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检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing XXIX"

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JPEG Artifact Removal for Hyperspectral images Based on Spatial-Spectral Regularization

JPEG Artifact Removal for Hyperspectral Images Based on Spat...

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Asia-Pacific-signal-and-Information-processing-Association Annual Summit and conference (APSIPA ASC)

作者： Eguchi, Ryunosuke Kobayashi, Iori Ono, Shunsuke Matsuoka, Ryo Univ Kitakyushu Fukuoka Japan Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9798350300673

A hyperspectral image (HSI) contains vast data, storing hundreds of spectral information with higher resolution than an RGB image. Therefore, considering practical aspects such as data transmission and storage, it is essential to explore the application of standard JPEG compression to an HSI. Conventional JPEG artifact removal methods, such as block and mosquito noise, recover a JPEG image by improving spatial smoothness under the quantization constraint of the DCT coefficients. However, these methods do not consider the application of JPEG compression to HSIs and the low rank of the spectral characteristics of HSIs. This paper proposes a novel optimization method for removing JPEG artifacts from an HSI. Specifically, we formulate a convex optimization problem by introducing a regularization term based on the nuclear norm to the conventional optimization problems. This regularization promotes low rankness in the spectral domain. To solve the proposed problem, we utilize the alternating direction method of multipliers (ADMM). Experiments demonstrate the effectiveness of the proposed method compared to several existing methods applied to both remote and non-remote sensing HSIs.

关键词： Hyperspectral image JPEG compression total variation low rank

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MUTUALLY SUPERVISED LEARNING VIA INTERACTIVE CONSISTENCY FOR GEOGRAPHIC OBJECT SEGMENTATION FROM WEAKLY LABELED remote sensing imageRY 30

MUTUALLY SUPERVISED LEARNING VIA INTERACTIVE CONSISTENCY FOR...

引用

30th IEEE International conference on image processing (ICIP)

作者： Liu, Yanan Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9781728198354

Geographic object segmentation from weakly annotated remote sensing images has become a research hotspot, since it can greatly reduce the costly annotation burden. Recently, it has made remarkable progress by dividing it into two sequential steps, which first produces pseudo labels (PLs) from a localization model, then uses PLs to train a segmentation network for final results. The one-way knowledge transfer in the above schemes, however, lacks the feedback from the segmentation to localization model which may result in suboptimal performance. In this paper, we develop a mutually supervised learning (MSL) framework for geographic object segmentation under image-wise annotations. First, MSL learns the localization and segmentation model concurrently and employs the output from each of the two models as pseudo supervision for the other one by formulating an interactive consistency loss, which encourages each model to provide positive feedback and guidance to the other. Then, a variance-based uncertainty estimation strategy is introduced to explicitly approximate the uncertainty of the PLs, which helps to alleviate the detrimental effect caused by learning from noisy PLs. Finally, we design a multi-scale activation integration-based localization model to produce high-quality localization maps. Comprehensive evaluations and ablation studies validate the superiority of the MSL framework.

关键词： remote sensing geographic object segmentation mutually supervised learning

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Visual Instruction Tuning for Holistic and Regional remote sensing imagery Comprehension 2

Visual Instruction Tuning for Holistic and Regional Remote S...

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2nd IEEE International conference on signal, Information and Data processing, ICSIDP 2024

作者： Zhang, Wei Cai, Miaoxin Zhang, Tong Yin, Zhuang Mao, Xuerui National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing China Advanced Research Institute of Multidisciplinary Sciences China School of Mechatronical Engineering Yangtze Delta Region Academy of Beijing Institute of Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331515669

Recently, the visual instruction multimodal large language models (MLLMs) have been extensively studied in the nature scenario. However, current remote sensing (RS) MLLMs mainly focus on image-level understanding and typically allow interaction only through text instructions, resulting in limited accuracy and efficiency. To address those limitations, a visual instruction model is proposed in this article to extend MLLM's fine-grained perception ability by incorporating bounding boxes with language instruction, aiming at achieving region-level visual understanding. To achieve this goal, a visual instruction dataset featuring multi-modal box-based region-text pairs is constructed. Furthermore, the visual instructions and images are encoded by the different encoders and subsequently fed into a large language model (LLM) along with text instruction tokens for tuning. Finally, experimental results demonstrate the proposed model's promising performance in region-level image comprehension. © 2024 IEEE.

关键词： Adversarial machine learning

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Prompting DirectSAM for Semantic Contour Extraction in remote sensing images

Prompting DirectSAM for Semantic Contour Extraction in Remot...

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

作者： Miao, Shiyu Chen, Delong Liu, Fan Zhang, Chuanyi Gu, Yanhui Guo, Shengjie Zhou, Jun College of Computer Science and Software Engineering Hohai University Nanjing China Hong Kong University of Science and Technology Hong Kong School of Computer and Electronic Information Nanjing Normal University Nanjing China School of Information and Communication Technology Griffith University QLD Australia

ISBN: (纸本)9798350368741

The Direct Segment Anything Model (DirectSAM) excels in class-agnostic contour extraction. In this paper, we explore its use by applying it to optical remote sensing imagery, where semantic contour extraction-such as identifying buildings, road networks, and coastlines-holds significant practical value. Those applications are currently handled via training specialized small models separately on small datasets in each domain. We introduce a foundation model derived from DirectSAM, termed DirectSAM-RS, which not only inherits the strong segmentation capability acquired from natural images, but also benefits from a large-scale dataset we created for remote sensing semantic contour extraction. This dataset comprises over 34k image-text-contour triplets, making it at least 30 times larger than individual dataset. DirectSAM-RS integrates a prompter module: a text encoder and cross-attention layers attached to the DirectSAM architecture, which allows flexible conditioning on target class labels or referring expressions. We evaluate the DirectSAM-RS in both zero-shot and fine-tuning setting, and demonstrate that it achieves state-of-the-art performance across several downstream benchmarks. © 2025 IEEE.

关键词： remote sensing Semantic contour extraction Vision-language learning

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An improved yolov5s method of vehicle target detection in remote sensing images 4

An improved yolov5s method of vehicle target detection in re...

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4th International conference on signal image processing and Communication, ICSIPC 2024

作者： Liu, Junmiao Wang, Haichen School of Information Engineering Chang'an University Xi’an710064 China

ISBN: (纸本)9781510682467

In target detection, remote sensing images have characteristics such as complex background, dense target distribution, and small targets, which lead to poor detection results, missed detections and false detection. This paper presents ABSYOLOv5s, a target detection method based on YOLOv5s. In order to overcome the difficulties in detecting small target and prevent missed detection and error detection, a self-attention mechanism with position information encoding is introduced to strengthen the fusion of feature information within the same scale. BiFPN is used in the neck network to better integrate low-level and high-level feature information. In addition, the ShapeIoU loss function proposed by Zhang et al. is applied to make the model in this article concentrate more on bounding box’s shape and scale to improve detection accuracy. This paper conducted a complete experiment on the remote sensing vehicle data set COWC. The experiment shows that all indicators of the improved model have been improved, with the accuracy increased by 0.4%, recall, mAP, mAP@0.5/0.95, have improved by 1.1 per cent, 0.7 per cent, and 0.4 per cent, respectively. © 2024 SPIE.

关键词： Proximity sensors

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FAST CAUCHY-RICIAN MODELLING OF SAR imageS WITH METHOD OF ALGEBRAIC MOMENTS ESTIMATOR

FAST CAUCHY-RICIAN MODELLING OF SAR IMAGES WITH METHOD OF AL...

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

作者： Li, Mutong Kuruoglu, Ercan E. Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Int Grad Sch 2279 Lishui Rd Shenzhen Peoples R China

ISBN: (纸本)9798350302615

SAR technology has been intensively implemented for geo-sensing and mapping purposes due to its advantages of high azimuthal resolution and weather-independent operation compared to other remote sensing technologies. Modelling SAR image data consequently becomes a prominent topic of interest, especially for data populations with impulsive signal features, which are common in SAR images of urban areas. A recently proposed model named Cauchy-Rician has manifested great potential in modelling extremely heterogeneous SAR images, yet the work only provided a MCMC-based parameter estimator that demands considerable computational power. In this work, a novel analytical parameter estimation method based on algebraic moments is proposed to provide stable and accurate estimation of the parameters of the Cauchy-Rician model with significant improvement on computation speed.

关键词： SAR imagery urban remote sensing Cauchy-Rician distribution parameter estimation algebraic moments

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Radiometric resolution transformation of remote sensing based on histogram matching 6

Radiometric resolution transformation of remote sensing base...

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6th International conference on Geoscience and remote sensing Mapping, GRSM 2024

作者： Cheng, Jinsong Dai, Jiguang Zhang, Tengda School of Surveying Mapping and Geographic Sciences Liaoning Technical University Liaoning Fuxin123000 China

ISBN: (纸本)9781510688018

remote sensing image radiometric resolution transformation plays a fundamental role in data storage, transmission efficiency, processing speed, image visualization, data simplification, and analysis. As a crucial preprocessing step, radiometric resolution transformation is commonly based on gray-level stretching methods. However, these methods are often time-consuming due to the complexities involved in method selection and parameter adjustment. This paper proposes a method based on Generative Adversarial Networks (GANs), inspired by histogram matching, to achieve radiometric resolution transformation of remote sensing images. Given the significant dynamic range changes in radiometric resolution transformation, establishing a direct mapping relationship is challenging. Therefore, a Discrete Histogram Approximation Loss is designed to compute the similarity between High Radiometric Resolution images (HRRI) and Standard-Resolution Radiometric images (SRRI), strengthening the mapping correlation and optimizing the visual features of the generated images. remote sensing images contain a variety of ground objects and complex detail textures;hence, Structure Correction Loss is used to constrain the structural features, ensuring the clarity and accuracy of the resultant images. This study was validated using a self-made dataset, and comparative experiments with other methods were conducted. The results demonstrate that the proposed method achieves a peak signal-to-noise ratio of 29.20dB and a structural similarity index of 86.8%, significantly outperforming other methods. These findings confirm the feasibility of the proposed approach, with the generated images exhibiting high color and structural accuracy and realism. © 2025 SPIE.

关键词： Radiometry

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Analysis Of Extreme Rainstorm Process Using Multi-source remote sensing Data: A Case Study of Shenyang

Analysis Of Extreme Rainstorm Process Using Multi-source Rem...

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2024 International conference on remote sensing, Mapping, and image processing, RSMIP 2024

作者： Li, Dian Cui, Jinglin Zhang, Shuai Lu, Yang Hou, Wanting Shenyang Meteorological Bureau of Liaoning Province Shenyang110168 China

ISBN: (纸本)9781510680012

Using the data from GNSS-PWV, FY-2G Satellite detection products, wind profile radar and conventional observation data, an extreme rainstorm process on 7th August 2018 in Shenyang was analyzed. The results show that PWV values decreased first and then increased 2-3hours before the start of heavy rainfall. There is a consistent correspondence between the decrease in TBB Value and the enhancement of rainfall intensity, but the center of high precipitation is slighty westward compared to the center of low TBB Value. There are two peaks for vertical speed and signal to noise ratio (SNR) during the main period of rainstorm. The first peak time is corresponding to the peak time of the storm relative helicity (SRH) and refractive index structure constant, it shows that the storm system is under favorable environmental conditions. Peak rain intensity appears within 1~2 h after the first peak time. The second peak time is corresponding to the time of cold air intrusion. The refractive index structure constant and storm relative helicity can forecast strong precipitation. © 2024 SPIE.

关键词： remote sensing

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TriFormer: Triple Branch Heterogeneous Transformers for UHD image Restoration

TriFormer: Triple Branch Heterogeneous Transformers for UHD ...

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

作者： Ma, Xinchen Lu, Yue School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (纸本)9798350368741

Ultra-high-definition (UHD) image restoration is becoming a critical research area due to the increasing demand for high-quality visual content in various applications, including autonomous driving, remote sensing, digital entertainment, etc. However, UHD image restoration tasks present notable challenges, including the significant computational burden posed by the large image size scale, the difficulty in preserving fine details and textures, and the increased susceptibility to noise and artifacts during the restoration process. In this paper, we propose a novel triple-branched optimized heterogeneous transformer for UHD image restoration, named TriFormer. Specifically, our method embeds a high-resolution CNN branch for high-frequency features, a low-resolution pixel-wise attention branch for low-frequency features, and a channel-wise attention branch for feature fusion. We perform experiments across two UHD image restoration tasks: enhancing low-light images and deblurring. Results demonstrate that our model outperforms state-of-the-art approaches both quantitatively and qualitatively. The code will be made available at https://***/Chloemxxxxc/TRIFORMER. © 2025 IEEE.

关键词： image restoration transformer Ultra-high-definition(UHD)

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An enhanced feature matching multi-temporal port remote sensing image registration network E-SuperGlue

An enhanced feature matching multi-temporal port remote sens...

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2024 International conference on image, signal processing, and Pattern Recognition, ISPP 2024

作者： He, Xinyu Feng, Shuyi Shao, Wenbo He, Hengxiang Wu, Liming Shanghai Aerospace Electronic Technology Institute Department of Aerospace Electronic Research Shanghai China

ISBN: (纸本)9781510680425

Multi-temporal collaborative analysis of port scenes can enhance the representation ability of image scenes, and image registration is required before multi-temporal analysis. In this paper, an image registration network E-SuperGlue with enhanced feature matching is proposed to solve the problems such as the difficulty of extracting feature points and matching feature descriptors for port multi-temporal image registration. Our network takes SuperGlue network as the basic framework. Firstly, Focus is introduced into the feature extraction network to increase the number to increase the number and detection rate of feature points. Secondly, LFPE module is added to feature matching network coding module to improve the information efficiency of feature descriptor coding. Finally, an improved multi-layer sensing structure E-MLP is added to the feature matching network to improve the utilization rate of channel information. © 2024 SPIE.

关键词： Graph neural networks

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