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7th International Conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Zhang, Xiaodi Xu, Yan Liu, Guorong Hu, Ping Chen, Zhaoyuan Li, Fang College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350350890

remote sensing information processing plays an important role in many fields such as environmental protection, urban planning, military reconnaissance and so on. However, there are problems in remote sensing images such as complex background, large size change, small target, etc. A remote sensing image target detection algorithm to improve YOLOv5 is proposed. Using a certain percentage of data enhancement technology at the input side to enrich the data features and improve the model generalization ability and robustness;for the problem of small targets in remote sensing images, SPD-Conv module is added to reduce the loss of fine-grained information and improve the detection performance of small targets;the design uses the DCNv2 module to replace part of the neck structure of the C3 layer, which breaks through the limitation of traditional convolution and improves the feature extraction ability of the network in remote sensing images. Experimental results show that the improved YOLOv5 model improves the detection accuracy by 3.9%. © 2024 IEEE.

关键词： Target tracking

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One-shot adaptation for cross-domain semantic segmentation in remote sensing images

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pattern recognition 2025年 162卷

作者： Tan, Jiaojiao Zhang, Haiwei Yao, Ning Yu, Qiang Chinese Acad Sci Res Ctr Soil & Water Conservat & Ecol Environm Yangling Shaanxi Peoples R China Minist Educ Yangling Shaanxi Peoples R China Chinese Acad Sci & Minist Water Resources Inst Soil & Water Conservat Yangling 712100 Shaanxi Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Weichai Power Co Ltd Weifang 261000 Shandong Peoples R China Northwest A&F Univ Coll Water Resources & Architectural Engn Key Lab Agr Soil & Water Engn Arid & Semiarid Area Minist Educ Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ State Key Lab Soil Eros & Dryland Farming Loess Pl Yangling 712100 Shaanxi Peoples R China

Contemporary cross-domain remote sensing (RS) image segmentation has been successful in recent years. When the target domain data becomes scarce in some realistic scenarios, the performance of traditional domain adaptation (DA) methods significantly drops. In this paper, we tackle the problem of fast cross-domain adaptation by observing only one unlabeled target data. To deal with dynamic domain shift efficiently, this paper introduces a novel framework named Minimax One-shot AdapTation (MOAT) to perform cross-domain feature alignment in semantic segmentation. Specifically, MOAT alternately maximizes the cross-entropy to select the most informative source samples and minimizes the cross-entropy of obtained samples to make the model fit the target data. The selected source samples can effectively describe the target data distribution using the proposed uncertainty-based distribution estimation technique. We propose a memory-based feature enhancement strategy to learn domain-invariant decision boundaries to accomplish semantic alignment. Generally, we empirically demonstrate the effectiveness of the proposed MOAT. It achieves anew state-of-theart performance on cross-domain RS image segmentation for conventional unsupervised domain adaptation and one-shot domain adaptation scenarios.

关键词： remote sensing segmentation One-shot domain adaptation Minimax strategy

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Semantic segmentation of remote sensing image based on bilateral branch network

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VISUAL COMPUTER 2024年第5期40卷 3069-3090页

作者： Li, Zhongyu Wang, Huajun Liu, Yang Chengdu Univ Technol Coll Geophys Chengdu 610059 Peoples R China Chengdu Normal Univ Coll Comp Sci Chengdu 611130 Peoples R China Chengdu Normal Univ Inst Higher Educ Sichuan Prov Key Lab Interior Layout Optimizat & Secur Chengdu 611130 Peoples R China

Due to the large intra-class differences between the same categories and the scale imbalance between different categories in the remote sensing image dataset, the semantic segmentation task presents the problem of small-scale object information loss, the imbalance between foreground and background, and simultaneously the background dominates, which seriously affects the performance of the network model. To solve the above problems, this paper proposes an efficient bilateral branch depth neural network model based on the U-Net depth neural network, named BBU-Net. Firstly, one branch of the network learns the distribution characteristics of the original data, and the other focuses on difficult samples. Then the two branches improve the representation and classification ability of the neural network by accumulating learning strategies. Finally, considering the geometric diversity of remote sensing images, this paper adopts test time augmentation and reflection padding strategies and proposes a balanced weighted loss function named CombineLoss to alleviate the imbalance in the training process. The depth neural network proposed in this paper was first tested on the Inria Aerial image Labeling Dataset, and 87.53% of mean intersection over union and 97.4% of mean pixel accuracy were obtained, respectively. At the same time, to verify the model's complexity, the model proposed in this paper is compared with the neural network based on integrated learning. The comparison results show that the spatial complexity of the network proposed in this paper is much lower than the neural network obtained by integrated learning, and the parameters are also much smaller than the neural network based on integrated learning. Then use the satellite building dataset I in the WHU Building Dataset and mainstream semantic segmentation methods for multiple groups of comparative experiments. The experimental results show that the method proposed in this paper can effectively extract the semanti

关键词： Feature extraction Semantic segmentation Deep neural network Imbalance Integrated learning

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Detection and Classification of Satellite remote sensing images Using Hybrid Segmentation and Feature Extraction with Effective Algorithms 2

Detection and Classification of Satellite Remote Sensing Ima...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Vinuja, G. Devi, N. Bharatha Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Cse Chennai India

ISBN: (纸本)9798350382952

The remote sensing image analysis, classification, and pattern recognition processes all depend on image segmentation. In this research, a search-based convolutional neural network (SBCNN) is used to identification method for remote sensing images. Prior to applying the image data to the SKFCM with PeSOA segmentation step, the image data must first undergo pre-processing. When pre-processing satellite images for road networks, noise is removed using an improved median filtering technique. The image is then segmented using the SKFCM with PeSOA Segmentation technique to have inverse shape determination with lowest energy usage. Using an intensity constraint, it is possible to identify the segments of a building and vegetation, a road, and a barren area of land. Following segmentation, MLBP with DWT feature extraction is performed on the road satellite images, and the SBCNN is then used to categorize the images. After associated with obtainable methods, the findings of the suggested technique display excellent precision of 98.6%. © 2024 IEEE.

关键词： Feature extraction

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Semisupervised Few-Shot remote sensing image Classification Based on KNN Distance Entropy

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2022年 15卷 8798-8805页

作者： Chao, Xuewei Li, Yang Shihezi Univ Coll Mech & Elect Engn Shihezi 832003 Peoples R China

In recent years, remote sensing image processing based on deep learning has been widely applied in many scenes, but the involved deep learning technology requires large-scale labeled data, which has been a practical problem in the remote sensing field. In this study, we proposed a novel data information quality assessment method, called K-nearest neighbor (KNN) distance entropy, to screen the remote sensing images. The evaluation metric was used to assess unlabeled data and assign the pseudolabel, which further constitutes the proposed semisupervised few-shot classification method in this article. The metatask setting was adopted to verify the validity and stability of experimental results. Specifically, the KNN distance entropy metric can be used to distinguish the samples in the core set or boundary set. Experimental results show that the core set samples are more suitable under the few-shot condition, for instance, the metatask average accuracy trained by the core set samples outperforms that by boundary samples by about 18% in the case of 45-ways and 5-shot. The proposed semisupervised few-shot method based on KNN distance entropy achieves significant improvement under different experimental conditions. The visualization of the feature distribution of screened data is shown to provide an intuitive interpretation. This article lays a meaningful foundation for screening and evaluating remote sensing images under few-shot conditions, and inspires the data-efficient few-shot learning based on high-quality data in the remote sensing field.

关键词： Entropy remote sensing Task analysis Measurement image quality Deep learning Sensors Entropy feature extraction image processing pattern recognition

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Uncertainty based regional-decision fusion of multispectral and nDSM for ultra-high resolution UAV imagery

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JOURNAL OF SPATIAL SCIENCE 2024年第3期69卷 745-771页

作者： Soni, Ashish Koner, Radhakanta Villuri, Vasant Govind Kumar Indian Inst Technol ISM Dept Min Engn Remote Sensing & GIS Lab Dhanbad India Indian Inst Technol ISM Dept Min Engn Dhanbad India

The recent advancement in the pattern recognition technique has demonstrated the superiority of remote sensing technology. Deep neural networks use spatial feature representations such as convolution neural networks (CNN) to provide better generalisation capability. Our aim is two-fold: firstly, increase the reliability feature by performing the Dual-scale fusion via a modified Markov random field known as DuCNN-MMRF. Secondly, an integration framework was introduced to combine the multispectral image classification produced by DuCNN-MMRF and Normalised-Digital Surface Model (nDSM) information, using a novel approach known as constraint-based Dempster Shafer theory (C-DST). C-DST targeted DuCNN-MMRF's uncertain information (ambiguous information) and rectified it with complementary information.

关键词： Markovian network Dempster-Shafer theory Convolution Neural Network image Fusion Building foot-print

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End-to-End Unsupervised Style and Resolution Transfer Adaptation Segmentation Model for remote sensing images 6th

End-to-End Unsupervised Style and Resolution Transfer Adapta...

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6th Chinese Conference on pattern recognition and Computer Vision (PRCV)

作者： Li, Zhengwei Wang, Xili Shaanxi Normal Univ Sch Comp Sci Xian Peoples R China

ISBN: (纸本)9789819984619;9789819984626

For remote sensing image unsupervised domain adaptation, there are differences in resolution except for feature differences between source and target domains. An end-to-end unsupervised domain adaptation segmentation model for remote sensing images is proposed to reduce the image style and resolution differences between the source and target domains. First, a generative adversarial-based style transfer network with residual connection, scale consistency module, and perceptual loss with class balance weights is proposed. It reduces the image style and resolution differences between the two domains and maintains the original structural information while transferring. Second, the visual attention network (VAN) that considers both spatial and channel attention is used as the feature extraction backbone network to improve the feature extraction capability. Finally, the style transfer and segmentation tasks are unified in an end-to-end network. Experimental results show that the proposed model effectively alleviates the performance degradation caused by different features and resolutions. The segmentation performance is significantly improved compared to advanced domain adaptation segmentation methods.

关键词： image segmentation remote sensing image Unsupervised domain adaptation Feature and resolution differences

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MRU-Net: A remote sensing image segmentation network for enhanced edge contour Detection

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2023年第12期17卷 3364-3382页

作者： Han, Jing Wang, Weiyu Lin, Yuqi Lyu, Xueqiang Beijing Informat Sci & Technol Univ Beijing Key Lab Internet Culture Digital Dissemina Beijing 100101 Peoples R China

remote sensing image segmentation plays an important role in realizing intelligent city construction. The current mainstream segmentation networks effectively improve the segmentation effect of remote sensing images by deeply mining the rich texture and semantic features of images. But there are still some problems such as rough results of small target region segmentation and poor edge contour segmentation. To overcome these three challenges, we propose an improved semantic segmentation model, referred to as MRU-Net, which adopts the U-Net architecture as its backbone. Firstly, the convolutional layer is replaced by BasicBlock structure in U-Net network to extract features, then the activation function is replaced to reduce the computational load of model in the network. Secondly, a hybrid multi scale recognition module is added in the encoder to improve the accuracy of image segmentation of small targets and edge parts. Finally, test on Massachusetts Buildings Dataset and WHU Dataset the experimental results show that compared with the original network the ACC,mIoU and F1 value are improved, and the imposed network shows good robustness and portability in different datasets.

关键词： Convolutional neural network image processing Hybrid multiscale identification module Micro residual structure remote sensing image segmentation

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Research on the Impact of change features for remote sensing image Change Detection 12

Research on the Impact of change features for Remote Sensing...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Mu, HaoDong Li, Haipeng Chen, Zhong Huazhong University of Science and Technology 1037 Luoyu Road HuBei WuHan430074 China Beijing Aerospace Automatic Control Institute Bejing100854 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

As remote sensing technology continues to advance, the accuracy and quantity of remote sensing images have significantly improved. The generation of a vast amount of available data has facilitated the widespread application of various deep learning methods in the field of remote sensing data processing, such as object detection, semantic segmentation, and change detection. In the aforementioned tasks, Change detection is used to identify alterations occurring on the Earth's surface by utilizing remote sensing (RS) data. In recent years, deep learning based methods have exhibited significantly superior performance compared to traditional change detection techniques. The fundamental strategy enabling these advancements involves extracting appropriate deep learning features from input remote sensing images through various backbone networks such as VGG, ResNet, DenseNet etc. Nevertheless, the features extracted by the aforementioned backbone networks may not fully cater to the specific requirements of remote sensing image change detection tasks. Consequently, our goal is to explore the influence of features extracted by different backbone networks on change detection tasks and introduce a specialized backbone network tailored for change detection. This endeavor aims to produce features that are better suited for the of change detection. The experimental results indicate that our specifically designed feature extraction network for remote sensing image change detection outperforms traditional networks in extracting task-specific features. These features are better suited for subsequent decoder modules, enhancing the generation of image-based change detection results. At the same time, we found that when using general backbone networks for change detection, ResNet achieves the highest metric accuracy, while DenseNet has the lowest memory usage and the fastest training and testing speed. Depending on the specific task, we can choose the appropriate backbone network as need

关键词： Feature extraction

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image Inpainting by Machine Learning Algorithms

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pattern recognition AND image ANALYSIS 2024年第2期34卷 237-243页

作者： Wan, Wei Leonov, Ivan Nanjing Res Inst Elect Engn Nanjing 210007 Jiangsu Peoples R China Belarusian State Univ Fac Appl Math & Comp Sci Minsk BELARUS

image inpainting is the process of filling in missing or damaged areas of images. In recent years, this area has received significant development, mainly owing to machine learning methods. Generative adversarial networks are a powerful tool for creating synthetic images. They are trained to create images similar to the original dataset. The use of such neural networks is not limited to creating realistic images. In areas where privacy is important, such as healthcare or finance, they help generate synthetic data that preserves the overall structure and statistical characteristics, but does not contain the sensitive information of individuals. However, direct use of this architecture will result in the generation of a completely new image. In the case where it is possible to indicate the location of confidential information on an image, it is advisable to use image inpainting in order to replace only the secret information with synthetic information. This paper discusses key approaches to solving this problem, as well as corresponding neural network architectures. Questions are also raised about the use of these algorithms to protect confidential image information, as well as the possibility of using these models when developing new applications.

关键词： image inpainting Wasserstein generative adversarial network generative adversarial network image imputation context encoders

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