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

共 3584 条记录，以下是11-20 订阅

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GABOR FEATURE NETWORK FOR TRANSFORMER-BASED BUILDING CHANGE DETECTION MODEL IN remote sensing 31

GABOR FEATURE NETWORK FOR TRANSFORMER-BASED BUILDING CHANGE ...

引用

2024 International conference on image processing

作者： Osa, Priscilla Indira Zerubia, Josiane Kato, Zoltan Univ Genoa DITEN Dept Genoa Italy Univ Cote Azur INRIA Nice France Univ Szeged Inst Informat Szeged Hungary J Selye Univ Komarno Slovakia

ISBN: (纸本)9798350349405;9798350349399

Detecting building change in bitemporal remote sensing (RS) imagery requires a model to highlight the changes in buildings and ignore the irrelevant changes of other objects and sensing conditions. Buildings have comparatively less diverse textures than other objects and appear as repetitive visual patterns on RS images. In this paper, we propose Gabor Feature Network (GFN) to extract the distinctive repetitive texture features of buildings. Furthermore, we also design Feature Fusion Module (FFM) to fuse the extracted multiscale features from GFN with the features from a Transformer-based encoder to pass on the texture features to different parts of the model. Using GFN and FFM, we design a Transformer-based model, called GabFormer for building change detection. Experimental results on the LEVIR-CD and WHU-CD datasets indicate that GabFormer outperforms other SOTA models and in particular show significant improvement in the generalization capability. Our code is available on https://***/Ayana-Inria/GabFormer.

关键词： Transformer Gabor feature building change detection remote sensing image analysis

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A remote sensing image de-noising method based on wavelet packet decomposition and information entropy threshold function

A remote sensing image de-noising method based on wavelet pa...

引用

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

作者： Ma, Hongjin Zhao, Jiayu Zhang, Weiwei School of Science Xi'an University of Architecture and Technology Shaanxi Xi'an710055 China School of Mathematics and Statistics Northwestern Polytechnical University Shaanxi Xi'an710072 China

ISBN: (纸本)9781510680425

remote sensing images are widely used in various fields, such as geographic information systems, environmental monitoring, and agricultural management. However, remote sensing images are often corrupted by various noises during acquisition, transmission, and storage, which degrade the image quality and affect the image analysis and utilization. Therefore, de-noising remote sensing images is an important step to improve the image quality and application performance. In this paper, we propose a remote sensing image de-noising method based on wavelet packet decomposition and information entropy threshold function. The proposed method uses wavelet packet decomposition to perform multi-resolution analysis on remote sensing images, and combines information entropy threshold function to adaptively suppress noises. The experimental results show that the proposed method can effectively remove Gaussian noise and salt-and-pepper noise, and preserve the edge and detail information of remote sensing images. The proposed method outperforms the existing methods in terms of peak signal-to-noise ratio, structural similarity index, and visual quality. © 2024 SPIE.

关键词： Wavelet decomposition

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A lightweight object detection network based on YOLOv5 for SAR image 2

A lightweight object detection network based on YOLOv5 for S...

引用

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

作者： Shen, Aijia Zhao, Liangbo Xu, Fanyun Wang, Guoqing Liu, Wenchao Shen, Zimeng Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (纸本)9798331515669

remote sensing image target detection is one of the key technologies in the field of intelligent interpretation of remote sensing images, and it has significant application value in various areas, including military defense. When performing remote sensing image object detection on airborne and spaceborne platforms, the vast amount of remote sensing data processing and limited computational resources impose high real-time requirements on the object detection algorithms. This paper designs a lightweight object detection network model named YOLOv5-tiny, based on the existing deep learning network detection model YOLOv5s, and deploys it on the Jetson TX2 development board for training and testing. Experimental results show that the proposed YOLOv5-tiny model, when tested on the SAR-AIRcraft-1.0 with an input image size of 640x640, is 10 times smaller than YOLOv5s;it has a computational cost of 5.2 GFLOPs, which is 1/5 of YOLOv5s, and the processing time for a single image is reduced to half that of YOLOv5s, with only a 0.1% decrease in accuracy. © 2024 IEEE.

关键词： Object tracking

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Bag-of-Visual-Words Feature Extraction using Graph Convolutional Networks 7

Bag-of-Visual-Words Feature Extraction using Graph Convoluti...

引用

IEEE 7th International conference on Advanced Technologies, signal and image processing (ATSIP)

作者： Mastour, Montassar Farah, Mohamed Univ Manouba RIADI LR99ES26 ISAMM Univ Campus Manouba Manouba 2010 Tunisia

ISBN: (纸本)9798350351491;9798350351484

Feature representations are the fundamental building blocks for various image analysis tasks like retrieval and classification. Traditionally, the Bag-of-Visual-Words (BoVW) framework has been a well-established approach for feature extraction. However, BoVW exhibits limitations in capturing the complex spectral-spatial relationships and fine-grained details within remote sensing images (RSIs). This paper proposes a novel deep learning approach that addresses these shortcomings and fits well with the BoVW framework for RSI analysis. Our method leverages a hybrid architecture that combines a Convolutional Neural Network (CNN) and a Graph Convolutional Network (GCN). The CNN extracts high-level spatial features from RSIs capturing prominent visual patterns and spatial arrangements within the image. These extracted features are then fed into the GCN, which operates on a graph structure constructed from the image data. The GCN excels at learning complex relationships between image elements based on their spatial proximity and spectral information. processing the CNN outputs through the GCN aims to extract and enhance the features that encapsulate both the spatial layout and the intricate spectral-spatial relationships within RSIs. This potentially translates to improved performance in various RSI analysis tasks that demand a nuanced understanding of the image content.

关键词： Bag-Of-Visual-Words Graph Convolutional Network Convolutional Neural Network remote sensing image image processing image Classification

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remote sensing image UNEVEN HAZE REMOVAL BASED ON HAZE DENSITY ESTIMATION AND SALIENCY-DRIVEN DUAL CHANNEL FUSION 31

REMOTE SENSING IMAGE UNEVEN HAZE REMOVAL BASED ON HAZE DENSI...

引用

2024 International conference on image processing

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

ISBN: (纸本)9798350349405;9798350349399

remote sensing images (RSIs) are usually degraded by haze, losing the spectral fidelity and texture details. Most previous dehazing works adopted a unified dehazing strategy to process the entire image, ignoring the discrepancies of haze density, spectral information and texture complexity among different regions in one RSI, and thus resulted in the distorted spectral and blurred texture. In this paper, we propose a skip-connected network based on haze density estimation and saliency-driven dual channel fusion, realizing a differentiated defogging method. First, we propose a haze density estimation model, generating the haze density map. Second, we design a saliency-driven dual channel fusion to distinguish the spectral and texture features of different regions in the input, generating the saliency map. The two maps above jointly serve as guidance for the network to perform differentiated dehazing. Finally, an attention-based skip-connected block is introduced, which helps to fuse multi-scale information and obtain more refined dehazing results. A mixed loss function is also constructed to retain more texture details. The efficiency of the proposed method is validated by comparing its performance with other SOTA schemes.

关键词： remote sensing image dehazing haze density estimation saliency detection skip-connected network

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PHASE LEARNING BASED ON INTERACTIVE PERCEPTION FOR LIMITED-SAMPLE RESIDENTIAL AREA SEMANTIC SEGMENTATION 49

PHASE LEARNING BASED ON INTERACTIVE PERCEPTION FOR LIMITED-S...

引用

49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Lyu, Xinran Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Due to the rich details of residential areas and the characteristics of remote sensing image sharpness vulnerable to haze, it will not only consume a lot of labor costs but also be very difficult to produce a large-scale dataset with strong labels. Therefore, the limited-sample dataset has become a hotspot in recent years. To address this issue, we proposed a semantic segmentation method for residential areas by phase learning. The main task of the first stage is to generate a joint saliency map by reducing the interference of haze noise through the feature comparison similarity sorting algorithm and combine them to generate initial pixel-level pseudo labels for the next stage of training. In the second stage, we proposed to construct a group feature interactive perception module to achieve image group semantic co-segmentation. Comprehensive evaluations with 2 datasets and the comparison with 7 methods validate the superiority of the proposed model.

关键词： remote sensing phase learning weak annotation interactive perception contrast similarity ranking

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Efficient Vision Mamba with Dynamic Feature Fusion for remote sensing image Semantic Segmentation 2

Efficient Vision Mamba with Dynamic Feature Fusion for Remot...

引用

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

作者： Liu, Chenchen Cao, Yice Jiang, Dingkun Wu, Zhenhua Hu, He Huang, Zhixiang Anhui University School of Electronic and Information Engineering Hefei230601 China

ISBN: (纸本)9798331515669

The improvement in imaging resolution and the increase in imaging swath of remote sensing satellites have enabled the acquisition of vast and complex remote sensing image data. Effectively and comprehensively extracting target information to enhance segmentation accuracy and interpretation efficiency has become a research hotspot in the field of remote sensing semantic segmentation. Although current segmentation algorithms based on CNN and Transformer frameworks have made significant progress in performance, achieving a good balance between segmentation accuracy and computational complexity remains challenging, limiting their widespread application in practical tasks to a certain extent. To address this issue, this paper introduces a state-space model and proposes an efficient vision Mamba for remote sensing image semantic segmentation, abbreviated as EVMDNet. By designing an efficient vision state space block (EVSSBlock), the method effectively integrates local and non-local features while improving computational efficiency through efficient 2D scanning (ES2D) strategy. Furthermore, to avoid the insufficient utilization of features and weak discriminative ability caused by the direct addition of skip connections, a dynamic feature fusion block (DFFBlock) is designed to adaptively adjust the weights of different features for efficient feature fusion. Experimental results on classical remote sensing image datasets demonstrate that the proposed method not only improves segmentation accuracy but also reduces computational complexity to a certain extent, outperforming existing state-of-the-art segmentation algorithms. © 2024 IEEE.

关键词： Semantic Segmentation

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MAS-NET:MIXED-FEATURE ATTENTION SIAMESE NETWORK FOR CHANGE DETECTION ON remote sensing imageS 49

MAS-NET:MIXED-FEATURE ATTENTION SIAMESE NETWORK FOR CHANGE D...

引用

49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ding, Xingyu Wang, Weiqiang Univ Chinese Acad Sci CAS Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Change detection plays a crucial role in remote sensing tasks. However, current deep learning-based change detection methods suffer from issues such as misclassified pixels and unclear segmentation result on edges. To address these challenges, we propose a novel approach called Mixed-feature Attention Siamese Network (MAS-Net). MAS-Net adopts an encoder-decoder structure, where the encoder part effectively fuses image features from different time points, thereby preserving more cross-time information in the feature map. In the decoder part, we introduce the Feature Global Attention Module (FGAM) to leverage the global attention mechanism for extracting deep semantic information from the fused feature map. By incorporating these proposed modules and strategies, MAS-Net achieves fewer misclassified pixels and clearer edges in the resulting change detection maps. Experimental evaluations on the LEVIR-CD and CDD datasets demonstrate that MAS-Net outperforms state-of-the-art models by 0.15% (91.88% vs. 91.73%) and 1.3% (97.5% vs. 96.2%) in terms of F1-Score, respectively, thus establishing a solid baseline for change detection.

关键词： change dectection remote sensing attention mechanism

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signal-Centric remote sensing via Alternative Preprocessing and Acoustic processing for ML-Driven Applications

Signal-Centric Remote Sensing via Alternative Preprocessing ...

引用

2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Luna, Logan Jansen-Sánchez, Sirio Demirkiran, Ilteris Ghelarducci, Leo Embry-Riddle Aeronautical University Daytona Beach United States

ISBN: (纸本)9798331504847

The dominant method of processing sonar data is using image-based representations, requiring the preprocessing of image data on autonomous systems. We propose an alternative data processing method for remote sensing applications via the use of data in Comma-Seperated Value format. Experimentation on our alternative approach shows a reduction of processing time by 91.18%, an improvement in accurate object detection by Machine Learning, and an increase in SNR (signal-to-noise ratio), PSNR (Peak signal-to-noise ratio), and other evaluation metrics. © 2025 IEEE.

关键词： Data curation

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MULTIMODAL TRANSFORMER USING CROSS-CHANNEL ATTENTION FOR OBJECT DETECTION IN remote sensing imageS 31

MULTIMODAL TRANSFORMER USING CROSS-CHANNEL ATTENTION FOR OBJ...

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2024 International conference on image processing

作者： Bahaduri, Bissmella Ming, Zuheng Feng, Fangchen Mokraoui, Anissa Univ Sorbonne Paris Nord Lab L2TI Villetaneuse France

ISBN: (纸本)9798350349405;9798350349399

Object detection in remote sensing images (RSI) is a critical task for numerous applications in Earth Observation (EO). Differing from object detection in natural images, object detection in remote sensing images faces challenges of scarcity of annotated data and the presence of small objects represented by only a few pixels. Multi-modal fusion has been determined to enhance the accuracy by fusing data from multiple modalities such as RGB, infrared (IR), lidar, and synthetic aperture radar (SAR). To this end, the fusion of representations at the mid or late stage, produced by parallel subnetworks, is dominant, with the disadvantages of increasing computational complexity in the order of the number of modalities and the creation of additional engineering obstacles. Using the cross-attention mechanism, we propose a novel multi-modal fusion strategy for mapping relationships between different channels at the early stage, enabling the construction of a coherent input by aligning the different modalities. By addressing fusion in the early stage, as opposed to mid or late-stage methods, our method achieves competitive and even superior performance compared to existing techniques. Additionally, we enhance the SWIN transformer by integrating convolution layers into the feed-forward of non-shifting blocks. This augmentation strengthens the model's capacity to merge separated windows through local attention, thereby improving small object detection. Extensive experiments prove the effectiveness of the proposed multimodal fusion module and the architecture, demonstrating their applicability to object detection in multimodal aerial imagery. Our code is available at here.

关键词： Multimodal fusion cross-channel attention convolutional shifting window object detection remote sensing imagery

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