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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing"

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A Novel Framework for Classifying remote sensing images using Convolutional Neural Networks

A Novel Framework for Classifying Remote Sensing Images usin...

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2024 International conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Manoharan, Thiyagarajan Basha, Shaik Hedayath Murugan, J.Senthil Suja, G.P. Rajkumar, R. Srimathi, S. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology India R.M.K. College of Engineering and Technology Department of Ece India Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Cse Avadi Chennai India Muslim Arts College Thiruvithancode Department of Computer Science Kanyakumari629174 India Rajarajeswari College of Engineering Computer Science and Engineering Mysore Road Bengaluru74 India Simats Saveetha School of Engineering Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350359688

The classification of Hyperspectral remote sensing images (HRSIs) using Convolutional Neural Networks (CNNs) has gained significant. Due to excessive dimensional data and a lack of sample training examples, the Hughes phenomenon is frequently seen in hyperspectral remote sensing photos. In the realm of high-dimensional information processing, it is worth noting that there is a notable consumption of time and computing power. Additionally, there is a possibility that the features extracted may not accurately represent the data, leading to suboptimal classification efficiency and accuracy. In this research, a novel framework called GCNN is proposed, which combines the concepts of global context spatial attention (GCSA) and three-dimensional convolutional neural network (3DCNN). The GCNN framework aims to enhance the performance of spatial attention and convolutional neural networks by leveraging global context information. By integrating GCSA and 3DCNN, GCNN offers a promising approach for various applications in computer vision and image analysis. The mix-up operation is a commonly employed technique in the field of remote sensing image analysis. Its primary purpose is the spatially heterogeneous data available in these photos, and improve it. This is achieved by transforming the discrete sample space into a continuous one, thereby promoting improved flatness in the local vicinity of the data space. The integration of GCSA into the network allows for the encoding of contextual information from remote sensing scene images into the local features. The findings of this study indicate that the utilization of CNN for remote sensing image recognition yields significantly higher accuracy compared to traditional image recognition models. The recognition accuracy achieved by CNN in this context is reported to be as high as 97%. © 2024 IEEE.

关键词： Convolutional neural networks

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Attention-based U-Net for building extraction from remote sensing images

Attention-based U-Net for building extraction from remote se...

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2022 International conference on image, Signal processing, and pattern recognition, ISPP 2022

作者： Zhang, Qiong College of Geomatics and Geoinformation Guilin university of technology Guangxi Guilin China

ISBN: (纸本)9781510654846

To extract ground objects from remote sensing images based on deep learning method is one of the current research hotspots, and building information has attracted much attention as an important artificial feature. Convolutional neural networks have shown great potential for building extraction tasks. In view of the current research status, this paper proposes a lightweight improved network ECAU-Net that combines semantic segmentation network U-Net and efficient channel attention mechanism, and applies it to automatic extraction of buildings from high-resolution remote sensing images. Experiments show that the extraction method proposed in this paper shows good extraction results on the Massachusetts building data set, and the accuracy, recall, precision, and F1 score are better than the original U-Net. Therefore, this method does have certain advantages. © 2022 SPIE.

关键词： remote sensing

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AutoLC: Search Lightweight and Top-Performing Architecture for remote sensing image Land-Cover Classification 26

AutoLC: Search Lightweight and Top-Performing Architecture f...

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26th International conference on pattern recognition / 8th International Workshop on image Mining - Theory and Applications (IMTA)

作者： Zheng, Chenyu Wang, Junjue Ma, Ailong Zhong, Yanfei Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ LIESMARS Wuhan Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Land-cover classification has long been a hot and difficult challenge in remote sensing community. With massive High-resolution remote sensing (HRS) images available, manually and automatically designed Convolutional Neural Networks (CNNs) have already shown their great latent capacity on HRS land-cover classification in recent years. Especially, the former can achieve better performance while the latter is able to generate lightweight architecture. Unfortunately, they both have shortcomings. On the one hand, because manual CNNs are almost proposed for natural image processing, it becomes very redundant and inefficient to process HRS images. On the other hand, nascent Neural Architecture Search (NAS) techniques for dense prediction tasks are mainly based on encoder-decoder architecture, and just focus on the automatic design of the encoder, which makes it still difficult to recover the refined mapping when confronting complicated HRS scenes. To overcome their defects and tackle the HRS land-cover classification problems better, we propose AutoLC which combines the advantages of two methods. First, we devise a hierarchical search space and gain the lightweight encoder underlying gradient-based search strategy. Second, we meticulously design a lightweight but top-performing decoder that is adaptive to the searched encoder of itself. Finally, experimental results on the LoveDA land-cover dataset demonstrate that our AutoLC method outperforms the state-of-art manual and automatic methods with much less computational consumption.

关键词： Training Gradient methods image processing Computer architecture Manuals Search problems Decoding

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Typical Scene Material Classification Based on remote sensing image Data 12

Typical Scene Material Classification Based on Remote Sensin...

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

作者： Gao, Yang Dai, Ling Li, Haipeng Zhang, Yini Wang, Feng Beijing Aerospace Automatic Control Institute Beijing100854 China School of Electrical and Automation Engineering Nanjing Normal University Jiangsu Nanjing210023 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

In spectral scene simulation applications, it is necessary to recognize and detect desert satellite images and satellite images containing water. However, previous network models such as AlexNet, VggNet, and GoogleNet have gradually fallen behind the trend of the times. Compared with the latest network models, they not only have slow training speeds but also do not achieve high accuracy. Therefore, this study adopts a series of the latest network models to solve this problem and compare and analyze the results. © 2024 SPIE.

关键词： Semantic Segmentation

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Spatiotemporal pattern Evolution for Vegetation Using remote sensing Technology: A case study of Zhoushan Island, China

Spatiotemporal Pattern Evolution for Vegetation Using Remote...

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

作者： Chen, Yankun Chen, Chao Liu, Zhisong School of Information Engineering Zhejiang Ocean University Zhoushan316022 China School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou215009 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9781510680012

For ecological environment protection research and regional sustainable development, vegetation, as a crucial land cover type, necessitates long-term, large-scale, and highly accurate monitoring. This paper formulates an enduring vegetation monitoring approach using Landsat satellite remote sensing imagery on the Google Earth Engine (GEE) cloud platform. By computing the Normalized Difference Vegetation Index (NDVI), we formulate a method for prolonged remote sensing monitoring of vegetation, unveiling its temporal and spatial pattern evolution. Firstly, based on Landsat remote satellite imagery and GEE, we calculated the NDVI. By leveraging the Normalized Difference Water Index (NDWI), we removed inland water bodies and outliers within the study area, enabling the precise generation of spatiotemporal NDVI distribution maps. Subsequently, the NDVI was analyzed for time trends in conjunction with grade transfer matrices. Finally, combined with Moran's I, high/low clusters, clustering and outlier, cold/hot spots, etc., the NDVI spatial pattern characteristics were analyzed. Using the Zhoushan Island in China as an example, the results indicated that: (1) The most significant changes were observed in the low NDVI area (0-0.2), which expanded from 28.84 km2 to 67.29 km2. (2) Concentrated mainly in the inland areas are the NDVI High-High clusters and the significant hot spots, while the coastal areas primarily feature NDVI Low-Low clusters and significant cold spots. © 2024 SPIE.

关键词： Vegetation

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The Egyptian Journal of remote sensing and Space Science

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The Egyptian Journal of remote sensing and Space Science

作者： Prof. Dr. Islam Abou El-Magd

出版社：ELSEVIER

The Egyptian Journal of remote sensing and Space Sciences (EJRS) is dedicated to publishing high-quality, peer-reviewed research articles that demonstrate innovative advancements in methodologies, techniques, algorithms, and scientific understanding within the fields of remote sensing and space sciences. EJRS emphasizes contributions that push the boundaries of knowledge and offer transformative solutions to real-world challenges in the following areas: 1. Space and ground remote sensing technology: image and signal processing for: Optical and RADAR image processing theories and technical development image enhancement and restoration, machine learning, data fusion, etc image understanding such as pattern recognition, classification techniques, object and change detection, and content-based retrieval of images Geophysical measurements and LIDAR image processing theories and technical development 2. Advanced Applications of remote sensing and GIS in: Geology: New methodologies for planetary geology, geomorphology, structural geology, hydrogeology, mineral exploration, space archaeology, and coastal/marine geology Agriculture: Soil Science - Crop conditions assessment, Development of precision agriculture techniques, innovative irrigation management systems, advanced tools for crop yield estimation and agro-environmental protection Urban Planning: Novel GIS-based solutions for highway layouts, site investigations, engineering soil mapping, and urban corridor surveys Hydrology: Advanced modeling techniques for water resource management - dam sites location - flash flood analysis - water quality modeling and monitoring Environment: Land Degradation assessment- Disasters - Desertification-- Coastal and Marine Environment - climate change impact studies - Land Evaluation Geomatics: Innovations in digital photogrammetry, LIDAR, hyperspectral imaging, change detection, and feature extraction Machine Learning and AI: Pioneering applications in natural hazard assessment, mitig

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Multi-Scale Mixed Pixel Network for remote sensing image Super-Resolution 7

Multi-Scale Mixed Pixel Network for Remote Sensing Image Sup...

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

作者： Yang, He Ji, Xun Wang, Xu Chen, Shijie Dalian Maritime University School of Marine Electrical Engineering Liaoning Dalian China

ISBN: (纸本)9798350350890

Deep learning-based image super-resolution (SR) technology has gained extensive attention in the remote sensing community, which aims to reconstruct the abundant details of target images. However, the practical application of existing deep learning-based SR methods is often limited by the trade-off between network performance and computational load. This paper presents a novel multi-scale mixed pixel network (MSMP-Net) to explore the precise and cost-effective SR reconstruction of remote sensing images. Specifically, a dual-path feature capture mechanism (DPFCM) architecture is devised that incorporates diverse dilated convolution operations to comprehensively capture hierarchical features across varying receptive fields. In addition, a triple-path high-resolution rebuilder (TPHRR) structure is designed, which uses a set of dense branches, residual branches and pyramid branches to effectively promote the transfer and reuse of features. Extensive experiments reveal that our proposed MSMP-Net outperforms other state-of-the-art SR methods in reconstruction effects. © 2024 IEEE.

关键词： Cost effectiveness

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Analysis and Modeling Simulation of Hyperspectral Target Characteristics in remote sensing Earth Observation 12

Analysis and Modeling Simulation of Hyperspectral Target Cha...

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

作者： Cong, Longjian Li, Haipeng Wang, Hongshuo Xu, Siyan Huang, Yuhang Beijing Aerospace Automatic Control Institute Beijing100854 China Huazhong University of Science and Technology Hubei Province Wuhan430074 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

Aiming at the demand for target detection of typical ground and object backgrounds in hyperspectral earth observation, this project solves the problems of insufficient sample size and low fidelity of target environment scene in intelligent learning, training and testing of a novel optoelectronic detection system, breaks through the key technologies of modeling of hyperspectral imaging simulation mechanism, modeling of hyperspectral reflectance/radiation characteristics of typical target background, computation of hyperspectral atmospheric transmission characteristics, and modeling of hyperspectral optical detectors, etc., and organically integrates remote sensing imaging and spectral measurement technologies. Through simulation, it simultaneously acquires spatial and spectral radiation information data from typical target backgrounds, thus providing foundational data support for the design of target detection algorithms based on hyperspectral data. © 2024 SPIE.

关键词： Hyperspectral imaging

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WildlifeMapper: Aerial image Analysis for Multi-Species Detection and Identification

WildlifeMapper: Aerial Image Analysis for Multi-Species Dete...

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IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Kumar, Satish Zhang, Bowen Gudavalli, Chandrakanth Levenson, Connor Hughey, Lacey Stabach, Jared A. Amoke, Irene Ojwang, Gordon Mukeka, Joseph Mwiu, Stephen Ogutu, Joseph Frederick, Howard Manjunath, B. S. Univ Calif Santa Barbara Santa Barbara CA 93106 USA Smithsonian Natl Zoo & Conservat Biol Inst Washington DC USA Kenya Wildlife Trust Nairobi Kenya Univ Groningen Groningen Netherlands Wildlife Res & Training Inst Nairobi Kenya Univ Hohenheim Stuttgart Germany Tanzania Wildlife Res Inst Dar Es Salaam Tanzania

ISBN: (纸本)9798350353006

We introduce WildlifeMapper (WM), a flexible model designed to detect, locate, and identify multiple species in aerial imagery. It addresses the limitations of traditional, labor-intensive wildlife population assessments that are central to advancing environmental conservation efforts worldwide. While a number of methods exist to automate this process, they are often limited in their ability to generalize to different species or landscapes due to the dominance of homogeneous backgrounds and/or poorly captured local image structures. WM introduces two novel modules that help to capture the local structure and context of objects of interest to accurately localize and identify them, achieving a state-of-the-art (SOTA) detection rate of 0.56 mAP. Further, we introduce a large aerial imagery dataset with more than 11k images and 28k annotations verified by domain experts. WM also achieves SOTA performance on 3 other publicly available aerial survey datasets collected across 4 different countries, improving mAP by 42%. Source code and trained models are available at Github (1).

关键词： Aerial imagery Animal Detection Object Detection remote sensing

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A Physically Feasible Counter-Attack Method for remote sensing Imaging Point Clouds 6th

A Physically Feasible Counter-Attack Method for Remote Sensi...

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

作者： Wei, Bo Wei, Huanchun Cao, Cong Huang, Teng Xiong, Huagang Lang, Aobo Zhang, Xiqiu Zhang, Haiqing Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China Guangxi Univ Informat Engn Guangxi Coll Beidou Nanning Peoples R China Guangzhou Univ Inst Artificial Intelligence & Blockchain Guangzhou Peoples R China Sun Yat Sen Univ Sch Arts Guangzhou Peoples R China

ISBN: (纸本)9789819984619;9789819984626

This research introduces an innovative approach to address the vulnerability of deep learning-based point cloud target recognition systems to adversarial sample attacks. Instead of directly tampering with the spatial location information of the point cloud data, the approach focuses on modifying specific attribute information. The modified point cloud data is then associated with a simulated physical environment for comprehensive attack testing. The experimental findings demonstrate the aggressive nature of the generated counter-samples achieved through signal amplitude modulation, effectively deceiving deep learning-based target recognition systems. The experimental results highlight the effectiveness of the adversarial object samples generated by modifying the signal amplitude of the point cloud data, showcasing their strong misguiding capabilities towards the deep learning-based target recognition algorithm. This approach ensures good stealthiness and practicality, making it a viable attack method applicable in physical scenarios.

关键词： Point Cloud Target recognition Counter-sample Attacks

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