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

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DiffRS: An Extensible Diffusion Model for remote sensing image Generation

DiffRS: An Extensible Diffusion Model for Remote Sensing Ima...

引用

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

作者： Huang, Xinyue Niu, Xin Jiang, Jingfei Pan, Hengyue National University of Defense and Technology Changsha China

ISBN: (纸本)9798350368741

remote sensing image generation is of great value for virtual environment creation and adversarial learning for fake news detection. It could also address the learning sample shortage in the region of interest. However, most current image generation methods are limited to producing images of fixed sizes, few studies on extensible natural image generation largely focus on the stitching of random contents, lacking effective exploration of contextual information, which weakens the coherence of the extended images. To address this problem, we propose an extensible generation method for remote sensing images with the model DiffRS. This approach allows for sequential extension of arbitrary sizes by exploring the generated neighboring regions. The method is particularly suitable for scenes like remote sensing images where a generation block could cover multiple independent targets, rather than natural image tasks which may stitch across regions to form a completely target. Compared to the state-of-the-art extensible generation methods, DiffRS could improve the large scale image generation with better structure consistency, richer details and higher realism. Experiments showed that DiffRS could improve the FID score by 4.6% and 3.2% respectively in comparison with the MultiDiffusion and Mixture of Diffuser models. © 2025 IEEE.

关键词： Diffusion Model image Extensible Generation remote sensing image Sequence Generation

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Target point alignment with Gaussian mixture models 16

Target point alignment with Gaussian mixture models

引用

16th International conference on signal processing Systems, ICSPS 2024

作者： Li, Chuchen Zhao, Bingqing Tang, Shanjun Li, Xing Liao, Xin Shanghai Electro-Mechanical Engineering Institute Shanghai201109 China

ISBN: (纸本)9781510689251

Under the remote sensing perception scenario, the sensor field of view alignment is the key step for global information acquisition. However, the model can only utilize position information for alignment, as the remote sensing image has limited resolution and the size of targets is relatively small. And because of inevitable noise and complex background information, false alarms are difficult to eliminate. Moreover, the number of targets that a remote sensor can capture can be scarce compared with the general point registration task. With all these problems, precisely aligning remote sensing images that contain similar targets is extremely difficult. In this paper, we propose Gaussian mixture models with shape and false alarm sensitive strategy to tackle the limited resolution remote sensing image alignment task. We first apply shape context as a structure description to add intrinsic information to our model. Then we introduce a false alarm sensitive strategy to refine the information from the shape context to further enhance the alignment ability of the model. We simulate different target structures and conditions as our evaluation dataset. Thorough experimental results show the effectiveness of our proposed method. © 2025 SPIE.

关键词： Optical remote sensing

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Hazy remote sensing image Semantic Segmentation with Weak Annotations via Pre-training Optimization and Co-training

Hazy Remote Sensing Image Semantic Segmentation with Weak An...

引用

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

作者： Xu, Junda Zhang, Libao School of Artificial Intelligence Beijing Normal University Beijing100875 China

ISBN: (纸本)9798350368741

In recent years, weakly supervised semantic segmentation has emerged as a prominent research topic in the field of remote sensing image semantic segmentation due to its cost-effective labeling advantages. However, the presence of haze in remote sensing images poses significant challenges to accurate semantic segmentation. Despite the numerous haze removal methods developed for remote sensing images, their efficacy in the subsequent task of semantic segmentation remains inadequate. To address these issues, this paper aims to enhance the robustness of the segmentation network against haze interference by proposing a weakly supervised semantic segmentation framework based on pre-training optimization and dual-network co-training. The proposed approach employs a pseudo-label optimization network to effectively filter out noise interference and subsequently trains two parallel segmentation networks that mutually guide each other for enhanced robustness. Additionally, an edge optimization loss is introduced to improve prediction accuracy by incorporating both edge and texture information. Experimental comparisons with alternative methods across multiple datasets validate the superior performance of our proposed method. © 2025 IEEE.

关键词： co-training image dehazing remote sensing image Semantic segmentation weakly supervised learning

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SlotFusion: Object-Centric Audiovisual Feature Fusion with Slot Attention for remote sensing Scene Recognition

SlotFusion: Object-Centric Audiovisual Feature Fusion with S...

引用

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

作者： Han, Fangzhou Yu, Tianyi Zhang, Lamei Si, Lingyu Zhang, Yiqi Dept. of Information Engineering Harbin Institute of Technology Harbin China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Despite significant advancements in remote sensing multimodal learning, particularly in image-image feature fusion, the exploration of audio-image feature fusion remains insufficient. Given the complexity and redundancy of ground objects in remote sensing images, accurately aligning audio features with image features during the fusion process is a critical challenge. In this paper, we introduce an object-centric feature fusion method named SlotFusion. By employing a slot attention-based feature decoupling module and a slot-based audiovisual feature fusion module, we transform modality features with complex semantic information into a set of slot features corresponding to object units and use gated activation units to adaptively implement object-centric feature fusion. Experiments on the Audio Visual Aerial Scene Recognition dataset (ADVANCE) demonstrate that the proposed SlotFusion significantly improves remote sensing scene recognition performance, with a 7.04% increase in overall accuracy compared to previous methods, achieving state-of-the-art results. © 2025 IEEE.

关键词： Audiovisual feature fusion Gated feature fusion Object-centric learning Slot attention

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M2F2Net: Multi-stage Mixed Feature Fusion Network For remote sensing Change Detection

M2F2Net: Multi-stage Mixed Feature Fusion Network For Remote...

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

作者： Gu, Binhao Song, Lei Gu, Binjie Kong, Youyong Southeast University Nanjing China Jiangnan University Wuxi China

ISBN: (纸本)9798350368741

remote sensing image change detection (CD) seeks to analyze and discern changes in surface objects through the use of multi-temporal remote sensing imagery. However, as image resolution advances, existing methods often fall short in capturing comprehensive visual feature representations, and their networks are prone to spatial degradation. This results in incomplete boundary detection and difficulties in identifying subtle changes. To overcome these challenges, this paper introduces a Siamese U-Net architecture incorporating Multistage Mixed Feature Fusion (M2F2Net). The proposed model leverages a convolutional neural network (CNN) as the main encoder for local feature extraction, while employing a Transformer-based auxiliary encoder to capture global features. Furthermore, we introduce a Feature Fusion Module (FFM) to facilitate the efficient integration of local and global information. Additionally, we propose a novel convolutional unit and a Spatial Attention Module (SAM) designed to enhance the extraction of image features. Experimental results confirm that the proposed approach delivers substantial improvements across multiple evaluation criteria, while offering a superior accuracy compared to existing state-of-the-art change detection methods. © 2025 IEEE.

关键词： Change detection (CD) Deep learning Multi scale remote sensing images Self attention mechanism

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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...

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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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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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Joint Semantic Segmentation of Optical and SAR image in Hazy Environments via Cross-modal Information Rectification and Cross-attention Fusion

Joint Semantic Segmentation of Optical and SAR Image in Hazy...

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

作者： Fan, Xinyue Zhang, Libao School of Artificial Intelligence Beijing Normal University Beijing100875 China

ISBN: (纸本)9798350368741

Semantic segmentation is crucial in remote sensing image processing. In recent years, semantic segmentation using optical and SAR images for multi-modal fusion is gaining attention for its good results. The current research primarily encompasses two problems: 1) Existing fusion methods are designed for clear images and struggle in harsh weather. 2) Current fusion methods insufficiently capture multi-modal information correlation. This paper presents a joint semantic segmentation of optical and SAR in hazy environments network that incorporates channel fusion for feature enhancement and cross-attention for feature fusion, enabling efficient segmentation of hazy optical images. First, we design a channel-guided crossmodal information correction module. This module regards fog as noise and uses high-confidence features of one modality to calibrate the other modality, thereby reducing the impact of fog. Secondly, to address the issue of insufficient fusion of multimodal information, we introduce a cross-attention fusion module to effectively combine the complementary information from two streams leveraging the large receptive field enabled by self-attention. The implementation results show that this method has better results for the semantic segmentation of blurry remote sensing images. © 2025 IEEE.

关键词： multi-modal fusion optical images remote sensing SAR images Semantic segmentation

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Enhancing remote sensing Vision-Language Models for Zero-Shot Scene Classification

Enhancing Remote Sensing Vision-Language Models for Zero-Sho...

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

作者： El Khoury, Karim Zanella, Maxime Gérin, Benoît Godelaine, Tiffanie Macq, Benoît Mahmoudi, Saïd De Vleeschouwer, Christophe Ayed, Ismail Ben UCLouvain Belgium UMons Belgium ÉTS Montreal Canada

ISBN: (纸本)9798350368741

Vision-Language Models for remote sensing have shown promising uses thanks to their extensive pretraining. However, their conventional usage in zero-shot scene classification methods still involves dividing large images into patches and making independent predictions, i.e., inductive inference, thereby limiting their effectiveness by ignoring valuable contextual information. Our approach tackles this issue by utilizing initial predictions based on text prompting and patch affinity relationships from the image encoder to enhance zero-shot capabilities through transductive inference, all without the need for supervision and at a minor computational cost. Experiments on 10 remote sensing datasets with state-of-the-art Vision-Language Models demonstrate significant accuracy improvements over inductive zero-shot classification. Our source code is publicly available on Github: https://***/elkhouryk/RS-TransCLIP. © 2025 IEEE.

关键词： remote sensing scene classification transductive inference vision-language models zero-shot

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