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

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

GIS-Supervised Building Extraction With Label Noise-Adaptive Fully Convolutional Neural Network 基于标签噪声自适应全卷积神经网络的GIS监控建筑提取

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-01-30 DOI: 10.1109/LGRS.2019.2963065

Zenghui Zhang, Weiwei Guo, Mingjie Li, Wenxian Yu

{"title":"GIS-Supervised Building Extraction With Label Noise-Adaptive Fully Convolutional Neural Network","authors":"Zenghui Zhang, Weiwei Guo, Mingjie Li, Wenxian Yu","doi":"10.1109/LGRS.2019.2963065","DOIUrl":"https://doi.org/10.1109/LGRS.2019.2963065","url":null,"abstract":"Automatic building extraction from aerial or satellite images is a dense pixel prediction task for many applications. It demands a large number of clean label data to train a deep neural network for building extraction. But it is labor expensive to collect such pixel-wise annotated data manually. Fortunately, the building footprint data of geographic information system (GIS) maps provide a cheap way of generating building label data, but these labels are imperfect due to misalignment between the GIS maps and images. In this letter, we consider the task of learning a deep neural network to label images pixel-wise from such noisy label data for building extraction. To this end, we propose a general label noise-adaptive (NA) neural network framework consisting of a base network followed by an additional probability transition modular (PTM) which is introduced to capture the relationship between the true label and the noisy label. The parameters of the PTM can be estimated as part of the training process of the whole network by the off-the-shelf backpropagation algorithm. We conduct experiments on real-world data set to demonstrate that our proposed PTM can better handle noisy labels and improve the performance of convolutional neural networks (CNNs) trained on the noisy label data generated by GIS maps for building extraction. The experimental results indicate that being armed with our proposed PTM for fully CNN, it provides a promising solution to reduce manual annotation effort for the labor-expensive object extraction tasks from remote sensing images.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"17 1","pages":"2135-2139"},"PeriodicalIF":4.8,"publicationDate":"2020-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2019.2963065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46085301","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: 35

Introducing IEEE Collabratec IEEE Collabratec简介

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2019-11-01 DOI: 10.1109/lgrs.2019.2950117

引用次数: 0

Corn Plant Counting Using Deep Learning and UAV Images 利用深度学习和无人机图像进行玉米植株计数

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2019-08-08 DOI: 10.1109/LGRS.2019.2930549

Bruno T. Kitano, C. Mendes, A. R. Geus, Henrique C. Oliveira, Jefferson R. Souza

引用次数: 65

Extreme Learning Machine-Based Heterogeneous Domain Adaptation for Classification of Hyperspectral Images 基于极限学习机的高光谱图像分类异构域自适应

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2019-04-26 DOI: 10.1109/LGRS.2019.2909543

Li Zhou, Li Ma

引用次数: 17

Infrared and Visible Image Fusion Method by Using Hybrid Representation Learning 基于混合表示学习的红外与可见光图像融合方法

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2019-04-24 DOI: 10.1109/LGRS.2019.2907721

G. He, Jiaqi Ji, Dandan Dong, Jun Wang, Jianping Fan

{"title":"Infrared and Visible Image Fusion Method by Using Hybrid Representation Learning","authors":"G. He, Jiaqi Ji, Dandan Dong, Jun Wang, Jianping Fan","doi":"10.1109/LGRS.2019.2907721","DOIUrl":"https://doi.org/10.1109/LGRS.2019.2907721","url":null,"abstract":"For remote sensing image fusion, infrared and visible images have very different brightness due to their disparate imaging mechanisms, the result of which is that nontarget regions in the infrared image often affect the fusion of details in the visible image. This letter proposes a novel infrared and visible image fusion method basing hybrid representation learning by combining dictionary-learning-based joint sparse representation (JSR) and nonnegative sparse representation (NNSR). In the proposed method, different fusion strategies are adopted, respectively, for the mean image, which represents the primary energy information, and for the deaveraged image, which contains important detail features. Since the deaveraged image contains a large amount of high-frequency details information of the source image, JSR is utilized to sparsely and accurately extract the common and innovation features of the deaveraged image, thus, accurately merging high-frequency details in the deaveraged image. Then, the mean image represents low-frequency and overview features of the source image, according to NNSR, mean image is classified well-directed to different feature regions and then fused, respectively. Such proposed method, on the one hand, can eliminate the impact on fusion result suffering from very different brightness causing by different imaging mechanism between infrared and visible image; on the other hand, it can improve the readability and accuracy of the result fusion image. Experimental result shows that, compared with the classical and state-of-the-art fusion methods, the proposed method not only can accurately integrate the infrared target but also has rich background details of the visible image, and the fusion effect is superior.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"16 1","pages":"1796-1800"},"PeriodicalIF":4.8,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2019.2907721","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44449172","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: 11