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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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Domain Generalized Object Detection for remote sensing images 31

Domain Generalized Object Detection for Remote Sensing Image...

引用

31st IEEE conference on signal processing and Communications Applications (SIU)

作者： Durakli, Efkan Aptoula, Erchan Gebze Tech Univ Dept Comp Engn Kocaeli Turkiye Sabanci Univ Fac Engn & Nat Sci Istanbul Turkiye

ISBN: (纸本)9798350343557

Building roof type detection from remotely sensed images is a crucial task for many remote sensing applications, including urban planning and disaster management. In recent years, deep learning-based object detection approaches have demonstrated outstanding performance in this field. However, most of these approaches assume that the training and testing data are sampled from the same distribution. When there are differences between the distributions of training and test data, known as domain shift, the performance significantly degrades. In this paper, we proposed a domain generalization method to address domain shift at the instance and image level for roof type detection from remote sensing images. Furthermore, we evaluated our proposed method with IEEE Data Fusion Contest 2023 dataset. The proposed approach is the first of its kind in terms of domain generalization for remote sensing object detection.

关键词： domain generalization object detection remote sensing

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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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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...

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

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

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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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