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Changes of dominant scatterers and propagation paths as a possible origin of singular points in radar interferometry: Experimental analysis 雷达干涉测量中优势散射体和传播路径的变化作为奇点的可能来源:实验分析
2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6946414
R. Natsuaki, A. Hirose
{"title":"Changes of dominant scatterers and propagation paths as a possible origin of singular points in radar interferometry: Experimental analysis","authors":"R. Natsuaki, A. Hirose","doi":"10.1109/IGARSS.2014.6946414","DOIUrl":"https://doi.org/10.1109/IGARSS.2014.6946414","url":null,"abstract":"In Synthetic Aperture Radar (SAR) interferometry(InSAR), we generally expect that there is only one unique dominant scatterer in a pixel. To make a SAR interferogram, we have to observe every place twice. Between the observations, the dominant scatterer is expected to unchange. However, in actual situations, there are multiple scatterers in one pixel, and a dominant one may change observation by observation. Our main idea in this paper is that this change happens more frequently than we have expected, and that this phenomenon can generate singular points (SPs) in the SAR interferogram which prevent us from accurate phase unwrapping. Here, we present the results of the preliminary experiments using real aperture radar system, which suggest one of the mechanisms of the singular point generation.","PeriodicalId":385645,"journal":{"name":"2014 IEEE Geoscience and Remote Sensing Symposium","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123568587","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
Unsupervised change detection of remote sensing images based on semi-nonnegative matrix factorization 基于半非负矩阵分解的遥感图像无监督变化检测
2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6946669
Hengchao Li, N. Longbotham, W. Emery
{"title":"Unsupervised change detection of remote sensing images based on semi-nonnegative matrix factorization","authors":"Hengchao Li, N. Longbotham, W. Emery","doi":"10.1109/IGARSS.2014.6946669","DOIUrl":"https://doi.org/10.1109/IGARSS.2014.6946669","url":null,"abstract":"In this paper, we propose an unsupervised change detection approach for the multitemporal remote sensing images based on semi-nonnegative matrix factorization (semi-NMF). Specifically, the multitemporal source images, acquired at the same geographical area but at two different time instances, are first utilized to generate the difference image. Then, feature vector is created for each pixel of the difference image in such a way that its corresponding h × h block data is projected on the generated eigenvector space by principal component analysis (PCA), which is further arranged as a column vector to form a feature-by-item data matrix X. Next, we implement semi-NMF to factorize X into two nonnegative factors (i.e., the basis matrix F and the coefficient matrix G). Finally, the change detection is achieved by discriminating each column of GT according to the maximum criterion. Experimental results verify the feasibility and effectiveness of the proposed approach.","PeriodicalId":385645,"journal":{"name":"2014 IEEE Geoscience and Remote Sensing Symposium","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125261081","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
Data-based onboard estimation of antenna phase center spacing in space-borne azimuth multi-channel SAR system 星载方位多通道SAR系统中基于数据的天线相位中心间距星载估计
2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6946468
Yanqing Zhu, Jie Chen, H. Zeng, Hao-jie Zhang, Pengbo Wang, Ze Yu, Peng Xiao
{"title":"Data-based onboard estimation of antenna phase center spacing in space-borne azimuth multi-channel SAR system","authors":"Yanqing Zhu, Jie Chen, H. Zeng, Hao-jie Zhang, Pengbo Wang, Ze Yu, Peng Xiao","doi":"10.1109/IGARSS.2014.6946468","DOIUrl":"https://doi.org/10.1109/IGARSS.2014.6946468","url":null,"abstract":"In space-borne azimuth multi-channel SAR, the antenna phase center spacing should be measured precisely to obtain the reconstruction filters. In this paper, a data-based onboard estimation method of antenna phase center spacing in spaceborne multi-channel SAR system is proposed. Firstly, the principle of data-based onboard estimation is presented, then the estimation method in details is described step-by-step. Finally, simulations are carried out, with two influence factors, SCR and focusing accuracy, to demonstrate the validity of the proposed estimation method.","PeriodicalId":385645,"journal":{"name":"2014 IEEE Geoscience and Remote Sensing Symposium","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125295412","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
Sensitivity of modeled polarimetric radar ocean scattering to wind direction 模拟极化雷达海洋散射对风向的敏感性
2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6947624
A. Voronovich, V. Zavorotny
{"title":"Sensitivity of modeled polarimetric radar ocean scattering to wind direction","authors":"A. Voronovich, V. Zavorotny","doi":"10.1109/IGARSS.2014.6947624","DOIUrl":"https://doi.org/10.1109/IGARSS.2014.6947624","url":null,"abstract":"The results of airborne and satellite polarimetric radar experiments at Ku- and C-bands indicate that the sensitivity of cross-polarized signals persists for high wind speed when the co-polarized signals saturate. It was found that the cross-polarized return is also sensitive to the anisotropy of the wind-driven waves. Here, our intention is to analyze how well the available theoretical models can reproduce the azimuthal angle behavior of polarimetric radar signals. We calculated the fully polarimetric normalized radar cross section using the numerical code based on a small slope approximation of the second order. These calculations are performed for Ku- and C-bands and for two semi-empirical ocean spectral models. Results are compared with the available experimental data obtained from airborne and satellite platforms. The differences between the theoretical curves and experimental data suggest that some additional contribution, presumably from scattering by steep and breaking waves, needs to be accounted for.","PeriodicalId":385645,"journal":{"name":"2014 IEEE Geoscience and Remote Sensing Symposium","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125462956","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: 6
High performance SIFT feature classification of VHR satellite imagery for disaster management 用于灾害管理的VHR卫星图像的高性能SIFT特征分类
2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6947255
Ujwala M. Bhangale, S. Durbha
{"title":"High performance SIFT feature classification of VHR satellite imagery for disaster management","authors":"Ujwala M. Bhangale, S. Durbha","doi":"10.1109/IGARSS.2014.6947255","DOIUrl":"https://doi.org/10.1109/IGARSS.2014.6947255","url":null,"abstract":"High resolution satellite imagery is useful for disaster management activities such as damage assessment, immediate delivery of relief assistance etc. The process of analyzing Satellite imagery involves extraction of optimal features that closely represent the damaged areas. Accuracy of the analysis depends on the efficiency and robustness of selected features. Scale invariant feature transform (SIFT) enables to extract features, which are scale and rotation invariant. It provides robust features even in cluttered and partially-occluded images (such as those images that are obtained from a post disaster scenario). SIFT is robust at the cost of multiple stages involved in making features scale and rotation invariant, which is a time intensive process to apply on high resolution imagery. In general, there is a need to synthesize large amount of high-resolution, high temporal satellite data for disaster management applications to enable near real time response. However, this task is computationally intensive. Hence, this work focuses on high performance robust SIFT based feature extraction of various earthquake affected areas from high resolution imagery and subsequent classification of using Support Vector Machines (SVM). The high performance computing frameowrk consists of Tesla C2075 Graphics processing unit (GPU) with 448 cores. Results obtained from GPU implementation is shows significant gains in computational time over CPU based approach.","PeriodicalId":385645,"journal":{"name":"2014 IEEE Geoscience and Remote Sensing Symposium","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125480360","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
Spatiotemporal resolution enhancement via compressed sensing 基于压缩感知的时空分辨率增强
2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6947123
Cong Fan, Peng Liu, Lizhe Wang
{"title":"Spatiotemporal resolution enhancement via compressed sensing","authors":"Cong Fan, Peng Liu, Lizhe Wang","doi":"10.1109/IGARSS.2014.6947123","DOIUrl":"https://doi.org/10.1109/IGARSS.2014.6947123","url":null,"abstract":"In this paper, we propose a new compressed sensing based approach to enhance the spatial-temporal resolution of the remote sensing images with a pair of time-continuous spatial-temporal images and a low spatial resolution image at the same place. In compressed sensing, the measurement matrix is a key element to success. This paper presents a novel solution space model for designing the measurement matrix by establishing the correspondence between the spatial-temporal image pair to enhance the spatial-temporal resolution. The matrix we get does not only reflect the relationship between the high- and the low-spatial resolution images, but also have high randomness, thus satisfies the reconstruction requirements (e.g., RIP restriction) in compressed sensing. To verify the effectiveness of our method, we give the experimental reconstructed results and compare our results with the traditional Gaussian Random matrix and the Toplitz matrix. The experiment demonstrates the effectiveness and superiority of the proposed method.","PeriodicalId":385645,"journal":{"name":"2014 IEEE Geoscience and Remote Sensing Symposium","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125487073","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
Road extraction for SAR imagery based on the combination of beamlet and a selected kernel 基于波束和选择核相结合的SAR图像道路提取
2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6946919
Chu He, Bo Shi, Yu Zhang, Xin Xu, M. Liao
{"title":"Road extraction for SAR imagery based on the combination of beamlet and a selected kernel","authors":"Chu He, Bo Shi, Yu Zhang, Xin Xu, M. Liao","doi":"10.1109/IGARSS.2014.6946919","DOIUrl":"https://doi.org/10.1109/IGARSS.2014.6946919","url":null,"abstract":"In this paper, an algorithm applied for road extraction on SAR image is proposed, which is based on a multi-scale linear feature detector and beamlet framework, and then a quadratic kernel is introduced to offer optimal representation for the circle roads, aiming at improving the extraction quality. Firstly, a multi-scale pyramid is built on the input image and at each level the image is subdivided into a series of dyadic squares that constructs a quadtree. Then the multi-scale linear feature detector and beamlet are employed to compute pixels' responses. Finally, a quadratic kernel for non-linear candidates is introduced and adaptively selects the generating direction of segments. Experiments on TerraSAR images prove that the proposed approach significantly improves the extraction quality and performance when compared to several methods.","PeriodicalId":385645,"journal":{"name":"2014 IEEE Geoscience and Remote Sensing Symposium","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125504557","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
Damage detection due to the typhoon haiyan from high-resolution SAR images 基于高分辨率SAR图像的台风海燕损伤检测
2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6947575
Wen Liu, M. Matsuoka, B. Adriano, E. Mas, S. Koshimura
{"title":"Damage detection due to the typhoon haiyan from high-resolution SAR images","authors":"Wen Liu, M. Matsuoka, B. Adriano, E. Mas, S. Koshimura","doi":"10.1109/IGARSS.2014.6947575","DOIUrl":"https://doi.org/10.1109/IGARSS.2014.6947575","url":null,"abstract":"A strong typhoon “Haiyan” affected Southeast Asia on November 8, 2013, caused gigantic destruction in the Philippines. In this study, two pre- and one post-event COSMO-SkyMed SCSB data were used to detect the damaged area around Tacloban City, Leyte Island. First, the severe damaged areas were detected according to the difference between the pre- and post-event speckle divergence values. Then the pre- and co-event coherence (NDCI) and correlation coefficient (NDCOI) were calculated from the three temporal data. The relationships between the four building damage levels and NDCI or NDCOI value were obtained by introducing the visual interoperation result. Using this relationship, the possibility of each damage class was estimated in the whole urban area.","PeriodicalId":385645,"journal":{"name":"2014 IEEE Geoscience and Remote Sensing Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125507990","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
Weakly supervised alignment of image manifolds with semantic ties 具有语义联系的图像流形的弱监督对齐
2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6947248
D. Tuia
{"title":"Weakly supervised alignment of image manifolds with semantic ties","authors":"D. Tuia","doi":"10.1109/IGARSS.2014.6947248","DOIUrl":"https://doi.org/10.1109/IGARSS.2014.6947248","url":null,"abstract":"Aligning data distributions that underwent spectral distortions related to acquisition conditions is a key issue to improve the performance of classifiers applied to multi-temporal and multi-angular images. In this paper, we propose a feature extraction methodology, which aligns data manifolds based on their internal geometric structure and on a series of object correspondences highlighted on each image, or tie points. The weakly supervised manifold alignment (WeSMA) is a feature extractor that allows to define a common latent space, in which the images can be projected and processed by the same classifier. WeSMA relaxes the need for labeled pixels in all acquisitions of previous manifold alignment methods, an heavy constraint for remote sensing applications. Experiments on a set of World-View II images acquired at different viewing angles show the interest of the method that can compensate the spectral shift generated by the angular distortion without labels issued from the off-nadir image.","PeriodicalId":385645,"journal":{"name":"2014 IEEE Geoscience and Remote Sensing Symposium","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125528836","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
Development of level 1 algorithm of Dual Frequency Precipitation Radar (DPR) for the Global Precipitation Measurement (GPM) 全球降水测量双频降水雷达(DPR)一级算法的研制
2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6947609
T. Masaki, T. Kubota, R. Oki, M. Kojima, K. Furukawa, T. Miura, H. Kai, T. Iguchi, H. Hanado, N. Yoshida, T. Higashiuwatoko
{"title":"Development of level 1 algorithm of Dual Frequency Precipitation Radar (DPR) for the Global Precipitation Measurement (GPM)","authors":"T. Masaki, T. Kubota, R. Oki, M. Kojima, K. Furukawa, T. Miura, H. Kai, T. Iguchi, H. Hanado, N. Yoshida, T. Higashiuwatoko","doi":"10.1109/IGARSS.2014.6947609","DOIUrl":"https://doi.org/10.1109/IGARSS.2014.6947609","url":null,"abstract":"The Global Precipitation Measurement (GPM) mission consists of the GPM core Observatory (satellite) and the constellation satellites. The GPM core Observatory launched at 3:37 on February 28, 2014 (JST) from Tanegashima Space Center in Japan. It carries the Dual-frequency Precipitation Radar (DPR) and the GPM Microwave Imager (GMI). The DPR consists of Ku-band precipitation radar (KuPR) and Ka-band precipitation radar (KaPR). These radars have developed by Japan Aerospace Exploration Agency (JAXA) and National Institute of Information and Communications Technology (NICT) [1] and GMI has developed by National Aeronautics and Space Administration (NASA). The DPR level 1 algorithm has been developed by the JAXA. The main roles of level 1 algorithm are limit check for fatal and caution incidents, orbital cut and calculation of geometric information and transformation for engineering value. JAXA also has the responsibility of calibration of the DPR. The calibration methods have two types. One is the internal calibration with onboard calibration system, and the other is the external calibration with Active Radar Calibrator (ARC). These calibrations will carry out densely by the public data release. This paper describes the concepts of the level 1 algorithm and the calibration methods.","PeriodicalId":385645,"journal":{"name":"2014 IEEE Geoscience and Remote Sensing Symposium","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126822522","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
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