kaihua IEIT SYSTEMS Co. Ltd. China NUIST China

ISBN: (纸本)9798350368741

Existing leading remote sensing change detection (RSCD) often takes a semantic-agnostic learning paradigm, which uses a binary ground-truth mask as supervision for model training. Despite the demonstrated success, due to the intrinsic characteristic of extremely complicated scene changes in RS images, this paradigm is prone to be misled by irrelevant semantic category changes, leading to a noisy CD mask prediction. To address this issue, this paper presents a Spatio-Semantic Prompt (SSP) guided adaptive Segment Anything Model (SAM) for RSCD, dubbed as SSP-SAM. The SSP-SAM introduces sparse textual and dense mask prompts into SAM to encode the task-specific semantic knowledge for RSCD. Specifically, we first encode the powerful textual semantic knowledge using Contrastive Language-image Pre-training (CLIP) to determine the desired change semantic category. Then, we design a spatial dense prompt module that yields an attention map as prompt features to further refine the desired changed regions. Subsequently, we fine-tune the SAM through an adaptor to integrate the spatial-semantic prompt cues, yielding a coarse CD mask prediction. Finally, guided by the coarse CD mask, a multi-scale mask attention mechanism is adopted to learn the refined semantic representations of the changed targets, predicting the accurate CD mask. Extensive experiments on a variety of benchmark datasets demonstrate that the proposed SSP-SAM achieves state-of-the-art performance. © 2025 IEEE.

关键词： contrastive language-image pre-training(CLIP) mask attention remote sensing change detection segment anything model(SAM) semantic information

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Comparison between high and coarse spatial resolution surface soil moisture products over two Mediterranean sites in Tunisia and France 7

Comparison between high and coarse spatial resolution surfac...

引用

IEEE 7th International conference on Advanced Technologies, signal and image processing (ATSIP)

作者： Ayari, Emna Baghdadi, Nicolas Rodriguez-Fernandez, Nemesio J. Albergel, Clement Stradiotti, Pietro Lili-Chabaane, Zohra Zribi, Mehrez UT3 Paul Sabatier CESBIO CNES CNRSINRAEIRD Toulouse France Univ Montpellier AgroParisTech CIRAD CNRSINRAETETIS Montpellier France ECSAT European Space Agcy Climate Off Harwell Campus Didcot OX11 0FD Oxon England Vienna Univ Technol Climate & Environm Remote Sensing Grp Dept Geodesy & Geoinformat Vienna Austria Spatial Anal & Modeling GREEN TEAM Agron Inst Tunisia LR17AGR01 InteGRatEd Management Nat Resources Rem Tunis Tunisia

ISBN: (纸本)9798350351491;9798350351484

The surface soil moisture (SSM) is an essential component in hydrologic modelling. Therefore, an accurate retrieval of SSM with high repetitiveness in time and space will improve the hydrologic performance. In the present paper, we compare between 1-km SSM product available every 6 days (HRSM) with operational SSM products namely Soil Moisture Active and Passive (SMAP) and ESA CCI combined (CCI) characterized with coarse spatial resolution and high revisit time up between 1 and 3 days over two Mediterranean sites in Tunisia and Occitanie in France. The analyses of SSM time series are performed as function of SSM product date in first step and monthly in a second step. The results revealed that HRSM time series, over the two sites, are in coherence with SMAP and CCI time series and marked by seasonality from one year to another (2017-2021). Over the Occitanie site, the comparison between HRSM monthly averages and the SMAP and CCI averages is characterized by an RMSE values varying between 5 and 9.8 vol.%, respectively. The RMSE values decreased, over Tunisia, and reached a maximum of 5 vol.%. The aforementioned findings underlined the potential of 1-km product to be included in future applications such as drought identification.

关键词： surface soil moisture microwave Sentinel-1 Sentinel-2 SMAP ESA CCI drought Tunisia France

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Slow Scan Television (SSTV) Method for Transmitting images Using HT and SDR as Receivers

Slow Scan Television (SSTV) Method for Transmitting Images U...

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2024 International conference on Aerospace Electronics and remote sensing Technology

作者： Maharani, Eliana Pratiwi, Amelia Chandra Zega, De Shepherd Guella Winisia Aprilliyani, Ria Ariteja, Dananjaya Dindin, Imanuel Republ Indonesia Def Univ Mil Elect Engn Bogor Indonesia Republ Indonesia Def Univ Mil Mech Engn Bogor Indonesia Republ Indonesia Def Univ Fac Def Sci & Technol Bogor Indonesia

ISBN: (纸本)9798331542023;9798331542016

In this paper, the performance of Slow Scan Television (SSTV) image transmission was investigated using two types of receivers: Handheld Transceiver (HT) and Software-Defined Radio (SDR). The study aimed to compare the effectiveness of these receivers based on image quality metrics, including Peak signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Root Mean Square Error (RMSE), at varying distances of 50 m, 100 m, and 200 m. Experimental results demonstrated that SDR consistently outperforms HT, particularly at 50 meters, where SDR achieved the highest PSNR (28.452), the lowest MSE (92.872) and the lowest RMSE (9.634), indicating superior image reception quality. These findings suggested that SDR offers enhanced capabilities for image transmission over short distances, making it a preferable choice in applications such as amateur radio and emergency communications. This research provided valuable insights into optimizing SSTV transmissions and highlighted SDR's adaptability for various communication scenarios.

关键词： SSTV SDR HT image processing communication

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SUPERVISING remote sensing CHANGE DETECTION MODELS WITH 3D SURFACE SEMANTICS 29

SUPERVISING REMOTE SENSING CHANGE DETECTION MODELS WITH 3D S...

引用

IEEE International conference on image processing (ICIP)

作者： Corley, Isaac Najafirad, Peyman Univ Texas San Antonio Secure Artificial Intelligence Lab Auton AILA San Antonio TX USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

remote sensing change detection, identifying changes between scenes of the same location, is an active area of research with a broad range of applications. Recent advances in multimodal self-supervised pretraining have resulted in state-of-the-art methods which surpass vision models trained solely on optical imagery. In the remote sensing field, there is a wealth of overlapping 2D and 3D modalities which can be exploited to supervise representation learning in vision models. In this paper we propose Contrastive Surface-image Pretraining (CSIP) for joint learning using optical RGB and above ground level (AGL) map pairs. We then evaluate these pretrained models on several building segmentation and change detection datasets to show that our method does, in fact, extract features relevant to downstream applications where natural and artificial surface information is relevant.(1)

关键词： self-supervised learning contrastive learning remote sensing above ground level maps

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