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3rd International Conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Akodad, Sara Bombrun, Lionel Germain, Christian Berthoumieu, Yannick Université de Bordeaux CNRS IMS UMR 5218 Groupe Signal et Image Talence33405 France

ISBN: (纸本)9783031090363

The new generation of remote sensing imaging sensors enables high spatial, spectral and temporal resolution images with high revisit frequencies. These sensors allow the acquisition of multi-spectral and multi-temporal images. The availability of these data has raised the interest of the remote sensing community to develop novel machine learning strategies for supervised classification. This paper aims at introducing a novel supervised classification algorithm based on covariance pooling of multi-layer convolutional neural network (CNN) features. The basic idea consists in an ensemble learning approach based on covariance matrices estimation from CNN features. Then, after being projected on the log-Euclidean space, an SVM classifier is used to make a decision. In order to give more strength to relatively small objects of interest in the scene, we propose to incorporate the visual saliency map in the process. For that, inspired by the theory of robust statistics, a weighted covariance matrix estimator is considered. Larger weights are given to more salient regions. Finally, some experiments on remote sensing classification are conducted on the UC Merced land use dataset. The obtained results confirm the potential of the proposed approach in terms of classification scene accuracy. It demonstrates, besides the interest of exploiting second order statistics and adopting an ensemble learning approach, the benefit of incorporating visual saliency maps. © 2022, Springer Nature Switzerland AG.

关键词： remote sensing

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Implications of neuroimaging findings in addiction

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Psychoradiology 2023年第1期3卷 18-23页

作者： Xinwen Wen Lirong Yue Zhe Du Linling Li Yuanqiang Zhu Dahua Yu Kai Yuan School of Life Science and Technology Xidian UniversityXi’an 710126China School of Biomedical Engineering Medical SchoolShenzhen UniversityShenzhen 518060China Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen UniversityShenzhen 518060China Department of Radiology Xijing HospitalFourth Military Medical UniversityXi’an 710032China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information EngineeringInner Mongolia University of Science and TechnologyBaotou 014010China Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Xidian UniversityXi’an 710126China Xi’an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information School of Life Science and TechnologyXidian UniversityXi’an 710126China

Addiction is a chronic and often relapsing brain disorder characterized by drug abuse and withdrawal symptoms and compulsive drug seeking(Koob and Volkow,2010)when access to the drug is *** leads to structural and fun... 详细信息

关键词： motivation finding seeking

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Evaluation of Internal and External Starch Changes in Apples Using Hyper-Spectral Analyses in the NIR-SWIR

Evaluation of Internal and External Starch Changes in Apples...

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Workshop on Hyperspectral image and Signal processing: Evolution in remote sensing, WHISPERS

作者： A. K. Ruvalcaba Perez G. Siess N. Janunts V. Boehm A. Tuennermann Abbe Center of Photonics Institute of Applied Physics Friedrich Schiller University Jena German ams OSRAM Group Jena Germany Institute of Nutritional Sciences Friedrich Schiller University Jena Germany Fraunhofer Institute for Applied Optics and Precision Engineering IOF Jena Germany

ISBN: (数字)9798331513139

ISBN: (纸本)9798331513146

Starch pattern Index (SPI) is one of the most common parameters used to determine the degree of ripeness of fruits. To predict the degree of ripeness, SPI is combined with chemometric analysis. Nevertheless, the spectral correlation between external and internal starch changes has not been evaluated. This study evaluates the internal and external changes of starch in Gala apples during the 2024 season by covering the near-infrared (NIR) and short-wave infrared (SWIR) spectral ranges from 700nm to 2300nm. The evaluation consisted of a multispectral NIR-SWIR camera analysis and spectral measurements along the surface. Results revealed a pattern on the apple cortex similar to SPI without the use of iodine implementation of a false negative RGB image. Furthermore, the different SPIs were also distinguished by the spectral measurements along the surface, allowing a correlation in both instances with the same phenomenon.

关键词： Correlation Surface waves Conferences Signal processing Cameras Indexes Surface treatment remote sensing Iodine Hyperspectral imaging

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Enhancing Robotic Systems for Revolutionizing Healthcare Using Markov Decision Processes 1

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6th International Conference on Recent Trends in image processing and pattern recognition, RTIP2R 2023

作者： Dahamou, Ikram Daoui, Cherki Laboratory of Information Processing and Decision Support Faculty of Sciences and Techniques Sultan Moulay Slimane University Béni-Mellal Morocco

ISBN: (数字)9783031530821

ISBN: (纸本)9783031530814

This work is part of a research project carried out during the COVID-19 pandemic, involving the design and realization of an autonomous mobile hospital robot. Many real-world robotic tasks suffer from the critical characteristics: Noisy sensing, imperfect control, and environment changes. The Markov decision process MDP and its variants provide a mathematically based framework for modeling and solving robot decision and control tasks under uncertainty. This paper presents a review of Markov Decision Processes (MDPs) and their variants in the Control of Robotic systems. We begin by introducing the basic concepts of MDPs and their algorithms for solving completely observable decision problems, including value iteration and policy iteration. We then discuss the challenges associated with partially observable decision problems POMDPs. We review various approaches for solving POMDPs, including belief-state planning and Monte Carlo tree search. Finally, we discuss the concept of POMDP augmentation, which involves incorporating additional information into the decision-making process to improve performance. We present several examples of POMDP augmentation techniques, including the use of deep neural networks and transfer learning. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Markov processes

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Reconciling Semantic Controllability and Diversity for remote sensing image Synthesis with Hybrid Semantic Embedding

arXiv

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arXiv 2024年

作者： Liu, Junde Zhao, Danpei Yuan, Bo Li, Wentao Li, Tian The Image Processing Center School of Astronautics Beihang University Beijing102206 China Tianmushan Laboratory Hangzhou311115 China

Significant advancements have been made in semantic image synthesis in remote sensing. However, existing methods still face formidable challenges in balancing semantic controllability and diversity. In this paper, we present a Hybrid Semantic Embedding Guided Generative Adversarial Network (HySEGGAN) for controllable and efficient remote sensing image synthesis. Specifically, HySEGGAN leverages hierarchical information from a single source. Motivated by feature description, we propose a hybrid semantic Embedding method, that coordinates fine-grained local semantic layouts to characterize the geometric structure of remote sensing objects without extra information. Besides, a Semantic Refinement Network (SRN) is introduced, incorporating a novel loss function to ensure fine-grained semantic feedback. The proposed approach mitigates semantic confusion and prevents geometric pattern collapse. Experimental results indicate that the method strikes an excellent balance between semantic controllability and diversity. Furthermore, HySEGGAN significantly improves the quality of synthesized images and achieves state-of-the-art performance as a data augmentation technique across multiple datasets for downstream tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Mamba-Based Residual Network for remote sensing image Super-Resolution

Mamba-Based Residual Network for Remote Sensing Image Super-...

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pattern recognition and Artificial Intelligence (PRAI), International Conference on

作者： Gongbo Zhao Feng Wang Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education) School of Information Science and Technology Fudan University Shanghai China Yiwu Research Institute Fudan University Yiwu China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

image super-resolution is the process of recovering high-resolution images from low-resolution inputs, which is crucial in low-level computer vision tasks. In recent years, progress in deep learning has significantly propelled the development of image super-resolution technology. Various models, such as convolutional neural networks (CNNs), generative adversarial networks, Transformers, and diffusion models, have been employed to address this problem with significant impact. Among these models, CNNs are effective in capturing local features but have limitations in modeling long-range dependencies; Transformers, despite their powerful modeling capabilities, face computational bottlenecks due to the quadratic complexity of the self-attention mechanism when dealing with large images. To address these issues, we propose the Residual Mamba Block, which combines state space models (SSMs) with residual connections, providing a linear complexity attention mechanism that effectively enhances long-range dependency modeling. Experimental results demonstrate the effectiveness and superior performance of the proposed method. Furthermore, our research indicates that vision state space models have great potential to become a foundational framework for future super-resolution tasks.

关键词： Performance evaluation image quality Computational modeling Superresolution Propulsion Transformers Data models Complexity theory remote sensing Residual neural networks

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Deeply Fine-Tune a Convolutional Neural Network in remote sensing image Classification: Easter Africa Countries (EAC) 2

Deeply Fine-Tune a Convolutional Neural Network in Remote Se...

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2nd IEEE International Conference on pattern recognition and Machine Learning, PRML 2021

作者： Bosco, Musabe J. Guoyin, Wang Chongqing University of Post and Telecommunication Chongqing Key Laboratory of Computational Intelligence Chongqing China

ISBN: (纸本)9781665443838

remote sensing is resource data accessible and easy to get in different areas without time-consuming. The traditional image recognition task was unlimited to better classification. A convolutional neural network (CNN) was introduced to improve remote sensing image classification accuracy by eliminating the intra-class and class similarity. Training CNN from scratch requires a large annotated dataset that is occasional in the remote sensing area. Transfer learning of CNN weights from another large non-remote sensing dataset can occasionally help overcome typical RS image applications. Transfer learning consists of fine-tuning CNN layers to better the new dataset. In this paper, all of the experiments were done on nine categories for dataset collected in east Africa community countries (EAC) using three state-of-the-art architectures based on the effect of fine-tuning and pre-trained weights of CNN. Results indicate that fine-tuning the entire network is not always a significant way;we compared it with a process of using VGG16-DensNet pre-trained weights and RF as machine learning classified results can be improved up to 97.60. Alternatively, fine-tuning the top blocks can save computational power and produce a more robust classifier. © 2021 IEEE.

关键词： Convolutional neural networks

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Smart system for blurring undesirable objects in road applications 33

Smart system for blurring undesirable objects in road applic...

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Conference on pattern recognition and Tracking XXXIII Part of SPIE Defense and Commercial sensing Conference

作者： Kaddah, Wissam Gooya, Ehsan Sedgh Alfalou, Ayman Yncrea Ouest LSL Team LbISEN 20 Rue Cuirassee Bretagne CS 42807 F-29228 Brest 2 France

ISBN: (纸本)9781510650794;9781510650787

For many smart road applications, objects detection and recognition are one of the most important components. Indeed, precise detection of road objects is a critical task for autonomous urban driving and robotics technologies. In this paper, we describe our real-time smart system that consists in detecting and blurring undesirable road objects to anonymize and secure road users. Indeed, our proposed method is divided into three steps. The first step concerns the acquisition of images using the VIAPIX (R) system [1] developed by the ACTRIS company [2]. The second step is based on a neuronal approach for objects detection, namely vehicles, persons, road signs, etc. The third step allows to blur among the various objects detected only those which are undesirable on the road, i.e., person's faces, license plate. The obtained results demonstrate the efficiency of our robust approach in terms of good detection.

关键词： image processing Objects detection Artificial Intelligence Deep Learning Road applications Smart system

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Cloud Detection of remote sensing image Based on Transformer and multi scale Attention blocks 4

Cloud Detection of Remote Sensing Image Based on Transformer...

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4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Wen, Xiaozhuo Ye, Wujian Zhang, Ziwen School of Information Engineering Guangdong University of Technology Guangzhou510006 China Guangzhou Maritime University Guangdong Guangzhou510725 China

ISBN: (纸本)9781450398343

Cloud detection technology has been a pressing problem in remote sensing image processing. Aiming at the problem of unsatisfactory recognition accuracy of cloud regions in snow and ice scenes by existing methods, this study proposes an improved remote sensing cloud detection network model. By introducing a Transformer module and a multi-scale feature extraction module, the proposed model can achieve efficient extraction of global features and local multi-scale features of cloud regions. The experiment results show that the proposed model can better capture the edge information of different shaped cloud regions, and can reach an accuracy of 96.27% on Landsat 8 dataset. Compared with existing mainstream models such as Unet, SegNet, FCN-8s, and DeepLabV3+, the detection accuracy of the proposed model is about 2.6%-6% higher, and its ability to distinguish between ice and clouds has been greatly improved, which has great promising application value. © 2022 ACM.

关键词： Extraction

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Change Feature Refinement and Cross-Scale Interaction Network for High-Resolution remote sensing image Change Detection

Change Feature Refinement and Cross-Scale Interaction Networ...

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Signal, Information and Data processing (ICSIDP), IEEE International Conference on

作者： Yongqi Chen Shou Feng Yingjie Tang Nan Su Chunhui Zhao College of Information and Comminication Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Change detection represents a fundamental research area within remote sensing technology. Nevertheless, in practical applications, the spatial arrangement of terrestrial features is frequently highly complex. The changing objects vary significantly in size and exhibit irregular, complex shapes. Many mainstream methods are hindered by the intricate spatial scales of these changed objects, culminating in suboptimal detection performance. To overcome these obstacles, we propose a change feature refinement and cross-scale interaction network (FRCI-Net) for effective change detection. To capture more robust change features, FRCI-Net first employs a feature interaction module (FIM) and a differential feature refinement module (DFRM) to accurately capture change regions while enhancing the boundary details of the change features. To further enhance the recognition ability for multi-scale change targets, FRCI-Net leverages a cross-scale interaction module (CIM) to enable attention-driven integration and consolidation of change features across divers scales, thereby improving the network’s scale awareness of various change targets. Experiments conducted on two benchmark datasets validate that the proposed method surpasses six advanced methods.

关键词： Target recognition Shape Aggregates Benchmark testing Feature extraction Data processing remote sensing

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