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

共 3607 条记录，以下是21-30 订阅

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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

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

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： image synthesis Virtual environments Coherence signal processing Diffusion models Adversarial machine learning Sensors Speech processing Fake news remote sensing

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Bridging Task Boundaries: remote sensing image-Text Retrieval via Dictionary-Driven Adaptation

Bridging Task Boundaries: Remote Sensing Image-Text Retrieva...

引用

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

作者： Junwei Xu Tao Huang Zhenyu Wang Weisheng Dong Xin Li Hangzhou Institute of Technology Xidian University Hangzhou China the School of Artificial Intelligence Xidian University Xi’an China Department of Computer Science State University of New York at Albany New York USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Given image (or text), remote sensing image-text retrieval (RSITR) aims to retrieve corresponding text (or image) within diverse remote sensing data. However, due to the complex scenes and compact distribution of targets in remote sensing data, existing methods, particularly those leveraging large models like CLIP, often generate features with high intra-modal similarity and insufficient distinctive characteristics, thus resulting in suboptimal retrieval performance. To address these issues, we pro- pose a novel dictionary-based RSITR method that jointly models image and text feature estimation. Specifically, by incorporating a general dictionary and the corresponding sparse coefficients, our method more effectively captures the correlations between the learned features. Furthermore, we introduce adaptive weighted metric learning on a sample-by-sample basis to encourage the model to focus on more challenging negative samples, promoting fine-grained feature alignment. The extensive experiments on the RSICD and RSITMD datasets demonstrate the effectiveness of our method, demonstrating significant improvements in retrieval performance.

关键词： Measurement Adaptation models Dictionaries Semantics Estimation signal processing Feature extraction Data models Speech processing remote sensing

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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

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

作者： Fangzhou Han Tianyi Yu Lamei Zhang Lingyu Si Yiqi Zhang 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: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： Visualization Semantics Termination of employment Transforms Interference signal processing Logic gates Robustness Speech processing remote sensing

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MMCD: Memory-Based Multimodal Change Detection

MMCD: Memory-Based Multimodal Change Detection

引用

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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AMIC: A Novel Hybrid image Compression for Multi-spectral Satellite imagery 3rd

AMIC: A Novel Hybrid Image Compression for Multi-spectral S...

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3rd International conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Nhat, Trinh Le Nhat, Tan Thai Nguyen, Luong Vuong Vu, Bui Cao Department of Artificial Intelligence FPT University Danang Viet Nam

ISBN: (纸本)9789819624676

Multi-spectral satellite imagery (MSSI) offers a crucial means of collecting environmental and geological data from dangerous, remote locations. However, the substantial volume of such imagery complicates its storage and transmission, making efficient image compression indispensable. Traditional compression methods such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Consultative Committee for Space Data Systems (CCSDS), Bandelet, and JPEG 2000 have provided high reliability, simple implementation, and effectiveness, yet they lack sufficient adaptability. In contrast, Machine Learning (ML) offers a more adaptable, albeit complex and data-intensive, alternative. This paper proposed a hybrid approach, called AMIC, that combines the robustness of traditional methods with the flexibility of ML, using DWT to quantize image bands and ML to refine this process, thereby enhancing the compression quality with minimal computational load. The AMIC performance demonstrates high fidelity in compressed images, with Peak signal-to-Noise Ratio (PSNR) values ranging from 30dB to 42dB and Structural Similarity Index (SSIM) values above 0.78, ensuring excellent preservation of image details. The approach significantly optimizes compression and decompression times, achieving a reduction to 43.26% of the original size. This hybrid method ensures high-quality satellite image compression for improved transmission and storage. It broadly impacts real-time data processing across various fields, including environmental monitoring and remote sensing research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Satellite imagery

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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

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

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： Accuracy Semantic segmentation image edge detection Noise Interference Robustness Sensors Speech processing Optimization remote sensing

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A Cost-effective Solution for remote sensing image Segmentation via Train/Test-Time Adaptation

A Cost-effective Solution for Remote Sensing Image Segmentat...

引用

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

作者： Wei Chen Xin Luo Yulin He Tianrui Liu Di Wu Tianhang Guo Yuhang Li Yuhua Tang National University of Defense Technology Changsha China National Innovation Institute of Defense Technology Academy of Military Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

remote sensing image (RSI) segmentation has made significant strides, emerging as a leading solution for interpreting remote sensing data. However, due to the substantial domain gap between different remote sensors and limited computational resources, existing RSI segmentation methods often suffer from poor generalization performance. To address these challenges, we propose a COst-effective and Whole-process Domain Adaptation solution, namely COWDA, which adapts models at both the train and test time through three key phases: 1) Source-data Domain Alignment: We employ traditional image stylization techniques to translate source images into target-style alternatives, which avoids the computationally intensive need for auxiliary neural network models. 2) Target-data Train-time Fine-tuning: We propose a joint positive and negative learning (JPNL) algorithm that adds both positive and negative samples to effectively learn domain-invariant knowledge from noisy pseudo-labeled target data. 3) Test-time Adaptation: We propose an entropy-weighted test-time adaptation strategy to update the trained model with online test samples, further enhancing its performance. Extensive experiments on two widely-used domain adaptation benchmarks for remote sensing show that COWDA improves state-of-the-art counterparts by 1.4% and 2.6% F1 scores, respectively.

关键词： Adaptation models image segmentation Computational modeling signal processing algorithms Benchmark testing Data models Noise measurement Speech processing remote sensing signal resolution

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Frequency-Domain Guided Multiple Parallel Kernels Network for Low-Light remote sensing image Enhancement

Frequency-Domain Guided Multiple Parallel Kernels Network fo...

引用

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

作者： Jingxuan Zhou Hao Li Jinlong Wang Xiongxin Tang Fanjiang Xu Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to dark environments, optical aberrations, etc, the remote sensing images are often submerged under low contrast degradation, which greatly hinders their practical applications for agricultural management and other related tasks. The surface features of remote sensing images are often continuously distributed in space, thus, the sizes of the network’s receptive fields and its ability to learn long-range dependencies are crucial for restoring low-light remote sensing images. Existing methods based on CNN provide limited receptive fields, while Transformer-based methods are constrained by their quadratic computational complexity. To cope with these issues, we propose a novel low-light remote sensing image enhancement network that combines multi-scale receptive fields with frequency-domain attention. Specifically, this network employs multiple parallel kernels of varying sizes to learn multi-scale local features in the spatial domain and complements frequency-domain information to learn global long-range correlations, which achieves local-global feature extraction and further facilitates subsequent degraded images enhancement. We have conducted extensive experiments to demonstrate that our network outperforms existing methods quantitatively and achieves exceptional visual performance, which fully highlights the effectiveness and superiority of our method in enhancing low-light remote sensing images.

关键词： Visualization Frequency-domain analysis Transforms Speech enhancement Feature extraction Transformers image restoration Kernel remote sensing image enhancement

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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...

引用

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

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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 (M 2 F 2 Net). 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.

关键词： Degradation Visualization image resolution Convolution Semantics Feature extraction Transformers Convolutional neural networks Speech processing remote sensing

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TriFormer: Triple Branch Heterogeneous Transformers for UHD image Restoration

TriFormer: Triple Branch Heterogeneous Transformers for UHD ...

引用

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

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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://***/Chloe-mxxxxc/TRIFORMER.

关键词： Visualization Technological innovation Noise Graphics processing units Entertainment industry Computer architecture Transformers image restoration Speech processing remote sensing

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