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

共 3572 条记录，以下是11-20 订阅

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Learning Deep Frequency Degradation Prior for remote sensing Spatio-temporal Fusion

Learning Deep Frequency Degradation Prior for Remote Sensing...

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

作者： Bian, Yiting Hu, Shenglong Song, Huihui Zhang, Kaihua Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350368741

Existing deep learning-based remote sensing spatiotemporal fusion (STF) relies on a data-driven paradigm without considering the degradation prior modeling from the coarse-to fine-resolution images. This makes the learned model easy to overfit to the training dataset, resulting in poor domain generalization across different datasets. To this end, this paper presents a deep frequency degradation prior to STF, dubbed as DeepFDP. The DeepFDP is based on a statistical observation that the frequency feature distributions of the tokens from the coarse- and fine-resolution images in different datasets have a low intra-resolution variance and a high inter-resolution variance with compact well-separated clusters. Therefore, the DeepFDP first designs a frequency proximity module to learn the mapping function between the token frequency representations of the coarse- and fine-resolution images, which is to narrow their feature distribution difference. Since the statistical properties of the token frequency representations are independent of the land-cover classes, the DeepFDP can be learned on a training set of limited image pairs without extra-supervision signals, which has a favorable zero-shot generalization capability across different datasets. Then, to faithfully recover the land-surface details, a high-frequency feature modulation module is designed that uses the fine-resolution image as guidance to progressively learn the multi-scale residual features in a coarse-to-fine fashion, yielding the fused features with rich high-frequency details. Finally, the progressively-fused features at each stage are fed into a hybrid fusion module, yielding the fine-resolution image prediction. Extensive evaluations on LGC and CIA datasets demonstrate favorable performance of the DeepFDP over state-of-the-art methods. Especially, the DeepFDP also shows a good zero-shot generalization performance when training on LGC and testing on CIA. © 2025 IEEE.

关键词： domain generalization frequency degradation prior remote sensing Spatio-temporal fusion

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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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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...

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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

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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 ...

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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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