2019 Joint Urban Remote Sensing Event (JURSE)最新文献_第9页

Building Instance Classification using Social Media Images 使用社交媒体图像构建实例分类

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-05-01 DOI: 10.1109/JURSE.2019.8809056

E. J. Hoffmann, M. Werner, Xiaoxiang Zhu

{"title":"Building Instance Classification using Social Media Images","authors":"E. J. Hoffmann, M. Werner, Xiaoxiang Zhu","doi":"10.1109/JURSE.2019.8809056","DOIUrl":"https://doi.org/10.1109/JURSE.2019.8809056","url":null,"abstract":"Understanding urbanization and planning for the upcoming changes require detailed knowledge about the places where people live and work. Thus, knowing the usage of buildings is inevitable to distinguish between residential and commercial places. Assessing the usage of buildings from an aerial perspective alone is challenging and results in unresolveable ambiguities.As complementary data sources, social media images taken from ground level allow access to the building façades, as well as ongoing social activities around the buildings, which are very valuable information while coming to accessing the building usages. Towards the fusion of social media images and remote sensing data for this purpose, in this work we present a method to assess building usages from social media images taken in their neighborhood. Using a straight forward next neighbor classifier for mapping images to buildings and pre-trained networks for dimensionality reduction we trained a logistic regression classifier to distinguish between five different building usage classes. Applied to a study area of Los Angeles metropolitan area, USA, we obtain an average precision of 0.67. Hence, we show that social media images can be a valuable additional source to remote sensing data.","PeriodicalId":299183,"journal":{"name":"2019 Joint Urban Remote Sensing Event (JURSE)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131602296","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: 8

ES-CNN: An End-to-End Siamese Convolutional Neural Network for Hyperspectral Image Classification ES-CNN:用于高光谱图像分类的端到端暹罗卷积神经网络

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-05-01 DOI: 10.1109/JURSE.2019.8808991

M. Rao, L. Tang, Ping Tang, Zheng Zhang

引用次数: 6

What Data are needed for Semantic Segmentation in Earth Observation? 对地观测语义分割需要哪些数据?

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-05-01 DOI: 10.1109/JURSE.2019.8809071

J. Castillo-Navarro, N. Audebert, Alexandre Boulch, B. L. Saux, S. Lefèvre

引用次数: 4

Urban Scene Labeling Based on Multi-Modal Data Acquired from Aerial Sensor Platforms 基于航空传感器平台多模态数据的城市场景标注

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-05-01 DOI: 10.1109/JURSE.2019.8809035

M. Weinmann, Michael Weinmann

引用次数: 1

Large-Scale Urban Mapping using Small Stack Multi-baseline TanDEM-X Interferograms 基于小叠多基线TanDEM-X干涉图的大尺度城市制图

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-05-01 DOI: 10.1109/JURSE.2019.8808986

Yilei Shi, Yuanyuan Wang, Xiaoxiang Zhu, R. Bamler

{"title":"Large-Scale Urban Mapping using Small Stack Multi-baseline TanDEM-X Interferograms","authors":"Yilei Shi, Yuanyuan Wang, Xiaoxiang Zhu, R. Bamler","doi":"10.1109/JURSE.2019.8808986","DOIUrl":"https://doi.org/10.1109/JURSE.2019.8808986","url":null,"abstract":"Multi-baseline synthetic aperture radar (SAR) interferometric techniques, such as SAR tomography, is well established for 3-D reconstruction in the urban area. These methods usually require fairly large interferometric stacks (> 20 images) for a reliable reconstruction. They are not directly applicable to SAR interferometric (InSAR) stack with only a few acquisitions, as the extremely small number of acquisitions can severely bias the estimates from the spectral estimators, such as beamforming which is often only asymptotically optimal. In addition, the number of images also causes severe ambiguity issue of the pixel with low signal-to-noise ratio. In this work, we propose a new processing framework of 3-D reconstruction with TomoSAR using extremely small stacks. Moreover, the applicability of the algorithm is demonstrated by exploiting TanDEM-X co-registered phase preserving single look slant range complex SAR images (CoSSC) over a large-scale test site of the whole Munich city, Germany. The reconstructed results have been systematically compared with global production of TanDEM-X digital elevation models (DEM) and LiDAR dataset, which show the potential of high quality large-scale 3-D urban mapping.","PeriodicalId":299183,"journal":{"name":"2019 Joint Urban Remote Sensing Event (JURSE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115008659","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: 2

Weakly Supervised Semantic Segmentation of Satellite Images 卫星图像的弱监督语义分割

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-04-08 DOI: 10.1109/JURSE.2019.8809060

A. Nivaggioli, Hicham Randrianarivo

{"title":"Weakly Supervised Semantic Segmentation of Satellite Images","authors":"A. Nivaggioli, Hicham Randrianarivo","doi":"10.1109/JURSE.2019.8809060","DOIUrl":"https://doi.org/10.1109/JURSE.2019.8809060","url":null,"abstract":"When one wants to train a neural network to perform semantic segmentation, creating pixel-level annotations for each of the images in the database is a tedious task. If he works with aerial or satellite images, which are usually very large, it is even worse. With that in mind, we investigate how to use image-level annotations in order to perform semantic segmentation. Image-level annotations are much less expensive to acquire than pixel-level annotations, but we lose a lot of information for the training of the model. From the annotations of the images, the model must find by itself how to classify the different regions of the image. In this work, we use the method proposed by Anh and Kwak [1] to produce pixel-level annotation from image level annotation.We compare the overall quality of our generated dataset with the original dataset.In addition, we propose an adaptation of the AffinityNet that allows us to directly perform a semantic segmentation.Our results show that the generated labels lead to the same performances for the training of several segmentation networks. Also, the quality of semantic segmentation performed directly by the AffinityNet and the Random Walk is close to the one of the best fully-supervised approaches.","PeriodicalId":299183,"journal":{"name":"2019 Joint Urban Remote Sensing Event (JURSE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116635460","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: 24