IEEE Geoscience and Remote Sensing Letters最新文献_第7页

Infrared Small Target Tracking via Gaussian Curvature-Based Compressive Convolution Feature Extraction 基于高斯曲率压缩卷积特征提取的红外小目标跟踪

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-02-12 DOI: 10.1109/LGRS.2021.3051183

Minjie Wan, Xiaobo Ye, Xiaojie Zhang, Yunkai Xu, G. Gu, Qian Chen

{"title":"Infrared Small Target Tracking via Gaussian Curvature-Based Compressive Convolution Feature Extraction","authors":"Minjie Wan, Xiaobo Ye, Xiaojie Zhang, Yunkai Xu, G. Gu, Qian Chen","doi":"10.1109/LGRS.2021.3051183","DOIUrl":"https://doi.org/10.1109/LGRS.2021.3051183","url":null,"abstract":"The precision of infrared (IR) small target tracking is seriously limited due to lack of texture information and interference of background clutter. The key issue of robust tracking is to exploit generic feature representations of IR small targets under different types of background. In this letter, we present a new IR small target tracking method via compressive convolution feature (CCF) extraction. First, a Gaussian curvature-based feature map is calculated to suppress clutters so that the contrast between target and background can be obviously improved. Then, a three-layer compressive convolutional network, which consists of a simple layer, a compressive layer, and a complex layer, is designed to represent each candidate target by a CCF vector. Based on the proposed mechanism of feature extraction, a support vector machine (SVM) classifier with continuous probabilistic output is trained to compute the likelihood probability of each candidate. Finally, the long-term tracking for IR small target is implemented under the framework of the inverse sparse representation-based particle filter. Both qualitative and quantitative experiments based on real IR sequences verify that our method can achieve more satisfactory performances in terms of precision and robustness compared with other typical visual trackers.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"26 1","pages":"1-5"},"PeriodicalIF":4.8,"publicationDate":"2021-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2021.3051183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62476742","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: 15

Evaluation of the Initial Sea Surface Temperature From the HY-2B Scanning Microwave Radiometer HY-2B扫描微波辐射计对海表初始温度的估算

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-01-01 DOI: 10.1109/LGRS.2020.2968635

Lei Zhang, Hong Yu, Zhenzhan Wang, X. Yin, Liang Yang, Hua-dong Du, Bin Li, Y. Wang, Wu Zhou

{"title":"Evaluation of the Initial Sea Surface Temperature From the HY-2B Scanning Microwave Radiometer","authors":"Lei Zhang, Hong Yu, Zhenzhan Wang, X. Yin, Liang Yang, Hua-dong Du, Bin Li, Y. Wang, Wu Zhou","doi":"10.1109/LGRS.2020.2968635","DOIUrl":"https://doi.org/10.1109/LGRS.2020.2968635","url":null,"abstract":"Haiyang-2B (HY-2B) is the second marine dynamic environment satellite of China. Sea surface temperature (SST) products from the scanning microwave radiometer (SMR) onboard HY-2B satellite are evaluated against in situ measurements. Approximately, ten months of data are used for the initial evaluation, from January 15, 2019 to November 15, 2019. The temporal and spatial windows for collocation are 30 min and 25 km, respectively, which produce 450 416 matchup pairs between HY-2B/SMR and in situ SSTs. The statistical comparison of the entire data set shows that the mean bias is −0.13 °C (SMR minus buoy), and the corresponding root-mean-square error (RMSE) is 1.06 °C. Time series of collocations for the SST difference shows that a good agreement is found between HY-2B/SMR and in situ SSTs after June 15, revealing a mean bias and an RMSE of only 0.09 °C and 0.72 °C, respectively. A three-way error analysis is conducted between the SMR, Global Precipitation Measurement Microwave Imager (GMI), and in situ SSTs. Individual standard deviations are found to be 0.41 °C for the GMI SST, 0.15 °C for the in situ SST, and 1.03 °C for the SMR SST. The results indicate that the HY2B/SMR SST products need to be improved during the period from January 15, 2019 to June 15, 2019.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"137-141"},"PeriodicalIF":4.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2020.2968635","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62473216","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: 10

Kirsch Direction Template Despeckling Algorithm of High-Resolution SAR Images-Based on Structural Information Detection 基于结构信息检测的高分辨率SAR图像Kirsch方向模板去斑算法

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-01-01 DOI: 10.1109/LGRS.2020.2966369

S. Hou, Zengguo Sun, Liu Yang, Yunjing Song

{"title":"Kirsch Direction Template Despeckling Algorithm of High-Resolution SAR Images-Based on Structural Information Detection","authors":"S. Hou, Zengguo Sun, Liu Yang, Yunjing Song","doi":"10.1109/LGRS.2020.2966369","DOIUrl":"https://doi.org/10.1109/LGRS.2020.2966369","url":null,"abstract":"In order to overcome the drawback of the traditional Kirsch template despeckling usings fixed windows, an improved Kirsch direction template despeckling algorithm, based on structural information detection, is proposed for high-resolution synthetic aperture radar (SAR) images. First, the point targets are detected and preserved in the current region. Second, the window is enlarged adaptively based on the statistical characteristics of the local region. Finally, the window finally obtained is classified. The averaged filter is directly adopted if the region is homogeneous, or else the Kirsch template filter is used. Combining point target detection, adaptive windowing, and region classification, altogether the proposed algorithm can effectively improve the performance of the traditional Kirsch direction template despeckling. Despeckling experiments on simulated and real high-resolution SAR images demonstrate that the Kirsch direction template despeckling algorithm based on structural information detection can not only sufficiently suppress speckle in homogenous and edge regions, but also effectively preserve point targets and edge information, leading to good despeckling results.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"177-181"},"PeriodicalIF":4.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2020.2966369","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62472413","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: 2

Ship Detection and Direction Finding Based on Time-Frequency Analysis for Compact HF Radar 基于时频分析的小型高频雷达舰船探测与测向

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-01-01 DOI: 10.1109/LGRS.2020.2967387

Jiajia Cai, Hao Zhou, Weimin Huang, B. Wen

引用次数: 28

Object-Oriented Mangrove Species Classification Using Hyperspectral Data and 3-D Siamese Residual Network 基于高光谱数据和三维暹罗残差网络的面向对象红树林物种分类

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-12-01 DOI: 10.1109/LGRS.2019.2962723

Zhi He, Q. Shi, Kai Liu, Jingjing Cao, Wen Zhan, B. Cao

{"title":"Object-Oriented Mangrove Species Classification Using Hyperspectral Data and 3-D Siamese Residual Network","authors":"Zhi He, Q. Shi, Kai Liu, Jingjing Cao, Wen Zhan, B. Cao","doi":"10.1109/LGRS.2019.2962723","DOIUrl":"https://doi.org/10.1109/LGRS.2019.2962723","url":null,"abstract":"Mangrove species classification is of particular importance for coastal conservation and restoration. However, it is challenging to distinguish species-level differences with limited training data. In this letter, we propose an object-oriented classification method for mangrove forests by using the hyperspectral image (HSI) and the 3-D Siamese residual network. First, superpixel segmentation is utilized to obtain objects with various shapes and scales. Second, 3-D patches of each object are extracted from the original HSI, and those patches containing training samples are adopted to pairwise train the network. The 3-D spatial pyramid pooling (3-D-SPP) is added in the network to extract features in multiple scales. Finally, the abstract features of test samples are learned by the trained network, and the labels are determined by the nearest neighbor classifier within the metric space. Experiments on real mangrove hyperspectral data demonstrate the effectiveness of the proposed method in species classification of mangroves.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"17 1","pages":"2150-2154"},"PeriodicalIF":4.8,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2019.2962723","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45575045","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: 13

Linear Spectral Mixing Model-Guided Artificial Bee Colony Method for Endmember Generation 线性谱混合模型引导的人工蜂群末端生成方法

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-12-01 DOI: 10.1109/LGRS.2019.2961502

Mingming Xu, Yan Zhang, Yanguo Fan, Yanlong Chen, Dongmei Song

引用次数: 3

Seismic Impedance Inversion Using Fully Convolutional Residual Network and Transfer Learning 利用全卷积残差网络和传递学习反演地震阻抗

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-12-01 DOI: 10.1109/LGRS.2019.2963106

Bangyu Wu, Delin Meng, Lingling Wang, Naihao Liu, Ying Wang

引用次数: 63

A Novel AMS-DAT Algorithm for Moving Vehicle Detection in a Satellite Video 一种新的AMS-DAT卫星视频中移动车辆检测算法

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-11-09 DOI: 10.1109/lgrs.2020.3034677

Xu Chen, H. Sui, Jian Fang, Mingting Zhou, Chen Wu

{"title":"A Novel AMS-DAT Algorithm for Moving Vehicle Detection in a Satellite Video","authors":"Xu Chen, H. Sui, Jian Fang, Mingting Zhou, Chen Wu","doi":"10.1109/lgrs.2020.3034677","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3034677","url":null,"abstract":"Satellite videos have recently served as a new data source for a wide range of applications in traffic management and military surveillance. Due to its wider coverage, satellite videos show more advantages in large-scale monitoring than ground surveillance videos. However, pseudomotion background and low-resolution targets pose new challenges to moving vehicle detection in satellite videos, resulting in poor performance of conventional target detection methods when applied to satellite videos. To overcome this difficulty, we propose a novel moving vehicle detection approach using adaptive motion separation and difference accumulated trajectory. Specifically, a new indicator is designed to assist adaptive separation of moving targets and background, considering the scale invariance of vehicles in satellite videos. Meanwhile, we offer a vehicle discrimination algorithm based on a differential accumulated trajectory to distinguish the moving vehicles from the pseudomotion background. Experimental results on two satellite video data sets demonstrate that the proposed approach achieves better detection performance over the state-of-the-art moving vehicle detection methods.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"36 1","pages":"1-5"},"PeriodicalIF":4.8,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3034677","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62474385","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

Square-Law Detection of Exponential Targets in Weibull-Distributed Ground Clutter 威布尔分布地杂波中指数目标的平方律检测

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-07-22 DOI: 10.1109/lgrs.2020.3009304

Fernando Darío Almeida García, H. Mora, G. Fraidenraich, J. Filho

{"title":"Square-Law Detection of Exponential Targets in Weibull-Distributed Ground Clutter","authors":"Fernando Darío Almeida García, H. Mora, G. Fraidenraich, J. Filho","doi":"10.1109/lgrs.2020.3009304","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3009304","url":null,"abstract":"Modern radar systems use square-law detectors to search and track fluctuating targets embedded in Weibull-distributed ground clutter. However, the theoretical performance analysis of square-law detectors in the presence of Weibull clutter leads to cumbersome mathematical formulations. Some studies have circumvented this problem by using approximations or mathematical artifacts to simplify calculations. In this work, we derive a closed-form and exact expression for the probability of detection (PD) of a square-law detector in the presence of exponential targets and Weibull-distributed ground clutter, given in terms of the Fox H-function. Unlike previous studies, no approximations nor simplifying assumptions are made throughout our analysis. Furthermore, we derive a fast convergent series for the referred PD by exploiting the orthogonal selection of poles in Cauchy’s residue theorem. In passing, we also obtain closed-form solutions and series representations for the probability density function and the cumulative distribution function of the sum statistics that govern the output of a square-law detector. Numerical results and Monte Carlo simulations corroborate the validity of our expressions.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1956-1960"},"PeriodicalIF":4.8,"publicationDate":"2020-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3009304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46449550","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: 9

On Cokriging, Neural Networks, and Spatial Blind Source Separation for Multivariate Spatial Prediction 多元空间预测的Cokriging、神经网络和空间盲源分离

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-07-01 DOI: 10.1109/LGRS.2020.3011549

C. Muehlmann, K. Nordhausen, Mengxi Yi

引用次数: 9