2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)最新文献_第3页

The Ultra-wideband Software-Defined Radiometer (UWBRAD) for ice sheet internal temperature sensing: Results from recent observations 用于冰盖内部温度传感的超宽带软件定义辐射计(UWBRAD):最近观测的结果

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730848

J. Johnson, K. Jezek, M. Aksoy, A. Bringer, C. Yardim, M. Andrews, Chi-Chih Chen, Domenic J. Belgiovane, V. Leuski, M. Durand, Yuna Duan, G. Macelloni, M. Brogioni, Shurun Tan, Tianlin Wang, L. Tsang

引用次数: 12

Radar-based rainfall erosivity and hillslope erosion modelling in a burnt national park after storm events 一个被烧毁的国家公园在风暴事件后基于雷达的降雨侵蚀力和山坡侵蚀模型

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7729800

Xihua Yang, Qinggaozi Zhu, Bofu Yu, Liying Sun

引用次数: 0

Multi-dimensional airborne holographic SAR tomography reconstruction for glaciers at L-/P-band L-/ p波段冰川航空全息SAR层析成像重建

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7728993

Octavio Ponce, R. Scheiber, P. Prats, I. Hajnsek, A. Reigber

{"title":"Multi-dimensional airborne holographic SAR tomography reconstruction for glaciers at L-/P-band","authors":"Octavio Ponce, R. Scheiber, P. Prats, I. Hajnsek, A. Reigber","doi":"10.1109/IGARSS.2016.7728993","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7728993","url":null,"abstract":"This paper presents a study of the processing chain for ice and glacier structures in the holographic SAR tomography (HoloSAR) mode. The algorithm is mainly based on the fast factorized back-projection (FFBP) to solve a three-dimensional space using fully polarimetric data. The motivation of HoloSAR in the cryosphere is mainly driven by recent investigations that have shown the capabilities of this mode to obtain 3-D imaging reconstructions of volumes over 360 ° at very high resolution. In this regard, a HoloSAR campaign was conducted at L-/P-band by DLR's F-SAR sensor in K-Transect, Greenland. The first experimental results of this study show a polarimetric analysis of the resulting images of a single circular flight, where the arc- and circular patterns of the 2-D images in the (x, y) plane already indicate significant wave penetration. The second part presents 3-D images obtained by the combination of the circular and vertical synthetic apertures, which enable an estimation of the vertical profile of the glacier. This mode potentially allows for a more accurate estimation of the vertical profile of ice sheets and bedrock.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"101 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":"121120559","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

Wind farm effect on grassland vegetation due to its influence on the range, intensity and variation of wind direction 风电场对草地植被的影响主要来自于风电场对风速范围、强度和风向的影响

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7729331

Guoqing Li, Chunhua Zhang, Li Zhang, Meng Zhang

引用次数: 4

Wind and wave under strong tropical cyclones 强热带气旋下的风浪

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730202

X. Yin, Ruanyu Zhang, Xing-ou Xu, Yingzhu Huang, Zhenzhan Wang

引用次数: 0

Study on echo simulations of spaceborne millimeter-wave cloud profile radar 星载毫米波云廓线雷达回波模拟研究

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730247

Kai Cui, Dandan Zhang, Xichao Dong, M. Bian

引用次数: 0

An outlier generation approach for one-class random forests: An example in one-class classification of remote sensing imagery 一类随机森林的离群值生成方法:以遥感影像一类分类为例

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730331

Zhongkui Shi, Peijun Li, Yi Sun

引用次数: 9

Multi-aperture anomaly detector for clutter background 杂波背景多孔径异常探测器

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730730

Min Li, Xinnan Fan, Xuewu Zhang, Puhuang Li

{"title":"Multi-aperture anomaly detector for clutter background","authors":"Min Li, Xinnan Fan, Xuewu Zhang, Puhuang Li","doi":"10.1109/IGARSS.2016.7730730","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7730730","url":null,"abstract":"Without priori information, anomaly detector has more important utility compared with supervised target detection. Many classical anomaly detectors have obtained perfect performance in many situations. However, there still have two problems which are correlated with accuracy of anomaly detector. Firstly, clutter background induced more and more difficult pixel which have moderate statistical difference. Then, ideal uncontaminated subset of clutter background is hard to be obtain which is used to estimate background model. Secondly, difference of spectral content of different background objects will effect salience of anomaly targets. And it is arbitrary that uncertain pixels is nominated as non-anomalies by one threshold. For above two problems, a multi-aperture anomaly detector is proposed in this paper. Without selection of anomaly-free pixels and accurate statistical model, the proposed anomaly detector is expected to decrease false alarm rate with clutter background. A multi-aperture division for hyperspectral cube is conducted by iterative process. Statistical data of ever subaperture will be named as basis, which represent spectral characteristic of a certain range of spectral cube. Then, anomaly salience is proposed to measure the difference between pixels and sub-aperture basis. On the other hand, continuity of membership value based on fuzzy logical theory is more suitable to nominate difficulty pixels which has moderate anomaly salience. At last defuzzification ruler can be used to fuse different detection results from multi-aperture.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"13 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":"125027067","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 nonlinear hyperspectral unmixing based on the generalized bilinear model 基于广义双线性模型的无监督非线性高光谱解混

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730712

Jing Li, Xiaorun Li, Liaoying Zhao

引用次数: 1

Determination of optimal hyperspectral variables to monitor wheat biomass 小麦生物量监测最佳高光谱变量的确定

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7729819

Chen Zhou, Min Jia, Xue Ma, T. Cheng, Yan Zhu, Yongchao Tian, W. Cao, Xia Yao

{"title":"Determination of optimal hyperspectral variables to monitor wheat biomass","authors":"Chen Zhou, Min Jia, Xue Ma, T. Cheng, Yan Zhu, Yongchao Tian, W. Cao, Xia Yao","doi":"10.1109/IGARSS.2016.7729819","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7729819","url":null,"abstract":"It is critical to estimate the biomass for assessing crop growth and predicting yield in crop. The hyperspectral techniques provide a powerful technique for monitoring crop biomass. The previous studies about using hyperspectral data to study crop mainly focused on models based on the full spectra or the manually selected spectra. The stability and prediction ability of full spectra models may be weakened because of involving noises, other unrelated and collinear spectral variables. The manually selected spectra were extracted by vegetation indices, spectral absorption features, derivative spectra and spectral locations in common use, which may ignore the other spectral information, not identify the high biomass and impact the accuracy of model. In order to extract the optimal hyperspectral feature of wheat biomass, several algorithms for sensitive variable selection were compared to determine the spectral variables for estimation model of wheat biomass. Synergy interval partial least squares (SIPLS) [1] and successive projections algorithm (SPA) [2] were employed to eliminate useless variables from the full hyperspectral data. On that basis an approach was proposed by combing SIPLS with SPA to determine the optimal spectra. Then, the optimal features were considered as input variables of the partial least-squares regression (PLSR) method [3],which was the mostly used calibration model and regression method. The determination coefficient of calibration (R2C), the root mean square error (RMSEV), relative root mean square error of validation (RMSEV) and the number of input variables were presented to compare the performance of different methods in extracting sensitive spectral information.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"95 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":"125912010","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