IEEE Geoscience and Remote Sensing Letters最新文献_第8页

A Generalized Volume Scattering Model-Based Vegetation Index From Polarimetric SAR Data 基于广义体散射模型的极化SAR植被指数研究

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2019-04-23 DOI: 10.1109/LGRS.2019.2907703

D. Ratha, D. Mandal, Vineet Kumar, H. Mcnairn, A. Bhattacharya, A. Frery

{"title":"A Generalized Volume Scattering Model-Based Vegetation Index From Polarimetric SAR Data","authors":"D. Ratha, D. Mandal, Vineet Kumar, H. Mcnairn, A. Bhattacharya, A. Frery","doi":"10.1109/LGRS.2019.2907703","DOIUrl":"https://doi.org/10.1109/LGRS.2019.2907703","url":null,"abstract":"In this letter, we propose a novel vegetation index from polarimetric synthetic-aperture radar (PolSAR) data using the generalized volume scattering model. The geodesic distance between two Kennaugh matrices projected on a unit sphere proposed by Ratha et al. is used in this letter. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and generalized volume scattering models. A factor is estimated corresponding to the ratio of the minimum to the maximum geodesic distances between the observed Kennaugh matrix and the set of elementary targets: trihedral, cylinder, dihedral, and narrow dihedral. This factor is then scaled and multiplied with the similarity measure to obtain the novel vegetation index. The proposed vegetation index is compared with the radar vegetation index (RVI) proposed by Kim and van Zyl. A time series of RADARSAT-2 data acquired during the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) campaign in Manitoba, Canada, is used to assessing the proposed RVI.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"16 1","pages":"1791-1795"},"PeriodicalIF":4.8,"publicationDate":"2019-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2019.2907703","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44604132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 30

Discriminative Adaptation Regularization Framework-Based Transfer Learning for Ship Classification in SAR Images 基于判别自适应正则化框架的SAR图像船舶分类迁移学习

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2019-04-23 DOI: 10.1109/LGRS.2019.2907139

Y. Xu, H. Lang, Lihui Niu, Chenguang Ge

{"title":"Discriminative Adaptation Regularization Framework-Based Transfer Learning for Ship Classification in SAR Images","authors":"Y. Xu, H. Lang, Lihui Niu, Chenguang Ge","doi":"10.1109/LGRS.2019.2907139","DOIUrl":"https://doi.org/10.1109/LGRS.2019.2907139","url":null,"abstract":"Ship classification in synthetic-aperture radar (SAR) images is of great significance for dealing with various marine matters. Although traditional supervised learning methods have recently achieved dramatic successes, but they are limited by the insufficient labeled training data. This letter presents a novel unsupervised domain adaptation (DA) method, termed as discriminative adaptation regularization framework-based transfer learning (D-ARTL), to address the problem in case that there is no labeled training data available at all in the SAR image domain, i.e., target domain (TD). D-ARTL improves the original ARTL by adding a novel source discriminative information preservation (SDIP) regularization term. This improvement achieves an efficient transfer of interclass discriminative ability from source domain (SD) to TD, while achieving the alignment of cross-domain distributions. Extensive experiments have verified that D-ARTL outperforms state-of-the-art methods on the task of ship classification in SAR images by transferring the automatic identification system (AIS) information.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"16 1","pages":"1786-1790"},"PeriodicalIF":4.8,"publicationDate":"2019-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2019.2907139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41760101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Collaborative Cross-Domain $k$ NN Search for Remote Sensing Image Processing 遥感图像处理的协作跨域$k$NN搜索

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2019-04-18 DOI: 10.1109/LGRS.2019.2906686

Ying Zhong, Wei Weng, Jianmin Li, Shunzhi Zhu

{"title":"Collaborative Cross-Domain $k$ NN Search for Remote Sensing Image Processing","authors":"Ying Zhong, Wei Weng, Jianmin Li, Shunzhi Zhu","doi":"10.1109/LGRS.2019.2906686","DOIUrl":"https://doi.org/10.1109/LGRS.2019.2906686","url":null,"abstract":"<inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula>NN search is a fundamental function in image processing, which is useful in many real applications, including image cluster, image classification, and image understanding and analysis in general. In this light, we propose and study a novel collaborative cross-domain <inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula>NN search (CD-<inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula>NN) in multidomain space. Given a query location <inline-formula> <tex-math notation=\"LaTeX\">$q$ </tex-math></inline-formula> in a multidomain space (e.g., spatial domain, temporal domain, textual domain, and so on), the CD-<inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula>NN finds top-<inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula> data points with the minimum distance to <inline-formula> <tex-math notation=\"LaTeX\">$q$ </tex-math></inline-formula>. This problem is challenging due to two reasons. First, how to define practical distance measures to evaluate the distance in multidomain space. Second, how to prune the search space efficiently in multiple domains. To address the challenges, we define a linear combination method-based distance measure for multidomain space. Based on the distance measure, a collaborative search method is developed to constrain the CD search space in a comparable smaller range. A pair of upper and lower bounds is defined to prune the search space in multiple domains effectively. Finally, we conduct extensive experiments to verify that the developed methods can achieve a high performance.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"16 1","pages":"1801-1805"},"PeriodicalIF":4.8,"publicationDate":"2019-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2019.2906686","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46361514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Late Winter Observations of Sea Ice Pressure Ridge Sail Height 晚冬海冰气压脊帆高观测

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2019-01-10 DOI: 10.1002/essoar.10500429.1

K. Duncan, S. Farrell, J. Hutchings, J. Richter-Menge

{"title":"Late Winter Observations of Sea Ice Pressure Ridge Sail Height","authors":"K. Duncan, S. Farrell, J. Hutchings, J. Richter-Menge","doi":"10.1002/essoar.10500429.1","DOIUrl":"https://doi.org/10.1002/essoar.10500429.1","url":null,"abstract":"Analysis of high-resolution imagery acquired by the Digital Mapping System during annual, late-winter NASA Operation IceBridge surveys of Arctic sea ice between 2010 and 2018 reveals that pressure ridge sail heights (<inline-formula> <tex-math notation=\"LaTeX\">${H} _{mathbf {S}}$ </tex-math></inline-formula>) vary regionally and interannually. We find distinct differences in <inline-formula> <tex-math notation=\"LaTeX\">${H} _{mathbf {S}}$ </tex-math></inline-formula> distributions between the central Arctic (CA) and the Beaufort/Chukchi Seas region. Our results show that differences with respect to ice type occur within the tails of the <inline-formula> <tex-math notation=\"LaTeX\">${H} _{mathbf {S}}$ </tex-math></inline-formula> distributions and that the 95th and 99th percentiles of <inline-formula> <tex-math notation=\"LaTeX\">${H} _{mathbf {S}}$ </tex-math></inline-formula> are strong indicators of the predominant ice type in which the pressure ridge formed. During the first part of the study period <inline-formula> <tex-math notation=\"LaTeX\">${H} _{mathbf {S}}$ </tex-math></inline-formula> increased, with the largest sails observed in the winters of 2015 and 2016, after which <inline-formula> <tex-math notation=\"LaTeX\">${H} _{mathbf {S}}$ </tex-math></inline-formula> declined, suggesting that the most heavily deformed sea ice may have drifted beyond the area surveyed and exited the CA. Our analysis of the interannual and regional variability in sea ice deformation in the western Arctic during the last decade provides an improved understanding of sail height that will help advance ridge parameterizations in sea ice models.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1525-1529"},"PeriodicalIF":4.8,"publicationDate":"2019-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/essoar.10500429.1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48221682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Notice to the reader 读者须知

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2019-01-01 DOI: 10.1109/lgrs.2013.2256414

引用次数: 109

Nuclear Magnetic Resonance Spectrum Inversion Based on the Residual Hybrid

$l_{1}$

/

$l_{2}$

基于残差混合$l_{1}$ / $l_{2}$的核磁共振谱反演

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2018-08-01 DOI: 10.1109/lgrs.2018.2835457

Y. Zou, R. Xie, Mi Liu, Jiangfeng Guo, G. Jin

引用次数: 1

Moving Target Refocusing Algorithm in 2-D Wavenumber Domain After BP Integral BP积分后二维波数域运动目标重聚焦算法

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2018-01-01 DOI: 10.1109/LGRS.2017.2777494

Qi Dong, M. Xing, X. Xia, Sheng Zhang, Guangcai Sun

引用次数: 12

Interference Mitigation for Automotive Radar Using Orthogonal Noise Waveforms 基于正交噪声波形的汽车雷达干扰抑制

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2018-01-01 DOI: 10.1109/LGRS.2017.2777962

Zhihuo Xu, Quan Shi

引用次数: 42

Semisupervised Classification of Polarimetric SAR Image via Superpixel Restrained Deep Neural Network 基于超像素约束深度神经网络的极化SAR图像半监督分类

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2018-01-01 DOI: 10.1109/LGRS.2017.2777450

Jie Geng, Xiaorui Ma, Jianchao Fan, Hongyu Wang

{"title":"Semisupervised Classification of Polarimetric SAR Image via Superpixel Restrained Deep Neural Network","authors":"Jie Geng, Xiaorui Ma, Jianchao Fan, Hongyu Wang","doi":"10.1109/LGRS.2017.2777450","DOIUrl":"https://doi.org/10.1109/LGRS.2017.2777450","url":null,"abstract":"The classification of polarimetric synthetic aperture radar (PolSAR) image is of crucial significance for SAR applications. In this letter, a superpixel restrained deep neural network with multiple decisions (SRDNN-MDs) is proposed for PolSAR image classification, which not only extracts effective superpixel spatial features and degrades the influence of speckle noises but also deals with the limited training samples. First, the polarimetric features of coherency matrix and Yamaguchi decomposition are extracted as initial features, and superpixel segmentation is conducted on the Pauli color-coded image to acquire the superpixel averaged features. Then, an SRDNN based on sparse autoencoders is proposed to capture superpixel correlative features and reduce speckle noises. After that, MDs, including nonlocal decision and local decision, are developed to select credible testing samples. Finally, our deep network is updated by the extended training set to yield the final classification map. Experimental results demonstrate that the proposed SRDNN-MD yields higher accuracies compared with other related approaches, which indicate that the proposed method is able to capture superpixel correlative information and adds the information of unlabeled samples to improve the classification performance.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"4 1","pages":"122-126"},"PeriodicalIF":4.8,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2017.2777450","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62471947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 41

Time–Frequency Analysis of Seismic Data Using a Three Parameters S Transform 基于三参数S变换的地震资料时频分析

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2018-01-01 DOI: 10.1109/LGRS.2017.2778045

Naihao Liu, Jing-Hua Gao, Bo Zhang, Fangyu Li, Qian Wang

引用次数: 62