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

共 3583 条记录，以下是211-220 订阅

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DYNAMIC MUTUAL ENHANCEMENT NETWORK FOR SINGLE remote sensing image DEHAZING 29

DYNAMIC MUTUAL ENHANCEMENT NETWORK FOR SINGLE REMOTE SENSING...

引用

IEEE International conference on image processing (ICIP)

作者： Wang, Shan Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

In this paper, we propose a dynamic mutual enhancement network (DMENet) for haze removal in remote sensing images. It has three major advantages compared with other dehazing algorithms: 1) The proposed DMENet is based on the U-Net architecture to extract features effectively, which is composed of three components, i.e., a multi-scale encoder, a middle transmission layer (MTL), and a dynamic mutual decoder. 2) The dynamic mutual enhancement (DME) module is designed to dynamically integrate multi-level feature maps in a mutual way, which contains the low-level detail information and high-level semantic information respectively. 3) To improve the robustness and generalization performance of the DMENet, the hybrid supervision is built for network training between the restored results and their ground-truth labels, which consists of the pixel-level supervision, patch-level supervision and image-level supervision. Experimental results on both synthetic datasets and real remote sensing hazy images demonstrate that the proposed DMENet can gain significant progresses over the competing methods.

关键词： image enhancement remote sensing image dehazing dynamic mutual enhancement CNN

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Fusion of Multiple Models with Multi-Modal Datasets: Land Cover Mapping in the Yangtze River Economic Belt 14

Fusion of Multiple Models with Multi-Modal Datasets: Land Co...

引用

14th Workshop on Hyperspectral Imaging and signal processing: Evolution in remote sensing, WHISPERS 2024

作者： Li, Jiepan Huang, He Xia, Yu Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China Pengcheng Laboratory Shenzhen518066 China

ISBN: (纸本)9798331513139

Land cover is essential for understanding Earth's environment, playing a vital role in disaster monitoring and infrastructure management. The Yangtze River Economic Belt, a pivotal region for economic growth in China, requires comprehensive monitoring and analysis of land cover changes to support effective policy-making and environmental protection. The 2024 IEEE Whisper contest, MMSeg-YREB, aims to integrate multi-modal data, specifically Sentinel-1 and Sentinel-2, to conduct a detailed analysis of land cover types and their changes within the Yangtze River Economic Belt. To overcome this challenge, the MMSeg-YREB dataset was integrated with Google Earth Engine (GEE) tools and publicly available global land cover products for data preparation. With this enhanced dataset, multiple models were developed, including convolutional neural networks (CNN) and Transformer networks, which enables the learning of features from both local and global perspectives, significantly enhancing the accuracy of segmentation. To fuse these results, a variety of post-processing techniques were proposed to refine the outcomes of the different models, resulting in a highly accurate final land cover maps. This innovative approach allowed our team to achieve a commendable first-place in the competition, underscoring our contributions and effectiveness in advancing methodologies for land cover mapping. © 2024 IEEE.

关键词： Convolutional neural networks

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Domain-Adaptive Deep Transfer Learning for Cross-Modal Heterogeneous image Matching under Label-Scarce Constraints 2

Domain-Adaptive Deep Transfer Learning for Cross-Modal Heter...

引用

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

作者： Gong, Guoqing Yang, PeiZhen Yang, Chao Han, Zhiqiang Ye, Yuanxin Southwest Jiaotong University Faculty of Geosciences and Engineering Sichuan Chengdu611576 China

ISBN: (纸本)9798331515669

Deep learning has made significant strides in cross-modal heterogeneous remote sensing image matching, yet it remains heavily dependent on annotated target scene data for training models. Under label-scarce constraints, domain discrepancies between actual test data and training data often result in a decline in the performance of trained models. To tackle this challenge, a domain adaptation method is put forth for consideration in the paper for cross-modal heterogeneous image matching. The method achieves the transformation between the images of the existing scene and the target scene through style transfer, and narrows the domain offset between the images of the target scene generated by the transformation of images of the existing scene and the original target scene through adversarial learning. It aligns the target domain with unlabeled information and the source domain with labeled information from both the image and feature perspectives, thereby solving the matching tasks inside the target domain through the application of knowledge from the source domain. Experiments were conducted using two different optical and synthetic aperture radar (SAR) cross-modal datasets, alternating as both target and source domains in training and testing. Results confirmed that compared to current traditional matching methods and deep learning matching methods, our method demonstrates feasibility and effectiveness in cross-dataset transfer, effectively reducing the dependency on annotated data for matching tasks in the target scene. © 2024 IEEE.

关键词： Contrastive Learning

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Multi-scale Frequency Attention Fusion Network for Optical and SAR image Registration 17

Multi-scale Frequency Attention Fusion Network for Optical a...

引用

17th IEEE International conference on signal processing, ICSP 2024

作者： Hu, Xin Wu, Yan Li, Zhikang Yang, Zhifei Li, Ming Xidian University School of Electronic Engineering Xi'an China Xidian University National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9798350387384

image registration is a prerequisite for remote sensing image fusion and classification, and registration accuracy affects the performance of these tasks. However, there are significant nonlinear radiation differences between optical and synthetic aperture radar (SAR) images and speckle noise in SAR, which limits the ability of pure convolutional neural networks (CNNs) to obtain modality-shared features. To solve these problems, this paper proposes a Multi-scale Frequency Attention Fusion Network for Optical and SAR image Registration (MFAF-OS-RegNet), which includes a Multi-scale Frequency Channel Attention Fusion (MFCAF) module and a Momentum Contrastive Learning (MCL) strategy. Firstly, we extract repeatable keypoints distributed on the edges and develop a novel MFCAF module to obtain modality-shared features. In the MFCAF module, multi-scale feature fusion is used to improve the positioning accuracy of matching points, and frequency channel attention is used to mine more favorable features. Secondly, we design a MCL strategy to reduce the interference of speckle noise in network training. The momentum teacher corrects handcrafted labels by learning the essential characteristics of the data for robust matching. Finally, we filter outliers and estimate transformation parameters based on the initial matching correspondence output by MFAF-OS-RegNet. Experimental results on a publicly available OS-dataset show that the proposed method outperforms other state-of-the-art methods. © 2024 IEEE.

关键词： image fusion

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MPFFNet:Semantic Segmentation of remote sensing image Buildings Based on Multipath Feature Fusion 8

MPFFNet:Semantic Segmentation of Remote Sensing Image Buildi...

引用

8th International conference on Communication, image and signal processing, CCISP 2023

作者： Dong, Jie Mao, Jingkun Liu, Kun Cheng, Liangyong School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China Interplanetary Space Technology Development Co. LTD Tianjin China

ISBN: (纸本)9798350305838

The buildings with variable scales and complex background information in remote sensing images often result in missed and misclassified segmentation of buildings. To address this issue, this paper designs an multipath feature fusion network (MPFFNet). MPFFNet has an overall encoding and decoding structure, embedded with an residual depth Inception block(RDI) module to extract multi-scale features. At the same time, MPFFNet uses the multipath residual channel attention (MPRCA) module to expand the Receptive field of the feature map to obtain rich multi-scale context information. The validity of the proposed algorithm is proved on the WHU Buildings data set, with F1 reaching 94.70%, IOU reaching 89.93%, and Recall reaching 94.98%. © 2023 IEEE.

关键词： Buildings

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EM2-YOLO: Lightweight remote sensing image Detection 5th

EM2-YOLO: Lightweight Remote Sensing Image Detection

引用

5th International conference on 3D Imaging Technologies—Multidimensional signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Liu, Xinyi Song, Yaolian Wang, Can School of Information Engineering and Automation Kunming University of Science and Technology Yunnan650500 China

ISBN: (纸本)9789819751808

remote sensing photographs have a wealth of information, and object detection methods are crucial in this. On mobile and embedded platforms, deep learning-based target identification algorithms are challenging to implement. Additionally, the issues of delayed target detection and poor accuracy of small target detection for remote sensing photos must be resolved immediately. To address these issues, we provide EM2-YOLOv4, a simple method for recognizing remote sensing images. MobileNetv2 acts as the backbone network to reduce model complexity, while depth-separable convolution enhances detection speed. Additionally, we introduce a feature enhancement module and an Efficient Channel Attention (ECA) module to improve model accuracy. To address the issue of unequal assignment of positive and negative samples, we incorporate Focal Loss into the loss function. Experimental demonstrate that the model can perform detection at 45 frames per second with an average accuracy of 94.11%. The model has been significantly reduced, as seen by the fact that it is only 48 MB in size, but it still maintains good detection accuracy and accelerates detection. The model is ideal for usage on embedded devices because of this feature. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optical remote sensing

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Semi-supervised Medical image Classification Based on DenseNet and Capsule Network Combination Model

Semi-supervised Medical Image Classification Based on DenseN...

引用

2024 International conference on remote sensing, Mapping, and image processing, RSMIP 2024

作者： Shen, Hao Mo, Jiaqing Ren, Jinxun Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Xinjiang University Urumqi830017 China

ISBN: (纸本)9781510680012

Medical image classification plays a crucial role in clinical treatment and medical education. However, traditional classification methods have reached performance limits and require substantial time and effort for feature extraction and selection. In this study, we introduce a novel deep learning model that combines the feature extraction part of DenseNet with the dynamic routing mechanism of capsule networks. DenseNet, known for its dense connections, aids in gradient propagation and feature reuse. Capsule networks introduce capsule layers with sensitivity to spatial relationships. By merging these two structures, our aim is to enhance the model's ability to model object hierarchy and pose variations. Our approach initially incorporates the dynamic routing mechanism of capsule networks on top of DenseNet, achieving an effective fusion of the two structures. Experimental results on the Chest X-Ray14 dataset demonstrate a significant improvement in accuracy and robustness compared to traditional DenseNet and capsule networks. To address the issue of limited annotation in medical image data, we introduce a semi-supervised learning approach and improve training balance through anti-curriculum pseudo-labeling. Furthermore, to obtain more accurate pseudo-labels, we adopt a mixed model for pseudo-label generation. This method not only effectively balances the pseudo-label predictions generated by two models but also mitigates the challenges associated with threshold-based confidence intervals in the context of multi-class, multi-label scenarios. In summary, the combination of deep convolutional neural networks and capsule networks, along with semi-supervised learning and anti-curriculum pseudo-labeling, holds great promise for medical image classification. It has significant application value in clinical practice and presents a wide-ranging prospect for the future. © 2024 SPIE.

关键词： Feature extraction

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Semi-Supervised remote sensing image Change Detection Using Mean Teacher Model for Constructing Pseudo-Labels 48

Semi-Supervised Remote Sensing Image Change Detection Using ...

引用

48th IEEE International conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Mao, Zan Tong, Xinyu Luo, Ze Chinese Academy of Sciences Computer Network Information Center Beijing China University of the Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728163277

In recent years, deep learning has ushered in great developments in remote sensing image change detection. Practically, it is labor-intensive and time-consuming to label images for co-registration. In this paper, we propose a semi-supervised training that uses the mean teacher model to construct pseudo-labels to increase the generalizability of the model trained with a handful of data supervision. More specifically, we first supervise the training of a student change detection model with a few labeled data, while migrating the model parameters from each training round to a teacher model with the same structure through Exponential Moving Average (EMA). The weakly augmented output produced by the teacher model was preferable to the strongly augmented prediction produced by the student to penalize the latter. We explore the paradigm of Strong-to-Weak Consistency in change detection. Experiments on the LEVIR-CD and WHU-CD have been extensively conducted and state-of-the-art performance has been achieved. © 2023 IEEE.

关键词： Change detection

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Point process and CNN for small object detection in satellite images 28

Point process and CNN for small object detection in satellit...

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conference on image and signal processing for remote sensing XXVIII

作者： Mabon, Jules Ortner, Mathias Zerubia, Josiane Univ Cote dAzur Inria Sophia Antipolis France Airbus Def & Space Toulouse France

ISBN: (纸本)9781510655386;9781510655379

In this article we present a combination of marked point processes with convolutional neural networks applied to remote sensing. While point processes allow modeling interactions between objects via priors, classical methods rely on contrast measures that become unreliable as objects of interest and context become more diverse. We propose learning likelihood measures using convolutional neural networks to make these measures more versatile and resilient. We apply our method to the detection of vehicles in satellite images.

关键词： Small object detection remote sensing Marked point process Convolutional neural network

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Research on remote sensing image Classification Algorithm Based on EfficientNet 7

Research on Remote Sensing Image Classification Algorithm Ba...

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7th International conference on Intelligent Computing and signal processing, ICSP 2022

作者： Yin, Hang Yang, Chengyi Lu, Jiayi Xi'an University of Science and Technology School of Computer Science and Technology Xi'an China Harbin University of Science and Technology School of Computer Science and Technology Harbin China Beijing University of Chemical Technology School of Mechanical and Electrical Engineering Beijing China

ISBN: (纸本)9781665478571

Accurate classification of remote sensing images is important in remote sensing applications. In order to verify the efficiency and accuracy of efficientnet algorithm in remote sensing image classification, this paper classifies the UCMerced LandUse dataset based on EfficientNet. The experimental results show that compared with VGGNet, ResNet and MobileNet, the EfficientNet network introduces composite parameters and scales depth, width and resolution at the same time. The accuracy of EfficientV2-s in the verification set is 16.5%, 5.2%, 1.8% and 1.7% higher than that of VGG, MobileNetV2, ResNet34 and EfficientNet-b0, which shows the efficiency and accuracy of EfficientNet network in remote sensing image classification data set. © 2022 IEEE.

关键词： Efficiency

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