Photogrammetric Engineering & Remote Sensing最新文献_第10页

MCAFNet: Multi-Channel Attention Fusion Network-Based CNN For Remote Sensing Scene Classification MCAFNet:基于多通道注意力融合网络的CNN遥感场景分类

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-03-01 DOI: 10.14358/pers.22-00121r2

Jingming Xia, Yao Zhou, Ling Tan, Yue Ding

{"title":"MCAFNet: Multi-Channel Attention Fusion Network-Based CNN For Remote Sensing Scene Classification","authors":"Jingming Xia, Yao Zhou, Ling Tan, Yue Ding","doi":"10.14358/pers.22-00121r2","DOIUrl":"https://doi.org/10.14358/pers.22-00121r2","url":null,"abstract":"Remote sensing scene images are characterized by intra-class diversity and inter-class similarity. When recognizing remote sensing images, traditional image classification algorithms based on deep learning only extract the global features of scene images, ignoring the important role\u0000 of local key features in classification, which limits the ability of feature expression and restricts the improvement of classification accuracy. Therefore, this paper presents a multi-channel attention fusion network (MCAFNet). First, three channels are used to extract the features of the\u0000 image. The channel \"spatial attention module\" is added after the maximum pooling layer of two channels to get the global and local key features of the image. The other channel uses the original model to extract the deep features of the image. Second, features extracted from different channels\u0000 are effectively fused by the fusion module. Finally, an adaptive weight loss function is designed to automatically adjust the losses in different types of loss functions. Three challenging data sets, UC Merced Land-Use Dataset (UCM), Aerial Image Dataset (AID), and Northwestern Polytechnic\u0000 University Dataset (NWPU), are selected for the experiment. Experimental results show that our algorithm can effectively recognize scenes and obtain competitive classification results.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125883456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Book Review – Spatial Analysis for Radar Remote Sensing of Tropical Forests by Gianfranco D. Grandi and Elsa Carla De Grandi 书评-热带森林雷达遥感空间分析，作者:Gianfranco D. Grandi和Elsa Carla De Grandi

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-03-01 DOI: 10.14358/pers.89.3.145

Konrad E. Kern

引用次数: 0

Robust Guardrail Instantiation and Trajectory Optimization of Complex Highways Based on Mobile Laser Scanning Point Clouds 基于移动激光扫描点云的复杂公路稳健护栏实例化与轨迹优化

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-03-01 DOI: 10.14358/pers.22-00100r2

Xin Jia, Qing Zhu, X. Ge, Ruifeng Ma, Da Zhang, Tao Liu

{"title":"Robust Guardrail Instantiation and Trajectory Optimization of Complex Highways Based on Mobile Laser Scanning Point Clouds","authors":"Xin Jia, Qing Zhu, X. Ge, Ruifeng Ma, Da Zhang, Tao Liu","doi":"10.14358/pers.22-00100r2","DOIUrl":"https://doi.org/10.14358/pers.22-00100r2","url":null,"abstract":"As a basic asset of highways, guardrails are essential objects in the digital modeling of highways. Therefore, generating the vectorial 3D trajectory of a guardrail from mobile laser scanning (MLS) point clouds is required for real digital modeling. However, most methods limit straight-line\u0000 guardrails without considering the continuity and accuracy of the guardrails in turnoff and bend areas; thus, a completed 3D trajectory of a guardrail is not available. We use RANDLA-Net for extracting guardrails as preprocessing of MLS point clouds. We perform a region growth strategy based\u0000 on linear constraints to obtain correct instantiations and a forward direction. The improved Douglas– Puke algorithm is used to simplify the center points of guardrail, and the 3D trajectory of every guardrail can be vectorized using cubic spline curve fitting. The proposed approach\u0000 is validated on two 3-km case data sets that can completely instantiate MLS point clouds with remarkable effects. Quantitative evaluations demonstrate that the proposed guardrail instantiation algorithm achieves an overall precision and recall of 98.80% and 97.5%, respectively. The generated\u0000 3D trajectory can provide a high-precision design standard for the 3D modeling of the guardrail and has been applied to a long highway scene.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125388852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GIS Tips & Tricks – Simple Customizations Can Have a Large Impact GIS提示和技巧-简单的定制可以产生很大的影响

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-03-01 DOI: 10.14358/pers.89.3.143

Alma M. Karlin

引用次数: 0

Technology Changes During My 60-year Mapping Career 我60年制图生涯中的技术变革

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-03-01 DOI: 10.14358/pers.89.3.129

D. Maune

引用次数: 0

Protecting the Places We Love: Conservation Strategies for Entrusted Lands and Parks by Breece Robertson 《保护我们热爱的地方:委托土地和公园的保护策略》作者:布里斯·罗伯逊

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-02-01 DOI: 10.14358/pers.89.2.69

M. Ramspott

引用次数: 0

Comparative Analysis of Different CNN Models for Building Segmentation from Satellite and UAV Images 不同CNN模型用于卫星和无人机图像建筑物分割的比较分析

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-02-01 DOI: 10.14358/pers.22-00084r2

Batuhan Sariturk, Damla Kumbasar, D. Seker

引用次数: 0

Car Detection from Very High-Resolution UAV Images Using Deep Learning Algorithms 利用深度学习算法从高分辨率无人机图像中检测汽车

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-02-01 DOI: 10.14358/pers.22-00101r2

Y. Kaya, H. Şenol, Abdurahman Yasin Yiğit, M. Yakar

引用次数: 0

Unmanned Aerial Vehicle (UAV)–Based Imaging Spectroscopy for Predicting Wheat Leaf Nitrogen 基于无人机的小麦叶片氮素成像光谱预测研究

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-02-01 DOI: 10.14358/pers.22-00089r2

R. Sahoo, Shalini Gakhar, R. Rejith, R. Ranjan, M. C. Meena, A. Dey, J. Mukherjee, R. Dhakar, Sunny Arya, Anchal Daas, S. Babu, P. K. Upadhyay, Kapila Sekhawat, Sudhirkumar, Mahesh Kumar, V. Chinnusamy, M. Khanna

{"title":"Unmanned Aerial Vehicle (UAV)–Based Imaging Spectroscopy for Predicting Wheat Leaf Nitrogen","authors":"R. Sahoo, Shalini Gakhar, R. Rejith, R. Ranjan, M. C. Meena, A. Dey, J. Mukherjee, R. Dhakar, Sunny Arya, Anchal Daas, S. Babu, P. K. Upadhyay, Kapila Sekhawat, Sudhirkumar, Mahesh Kumar, V. Chinnusamy, M. Khanna","doi":"10.14358/pers.22-00089r2","DOIUrl":"https://doi.org/10.14358/pers.22-00089r2","url":null,"abstract":"Quantitative estimation of crop nitrogen is the key to site-specific management for enhanced nitrogen (N) use efficiency and a sustainable crop production system. As an alternate to the conventional approach through wet chemistry, sensor-based noninvasive, rapid, and near-real-time\u0000 assessment of crop N at the field scale has been the need for precision agriculture. The present study attempts to predict leaf N of wheat crop through spectroscopy using a field portable spectroradiometer (spectral range of 400–2500 nm) on the ground in the crop field and an imaging\u0000 spectrometer (spectral range of 400–1000 nm) from an unmanned aerial vehicle (UAV) with the objectives to evaluate (1) four multivariate spectral models (i.e., artificial neural network, extreme learning machine [ELM], least absolute shrinkage and selection operator, and support vector\u0000 machine regression) and (2) two sets of hyperspectral data collected from two platforms and two different sensors. In the former part of the study, ELM outperforms the other methods with maximum calibration and validation R2 of 0.99 and 0.96, respectively. Furthermore, the image data set acquired\u0000 from UAV gives higher performance compared to field spectral data. Also, significant bands are identified using stepwise multiple linear regression and used for modeling to generate a wheat leaf N map of the experimental field.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131982122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

UAS Edge Computing of Energy Infrastructure Damage Assessment 能源基础设施损害评估的UAS边缘计算

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-02-01 DOI: 10.14358/pers.22-00087r2

Jordan Bowman, Lexie Yang, O. Thomas, Jerry Kirk, Andrew M. Duncan, D. Hughes, Shannon Meade

引用次数: 0

Photogrammetric Engineering &amp; Remote Sensing最新文献

Photogrammetric Engineering & Remote Sensing最新文献