IEEE Geoscience and Remote Sensing Letters最新文献_第10页

Hyperspectral Image Classification via Multiscale Joint Collaborative Representation With Locally Adaptive Dictionary 基于局部自适应字典的多尺度联合协同表示高光谱图像分类

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2018-01-01 DOI: 10.1109/LGRS.2017.2776113

Jinghui Yang, Jinxi Qian

引用次数: 29

Low-Rank Plus Sparse Decomposition and Localized Radon Transform for Ship-Wake Detection in Synthetic Aperture Radar Images 低秩加稀疏分解和局部Radon变换在合成孔径雷达图像舰船尾迹检测中的应用

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2018-01-01 DOI: 10.1109/LGRS.2017.2777264

F. Biondi

引用次数: 51

Unsupervised Classification of PolSAR Data Using a Scattering Similarity Measure Derived From a Geodesic Distance 基于测地线距离的散射相似度量的PolSAR数据无监督分类

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2017-12-01 DOI: 10.1109/LGRS.2017.2778749

D. Ratha, A. Bhattacharya, A. Frery

{"title":"Unsupervised Classification of PolSAR Data Using a Scattering Similarity Measure Derived From a Geodesic Distance","authors":"D. Ratha, A. Bhattacharya, A. Frery","doi":"10.1109/LGRS.2017.2778749","DOIUrl":"https://doi.org/10.1109/LGRS.2017.2778749","url":null,"abstract":"In this letter, we propose a novel technique for obtaining scattering components from polarimetric synthetic aperture radar (PolSAR) data using the geodesic distance on the unit sphere. This geodesic distance is obtained between an elementary target and the observed Kennaugh matrix, and it is further utilized to compute a similarity measure between scattering mechanisms. The normalized similarity measure for each elementary target is then modulated with the total scattering power (Span). This measure is used to categorize pixels into three categories, i.e., odd-bounce, double-bounce, and volume, depending on which of the above scattering mechanisms dominate. Then the maximum likelihood classifier of Lee et al. based on the complex Wishart distribution is iteratively used for each category. Dominant scattering mechanisms are thus preserved in this classification scheme. We show results for L-band AIRSAR and ALOS-2 data sets acquired over San Francisco and Mumbai, respectively. The scattering mechanisms are better preserved using the proposed methodology than the unsupervised classification results using the Freeman–Durden scattering powers on an orientation angle corrected PolSAR image. Furthermore: 1) the scattering similarity is a completely nonnegative quantity unlike the negative powers that might occur in double-bounce and odd-bounce scattering component under Freeman–Durden decomposition and 2) the methodology can be extended to more canonical targets as well as for bistatic scattering.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"15 1","pages":"151-155"},"PeriodicalIF":4.8,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2017.2778749","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44055107","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: 33

A Band-Weighted Support Vector Machine Method for Hyperspectral Imagery Classification 基于波段加权支持向量机的高光谱图像分类方法

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2017-10-01 DOI: 10.1109/LGRS.2017.2729940

Weiwei Sun, Chun Liu, Yan Xu, Long Tian, Weiyue Li

引用次数: 22

Spectrum Width Estimation Using Matched Autocorrelations 使用匹配自相关的频谱宽度估计

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2017-10-01 DOI: 10.1109/LGRS.2017.2726898

D. Warde, S. Torres

引用次数: 2

Removal of Co-Frequency Powerline Harmonics From Multichannel Surface NMR Data 从多通道表面核磁共振数据中去除共频电力线谐波

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2017-09-03 DOI: 10.3997/2214-4609.201702040

Lichao Liu, D. Grombacher, E. Auken, J. Larsen

{"title":"Removal of Co-Frequency Powerline Harmonics From Multichannel Surface NMR Data","authors":"Lichao Liu, D. Grombacher, E. Auken, J. Larsen","doi":"10.3997/2214-4609.201702040","DOIUrl":"https://doi.org/10.3997/2214-4609.201702040","url":null,"abstract":"Powerline harmonics are often the primary noise source in surface nuclear magnetic resonance (NMR) measurements. State-of-the-art techniques, such as notch filtering, Wiener filtering, and model-based subtraction, have been demonstrated to greatly mitigate powerline harmonic noise, but these approaches break down when one of the powerline harmonics has a frequency close to or coincident with the Larmor frequency $f_{L}$ , referred to as a co-frequency harmonic. We propose a hybrid scheme where model-based subtraction of powerline harmonics is coupled with data from a synchronous reference coil to specifically subtract the co-frequency harmonic component. In standard model-based subtraction of powerline harmonics, a sinusoidal model of all harmonic components is fit to the data and subtracted. In the new approach, the amplitude and phase of the co-frequency harmonic are determined by a sinusoidal model fit to the synchronous noise-only data recorded in a reference coil. From the reference coil co-frequency model, the co-frequency harmonic in the primary coil is estimated using relationships between the amplitude and phase of the co-frequency harmonic in the two coils established during noise-only segments. By utilizing data from the reference coil to model the co-frequency harmonic, accidental fitting of the surface NMR signal is avoided. We investigate the efficiency of the method using a synthetic surface NMR signal embedded in noise-only data recorded in Denmark. Our results demonstrate that the co-frequency powerline harmonic can be removed efficiently without distorting the surface NMR signal and the new method performs better than standard methods.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"15 1","pages":"53-57"},"PeriodicalIF":4.8,"publicationDate":"2017-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46781783","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: 19

Sensitivity of NDVI-Based Spatial Downscaling Technique of Coarse Precipitation to Some Mediterranean Bioclimatic Stages 基于ndvi的粗降水空间降尺度技术对地中海部分生物气候阶段的敏感性

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2017-09-01 DOI: 10.1109/LGRS.2017.2720166

Hicham Ezzine, A. Bouziane, D. Ouazar, M. Hasnaoui

引用次数: 1

Feature-Fused SAR Target Discrimination Using Multiple Convolutional Neural Networks 基于多卷积神经网络的特征融合SAR目标识别

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2017-08-29 DOI: 10.1109/LGRS.2017.2729159

Ning Wang, Yinghua Wang, Hongwei Liu, Qunsheng Zuo, Jinglu He

引用次数: 38

Accurate Insect Orientation Extraction Based on Polarization Scattering Matrix Estimation 基于偏振散射矩阵估计的昆虫定向精确提取

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2017-08-17 DOI: 10.1109/LGRS.2017.2733719

C. Hu, R. Wang, C. Liu, T. Zhang, W. Li

引用次数: 21

Structure Preserving Transfer Learning for Unsupervised Hyperspectral Image Classification 无监督高光谱图像分类的结构保留迁移学习

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2017-08-15 DOI: 10.1109/LGRS.2017.2723763

Jianzhe Lin, Chen He, Z. J. Wang, Shuying Li

{"title":"Structure Preserving Transfer Learning for Unsupervised Hyperspectral Image Classification","authors":"Jianzhe Lin, Chen He, Z. J. Wang, Shuying Li","doi":"10.1109/LGRS.2017.2723763","DOIUrl":"https://doi.org/10.1109/LGRS.2017.2723763","url":null,"abstract":"Recent advances on remote sensing techniques allow easier access to imaging spectrometer data. Manually labeling and processing of such collected hyperspectral images (HSIs) with a vast quantities of samples and a large number of bands is labor and time consuming. To relieve these manual processes, machine learning based HSI processing methods have attracted increasing research attention. A major assumption in many machine learning problems is that the training and testing data are in the same feature space and follow the same distribution. However, this assumption doesn’t always hold true in many real world problems, especially in certain HSI processing problems with extremely insufficient or even without training samples. In this letter, we present a transfer learning framework to address this unsupervised challenge (i.e., without training samples in the target domain), by making the following three main contributions: 1) to the best of our knowledge, this is the first time for transfer learning framework to be used for the classification of totally unknown target HSI data with no training samples; 2) the characteristics of HSI are learned on dual spaces to exploit its structure knowledge to better label HSI samples; and 3) two specific new scenarios suitable for transfer learning are investigated. Experimental results on several real world HSIs support the superiority of the proposed work.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"14 1","pages":"1656-1660"},"PeriodicalIF":4.8,"publicationDate":"2017-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2017.2723763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43733146","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: 28