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2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)最新文献

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A simulation study on remote sensing of quasi-Gaussian sea wave slopes by the wave scatterometer SWIM SWIM波浪散射仪遥感准高斯波坡的模拟研究
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730518
Ping Chen, D. Hauser, Qihui Meng, Q. Yin
{"title":"A simulation study on remote sensing of quasi-Gaussian sea wave slopes by the wave scatterometer SWIM","authors":"Ping Chen, D. Hauser, Qihui Meng, Q. Yin","doi":"10.1109/IGARSS.2016.7730518","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7730518","url":null,"abstract":"Knowledge of the distribution of wave slopes in the sea is important for understanding a number of processes occurring at or near the air-sea interface. The sea slope probability density function (pdf) of sea waves is quasi-Gaussian, being able to be expressed as a Gram-Charlier expansion to fourth-order. However, the method is absent for remote sensing the sea slope pdf in a large scale. SWIM (Surface Waves Investigation and Monitoring), used on the CFOSAT (China-France Oceanography Satellite) mission, the first space borne wave scatterometer in the world can obtain two dimensional (2D) scattering measurements of sea surface in the incidence and azimuth direction with high resolution, which made it possible to measure 2D slope pdf with high accuracy. In the paper, the feasibility of remote sensing quasi-Gaussian sea slope by SWIM is studied by the simulation.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"373 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129095997","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
High-rise building detection in dense urban area based on high resolution SAR images 基于高分辨率SAR图像的密集城区高层建筑检测
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7729400
Kan Tang, Bo Liu, Bo Zou
{"title":"High-rise building detection in dense urban area based on high resolution SAR images","authors":"Kan Tang, Bo Liu, Bo Zou","doi":"10.1109/IGARSS.2016.7729400","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7729400","url":null,"abstract":"Building extraction is an important task for SAR applications in many fields, such as urban planning, population distribution, and environment evaluation. In this paper, we focus on high-rise buildings in complex urban scenarios. The specific characteristics of high-rise building in high resolution (HR) SAR images are analyzed firstly. In particular, we explore the contextual information of bright line features as corner lines and double bounce; extract the textural feature of the periodic patterns appearing in the layover area. Given those signature details, a new algorithm exploit the building information is proposed for high-rise building detection from single SAR images. The algorithm is applied on spotlight TerraSAR-X images of the center districts in Toronto, Canada, which confirm the applicability of the method for variable high-rise buildings.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130591219","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
An automatic K-Wishart distribution ship detector for PolSAR data 用于PolSAR数据的自动K-Wishart分布船舶探测器
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730236
Weiwei Fan, Feng Zhou, Mingliang Tao, Xueru Bai
{"title":"An automatic K-Wishart distribution ship detector for PolSAR data","authors":"Weiwei Fan, Feng Zhou, Mingliang Tao, Xueru Bai","doi":"10.1109/IGARSS.2016.7730236","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7730236","url":null,"abstract":"This paper presents an automatic ship detection algorithm for polarimetric synthetic aperture radar (PolSAR) data. Based on the non-Gaussian K-Wishart distribution model for complex backscattering coefficients, the PolSAR image is clustered automatically by a modified expectation maximization algorithm. A goodness-of-fit test is incorporated to improve the model fitness of the cluster iteratively. Then, the SPAN of ship cluster center is used to detect ships. Finally, the experimental results of a real measured UAVSAR dataset show that the proposed algorithm could improve the ability of weak target detection while reduces the rate of false alarm and miss detections.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123992435","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
A comparative study on Multiple Kernel Learning for remote sensing image classification 多核学习在遥感图像分类中的比较研究
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7729386
S. Niazmardi, B. Demir, L. Bruzzone, A. Safari, Saeid Homayouni
{"title":"A comparative study on Multiple Kernel Learning for remote sensing image classification","authors":"S. Niazmardi, B. Demir, L. Bruzzone, A. Safari, Saeid Homayouni","doi":"10.1109/IGARSS.2016.7729386","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7729386","url":null,"abstract":"This paper analyzes and compares different Multiple Kernel Learning (MKL) algorithms for the classification of remote sensing (RS) images. The main purpose of the comparison is to identify advantages and disadvantages of different MKL algorithms in terms of their computational time and classification accuracy. Furthermore, some guidelines on the proper selection of the MKL algorithms associated with different RS image classification problems are derived.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123235336","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
Fully convolutional neural networks for remote sensing image classification 用于遥感图像分类的全卷积神经网络
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/igarss.2016.7730322
Emmanuel Maggiori, Y. Tarabalka, G. Charpiat, P. Alliez
{"title":"Fully convolutional neural networks for remote sensing image classification","authors":"Emmanuel Maggiori, Y. Tarabalka, G. Charpiat, P. Alliez","doi":"10.1109/igarss.2016.7730322","DOIUrl":"https://doi.org/10.1109/igarss.2016.7730322","url":null,"abstract":"We propose a convolutional neural network (CNN) model for remote sensing image classification. Using CNNs provides us with a means of learning contextual features for large-scale image labeling. Our network consists of four stacked convolutional layers that downsample the image and extract relevant features. On top of these, a deconvolutional layer upsamples the data back to the initial resolution, producing a final dense image labeling. Contrary to previous frameworks, our network contains only convolution and deconvolution operations. Experiments on aerial images show that our network produces more accurate classifications in lower computational time.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123375346","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: 140
Hyperspectral image supervised classification via multi-view nuclear norm based 2D PCA feature extraction and kernel ELM 基于多视点核范数的二维PCA特征提取和核ELM的高光谱图像监督分类
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7729382
Jue Jiang, Lili Huang, Heng Li, Liang Xiao
{"title":"Hyperspectral image supervised classification via multi-view nuclear norm based 2D PCA feature extraction and kernel ELM","authors":"Jue Jiang, Lili Huang, Heng Li, Liang Xiao","doi":"10.1109/IGARSS.2016.7729382","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7729382","url":null,"abstract":"In this paper, we propose a novel flexible framework for hyperspectral image (HSI) classification using multi-view spectral-spatial feature extracted by nuclear norm based 2D PCA. We first use the multihyphonthesis (MH) prediction method based on ridge regression to generate the 3D spatial-feature array from the HSI. Then, we apply the nuclear norm based 2D PCA to multi-view slices (the image with the spatial width and spectral dimension or with the spatial height and spectral dimension) of the former feature array, which can provide a structured spatial-spectral characterization for the reconstruction error slice and further extract the spatial-spectral feature. Finally, the 3D spatial-spectral feature array is used to represent the HSI for classification by extreme learning machine (ELM) based on Radial Basis Function (RBF) kernal. Finally, majority voting procedure is used to further improve the classification accuracy. The efficiency of the proposed method is demonstrated by experimental results with real hyperspectral dataset.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123666760","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: 11
Ground-level digital terrain model (DTM) construction from TanDEM-X InSAR data and WorldView stereo-photogrammetric images 利用TanDEM-X InSAR数据和WorldView立体摄影测量图像构建地面数字地形模型(DTM)
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730578
Seung-Kuk Lee, T. Fatoyinbo, D. Lagomasino, B. Osmanoglu, E. Feliciano
{"title":"Ground-level digital terrain model (DTM) construction from TanDEM-X InSAR data and WorldView stereo-photogrammetric images","authors":"Seung-Kuk Lee, T. Fatoyinbo, D. Lagomasino, B. Osmanoglu, E. Feliciano","doi":"10.1109/IGARSS.2016.7730578","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7730578","url":null,"abstract":"The ground-level digital elevation model (DEM) or digital terrain model (DTM) information are invaluable for environmental modeling, such as water dynamics in forests, canopy height, forest biomass, carbon estimation, etc. We propose to extract the DTM over forested areas from the combination of interferometric complex coherence from single-pass TanDEM-X (TDX) data at HH polarization and Digital Surface Model (DSM) derived from high-resolution WorldView (WV) image pair by means of random volume over ground (RVoG) model. The RVoG model is a widely and successfully used model for polarimetric SAR interferometry (Pol-InSAR) technique for vertical forest structure parameter retrieval [1][2][3][4]. The ground-level DEM have been obtained by complex volume decorrelation in the RVoG model with the DSM using stere-ophotogrammetric technique. Finally, the airborne lidar data were used to validate the ground-level DEM and forest canopy height results.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"52 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114116041","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: 1
Estimate the fluctuation of Poyang Lake water level using Cryosat-2 data 利用Cryosat-2数据估算鄱阳湖水位波动
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730775
G. Shen, Jingjuan Liao, Yun Zhao
{"title":"Estimate the fluctuation of Poyang Lake water level using Cryosat-2 data","authors":"G. Shen, Jingjuan Liao, Yun Zhao","doi":"10.1109/IGARSS.2016.7730775","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7730775","url":null,"abstract":"Poyang Lake is the largest freshwater lake in China and one of the world's sites recognized in the RAMSAR Convention on Wetlands. The wetlands provide an important range of ecosystem services. Poyang Lake is subject to seasonal fluctuations, with changes in water levels apparent in seasonal, yearly and multi-year patterns. However, the lake is shallow, and the pronounced changes in water levels could lead to the large alterations in the wetlands landscape. Satellite radar altimetry has effectively been used for monitoring the water level change in recent years. This article focuses on the estimation of the fluctuation of Poyang Lake water level using level 2 geophysical data record (GDR) altimeter datasets from Cryosat-2/Siral in recent 6 years from 2010 to 2015. The altimeter datasets were extracted according the MODIS water product image. The abnormal water levels data were eliminated according to the Pauta criterion and Lomnaofski norm. The final water level results were interpolated using B-Spline to get the water level change trend and compared to the 6 hydrology stations' record. At last, the fluctuation trend in the past 6 years was analyzed using long-term trend estimation methods.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114206110","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
Global localization in 3D maps for structured environment 结构化环境下三维地图的全局定位
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730744
Yanxin Ma, Yulan Guo, Min Lu, Jian Zhao, Jun Zhang
{"title":"Global localization in 3D maps for structured environment","authors":"Yanxin Ma, Yulan Guo, Min Lu, Jian Zhao, Jun Zhang","doi":"10.1109/IGARSS.2016.7730744","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7730744","url":null,"abstract":"This paper presents a global localization method for mobile robots based on the geometric information of structured indoor environments. With a global/local point cloud and projection map, lines are extracted from the projection maps using Hough transform. According to the directions of the obtained lines, the orientations of projection maps and point clouds are normalized. Next, the template matching algorithm is applied to the normalized global and local projection maps. Once coarse localization is completed, final accurate localization is achieved using the Iterative Closest Points (ICP) algorithm. Experimental results on several point clouds show that the proposed method can achieve high localization accuracy in real-time. The proposed method can be used for other global localization applications in structured environments.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114209650","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: 4
Optical leaf parameter estimation based on directional characteristics of leaf-scale hyperspectral images 基于叶片尺度高光谱图像方向特征的叶片光学参数估计
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730804
K. Uto, Y. Kosugi, G. Saito
{"title":"Optical leaf parameter estimation based on directional characteristics of leaf-scale hyperspectral images","authors":"K. Uto, Y. Kosugi, G. Saito","doi":"10.1109/IGARSS.2016.7730804","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7730804","url":null,"abstract":"With the advent of UAVs and lightweight hyperspectral imagers, leaf-scale hyperspectral images of agricultural fields are available by low-altitude observation. Although leaf-scale hyperspectral images eliminate the effect of volume scattering and spectral mixing, the hyperspectral profile is affected by the surface shape of the leaf, i.e., shade and shadow. Because diffuse reflection of leaves has directional characteristics, the normalized vectors of the diffuse reflection are distributed around a point on a unit hypersphere. The center of the diffuse reflection is equivalent to the Lambert coefficients of the leaves. In this paper, we propose a method in which Lambert coefficients are estimated by investigating directional characteristics of leaf-scale hyperspectral images based on von Mises-Fisher distribution (vMF).","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114370626","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
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