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

共 3738 条记录，以下是11-20 订阅

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Prompting DirectSAM for Semantic Contour Extraction in remote sensing images

Prompting DirectSAM for Semantic Contour Extraction in Remot...

引用

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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Radiometric resolution transformation of remote sensing based on histogram matching 6

Radiometric resolution transformation of remote sensing base...

引用

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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Dense object detection for remote sensing images based on multi-scale partitioning and super-resolution optimization 24

Dense object detection for remote sensing images based on mu...

引用

3rd International conference on signal processing, Computer Networks and Communications, SPCNC 2024

作者： Li, Lu Zhu, Xia Ni, Shaofeng Gao, Fan State Grid Location based Service Co. Ltd. Beijing China State Grid Information & Telecommunication Group Co. Ltd. Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (纸本)9798400710834

Detecting dense objects in remote sensing image is a critical yet challenging task due to variations in target scale, image resolution, and complex backgrounds. Traditional detection methods often struggle with these dense regions, particularly for small or overlapping objects. To overcome these limitations, this study introduces an innovative approach that combines multi-scale partitioning with super-resolution optimization to improve the detection of densely packed objects. By applying K-Means clustering to identify regions of interest (ROI) and using a local super-resolution network to enhance these areas, we improve the clarity and detectability of small-scale objects. Additionally, we implement a global-local feature extraction architecture, where global features are captured using Transformer-based attention, while local features focus on fine-grained object details. Non-Maximum Suppression (NMS) is employed to fuse multi-scale features and eliminate redundant detections. Experimental results on the DOTA and HRSC2016 datasets demonstrate the effectiveness of our approach, achieving superior performance over state-of-the-art methods in detecting small and densely packed targets. This method shows significant promise for remote sensing applications requiring high-precision target detection. © 2024 Copyright held by the owner/author(s).

关键词： Proximity sensors

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TriFormer: Triple Branch Heterogeneous Transformers for UHD image Restoration

TriFormer: Triple Branch Heterogeneous Transformers for UHD ...

引用

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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Robust Multilayer Matrix Factorization for Blind Hyperspectral image Unmixing 16

Robust Multilayer Matrix Factorization for Blind Hyperspectr...

引用

16th International conference on signal processing Systems, ICSPS 2024

作者： Yang, Gang Southwest China Institute of Electronic Technology Chengdu China

ISBN: (纸本)9781510689251

Hyperspectral image unmixing estimates a collection of constituent materials (called endmembers) and their corresponding proportions (called abundances), which is a critical preprocessing step in many remote sensing applications. In this paper, we propose a novel robust multilayer matrix factorization (RMLMF) approach for hyperspectral image unmixing. Firstly, instead of sampling all observed pixels, the endmembers are modeled as a linear combination of candidate endmembers to reduce the redundant spectra and alleviate heavy computational cost. Secondly, to explore the hidden information, the shallow decomposition is extended into multilayer structure. Furthermore, considering that hyperspectral images (HSIs) still suffer from a variety of noises, L2,1-norm is employed to measure the reconstruction error of the multilayer structure. Experimental results on both synthetic and real data sets demonstrate the superiority of the proposed RMLMF over many state-of-the-art algorithms. © 2025 SPIE.

关键词： Matrix factorization

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Research on speckle image reconstruction method based on deep learning 24

Research on speckle image reconstruction method based on dee...

引用

2024 International conference on virtual Reality, image and signal processing, ICviSP 2024

作者： Li, Xuandong Chen, Musheng College of physics and information engineering Quanzhou Normal University Fuzhou China College of Photonic and Electronic Engineer Fujian Normal University Fuzhou China

ISBN: (纸本)9798400710926

Optical speckle image reconstruction is crucial in fields such as biomedical imaging, optical coherence tomography, and optical remote sensing. However, speckle images often suffer from noise and scattering, leading to diminished image quality. This paper introduces a deep learning-based approach to enhance the accuracy and quality of reconstructed speckle images. The process begins with the construction of an experimental optical setup using a spatial light modulator to generate the corresponding speckle patterns. A novel network model, named pix2pix, is then developed based on the generative adversarial network (GAN), integrating both generator and discriminator modules. The model is trained using both the collected speckle image data and real data. Extensive experiments have validated the effectiveness of our approach, showing significant improvements in reconstruction accuracy and computational efficiency. The results indicate that, compared to traditional methods, the deep learning-based technique greatly enhances image clarity, effectively reduces noise, and demonstrates superior generalization in image reconstruction. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Hyperspectral image methane detection based on DETR 16

Hyperspectral image methane detection based on DETR

引用

2024 16th International conference on Graphics and image processing, ICGIP 2024

作者： Liu, Qiwen Li, Xiaoli Du, Zhenlong School of Computer Science and Technology Nanjing TECH University Nanjing211816 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

As a potent greenhouse gas, methane (CH4) leakage not only intensifies global warming, but also brings huge security risks. In recent years, with the rapid development of remote sensing technology, methane leakage detection using hyperspectral images has become a research hotspot. Among them, the airborne visible Infrared Imaging Spectrometer (AviRIS-NG) shows great potential in methane leakage detection with its high spectral resolution and wide spectral coverage. However, due to the complexity of land type conditions and the interference of confounding factors similar to the spectral characteristics of methane, the existing analytical methods are often difficult to obtain ideal detection results. Therefore, based on the DETR model, a novel end-to-end spectral absorption wavelength sensing transformer target detection network is proposed to improve the accuracy and efficiency of methane gas leakage detection. The reference spectrum feature generator and query refiner modules together improve the design of traditional transformer. Based on the traditional linear filter, a new spectral linear filter is proposed, which can better whiten the background distribution and amplify the methane signal by strategically selecting the relevant pixels in the spectral domain. The method presented in this paper has certain significance for improving the detection of methane gas leakage. © 2025 SPIE.

关键词： remote sensing

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Spatial-Frequency Information Interaction Diffusion for SAR Colorization

Spatial-Frequency Information Interaction Diffusion for SAR ...

引用

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

作者： Zhang, Xupei Qin, Hanlin Li, Jingjing Geng, Jinni Gao, Zihan Yu, Yue School of Optoelectronic Engineering Xidian University Xi'an710071 China Xi'an Key Laboratory of Infrared Technology and System Xi'an710071 China

ISBN: (纸本)9798350368741

The inherent speckle noise and grayscale characteristics of synthetic aperture radar (SAR) images pose challenges to information perception and interpretation. To address this issue, we propose a novel conditional diffusion model with spatial-frequency information interaction for SAR colorization, namely SFI4SC. Specifically, we introduce a learnable wavelet transform module to obtain the global frequency information of the input SAR image and then take it as a guiding condition to assist the diffusion model for SAR colorization. Additionally, we improved the network structure for the diffusion model by designing and introducing a spatial and frequency information interaction module to achieve multidimensional information interaction for the network inputs, further enhancing perceptual details and geometric structures of the SAR colorization results. Experimental results in the real-world remote sensing dataset show that our approach can successfully enhance visual clarity and informational content while preserving the unique characteristics of the SAR images. The code will be made publicly available. © 2025 IEEE.

关键词： Diffusion Model image colorization Spatial-Frequency Interaction Synthetic aperture radar

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MMCD: Memory-Based Multimodal Change Detection

MMCD: Memory-Based Multimodal Change Detection

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

作者： Zhang, Limeng Zhang, Zenghui Wu, Juanping Guo, Weiwei Zhang, Tao Yu, Wenxian Shanghai Jiao Tong University Shanghai200240 China Center of Digital Innovation Tongji University Shanghai200092 China

ISBN: (纸本)9798350368741

Single-modal change detection methods based on optical or Synthetic Aperture Radar (SAR) images face challenges such as degradation due to adverse weather or noise interference. In contrast, multimodal change detection struggles with significant domain gaps between different modalities. Inspired by the SAM2 model's temporal memory mechanism for video segmentation, this paper introduces the concept of memory into change detection and proposes a novel approach called Memory-based Multimodal Change Detection (MMCD). By treating change detection as a temporal problem and modeling remote sensing images as video sequences, the proposed method integrates historical optical images with current SAR images to enhance detection accuracy. Additionally, a difference map enhancement module is introduced to mitigate false changes caused by modality discrepancies. Experimental results show that this approach achieves state-of-the-art performance in multimodal change detection, demonstrating the effectiveness of the proposed method. © 2025 IEEE.

关键词： difference enhancement Memory attention multimodal change detection optical image assistance

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remoteTrimmer: Adaptive Structural Pruning for remote sensing image Classification

RemoteTrimmer: Adaptive Structural Pruning for Remote Sensin...

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

作者： Guangwenjie Zou Liang Yao Fan Liu Chuanyi Zhang Xin Li Ning Chen Shengxiang Xu Jun Zhou Hohai University Nanjing China Griffith University Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Since high resolution remote sensing image classifi-cation often requires a relatively high computation complexity, lightweight models tend to be practical and efficient. Model pruning is an effective method for model compression. However, existing methods rarely take into account the specificity of remote sensing images, resulting in significant accuracy loss after pruning. To this end, we propose an effective structural pruning approach for remote sensing image classification. Specifically, a pruning strategy that amplifies the differences in channel importance of the model is introduced. Then an adaptive mining loss function is designed for the fine-tuning process of the pruned model. Finally, we conducted experiments on two remote sensing classification datasets. The experimental results demonstrate that our method achieves minimal accuracy loss after compressing remote sensing classification models, achieving state-of-the-art (SoTA) performance.

关键词： Training Adaptation models Accuracy image coding Computational modeling signal processing algorithms signal processing Speech processing remote sensing image classification

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