IEEE Geoscience and Remote Sensing Letters最新文献

英文中文

Comprehensive microRNA-seq transcriptomic profiling across 11 organs, 4 ages, and 2 sexes of Fischer 344 rats. 对 Fischer 344 大鼠的 11 个器官、4 个年龄段和 2 种性别进行全面的 microRNA-seq 转录组分析。

IF 5.8 3区地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Geoscience and Remote Sensing Letters

Pub Date : 2022-05-12 DOI: 10.1038/s41597-022-01285-7

Xintong Yao, Shanyue Sun, Yi Zi, Yaqing Liu, Jingcheng Yang, Luyao Ren, Guangchun Chen, Zehui Cao, Wanwan Hou, Yueqiang Song, Jun Shang, He Jiang, Zhihui Li, Haiyan Wang, Peipei Zhang, Leming Shi, Quan-Zhen Li, Ying Yu, Yuanting Zheng

Rat is one of the most widely-used models in chemical safety evaluation and biomedical research. However, the knowledge about its microRNA (miRNA) expression patterns across multiple organs and various developmental stages is still limited. Here, we constructed a comprehensive rat miRNA expression BodyMap using a diverse collection of 320 RNA samples from 11 organs of both sexes of juvenile, adolescent, adult and aged Fischer 344 rats with four biological replicates per group. Following the Illumina TruSeq Small RNA protocol, an average of 5.1 million 50 bp single-end reads was generated per sample, yielding a total of 1.6 billion reads. The quality of the resulting miRNA-seq data was deemed to be high from raw sequences, mapped sequences, and biological reproducibility. Importantly, aliquots of the same RNA samples have previously been used to construct the mRNA BodyMap. The currently presented miRNA-seq dataset along with the existing mRNA-seq dataset from the same RNA samples provides a unique resource for studying the expression characteristics of existing and novel miRNAs, and for integrative analysis of miRNA-mRNA interactions, thereby facilitating better utilization of rats for biomarker discovery.

大鼠是化学安全性评估和生物医学研究中使用最广泛的模型之一。然而，人们对其多个器官和不同发育阶段的 microRNA (miRNA) 表达模式的了解仍然有限。在这里，我们利用从幼年、青春期、成年和老龄 Fischer 344 大鼠的 11 个雌雄器官收集的 320 份 RNA 样本，构建了一个全面的大鼠 miRNA 表达 BodyMap，每组有四个生物重复。按照 Illumina TruSeq Small RNA 协议，每个样本平均产生 510 万个 50 bp 单端读数，总共产生 16 亿个读数。从原始序列、映射序列和生物重现性来看，所产生的 miRNA-seq 数据质量很高。重要的是，相同 RNA 样本的等分试样以前曾用于构建 mRNA BodyMap。目前展示的 miRNA-seq 数据集和来自相同 RNA 样本的现有 mRNA-seq 数据集为研究现有和新型 miRNA 的表达特征以及 miRNA-mRNA 相互作用的综合分析提供了独特的资源，从而有助于更好地利用大鼠发现生物标记物。

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引用次数: 0

Frequently used therapeutic antimicrobials and their resistance patterns on Staphylococcus aureus and Escherichia coli in mastitis affected lactating cows. 受乳腺炎影响的泌乳奶牛中金黄色葡萄球菌和大肠杆菌的常用治疗抗菌药及其耐药性模式。

IF 2.8 3区地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Geoscience and Remote Sensing Letters

Pub Date : 2022-02-28 eCollection Date: 2022-01-01 DOI: 10.1080/23144599.2022.2038494

Eaftekhar Ahmed Rana, Md Abul Fazal, Mohammad Abdul Alim

Mastitis is one of the most frequent and costly production diseases of dairy cattle. It is frequently treated with broad-spectrum antimicrobials. The objectives of this work were to investigate the prevalence of Staphylococcus aureus and Escherichia coli, find out the antimicrobials used in mastitis treatment, and explore the antimicrobial resistance profile including detection of resistance genes. Bacterial species and antimicrobial resistance genes were confirmed by the polymerase-chain reaction. A total of 450 cows were screened, where 23 (5.11%) and 173 (38.44%) were affected with clinical and sub-clinical mastitis, respectively. The prevalence of S. aureus was 39.13% (n = 9) and 47.97%(n = 83) while, E. coli was 30.43% (n = 7) and 15.60% (n = 27) in clinical and sub-clinical mastitis affected cows, respectively. The highest antimicrobials used for mastitis treatment were ciprofloxacin (83.34%), amoxycillin (80%) and ceftriaxone (76.67%). More than, 70% of S. aureus showed resistance against ampicillin, oxacillin, and tetracycline and more than 60% of E. coli exhibited resistance against oxacillin and sulfamethoxazole-trimethoprim. Selected antimicrobial resistance genes (mecA, tetK, tetL, tetM, tetA, tetB, tetC, sul1, sul2 and sul3) were identified from S. aureus and E. coli. Surprisingly, 7 (7.61%) S. aureus carried the mecA gene and were confirmed as methicillin-resistant S. aureus (MRSA). The most prevalent resistance genes were tetK 18 (19.57%) and tetL 13 (14.13%) for S. aureus, whereas sul1 16 (47.06%), tetA 12 (35.29%), sul2 11 (32.35%) and tetB 7 (20.59%) were the most common resistance genes in E. coli. Indiscriminate use of antimicrobials and the presence of multidrug-resistant bacteria suggest a potential threat to public health.

乳腺炎是奶牛生产中最常见、最昂贵的疾病之一。通常使用广谱抗菌药物进行治疗。这项工作的目的是调查金黄色葡萄球菌和大肠杆菌的流行情况，找出用于治疗乳腺炎的抗菌药物，并探索抗菌药物的耐药性概况，包括耐药基因的检测。细菌种类和抗菌药耐药性基因通过聚合酶链反应进行确认。共筛查了 450 头奶牛，其中 23 头（5.11%）和 173 头（38.44%）分别患有临床和亚临床乳腺炎。在临床和亚临床乳腺炎奶牛中，金黄色葡萄球菌的感染率分别为 39.13%（9 头）和 47.97%（83 头），而大肠杆菌的感染率分别为 30.43%（7 头）和 15.60%（27 头）。治疗乳腺炎使用最多的抗菌药是环丙沙星（83.34%）、阿莫西林（80%）和头孢曲松（76.67%）。超过 70% 的金黄色葡萄球菌对氨苄西林、氧西林和四环素产生耐药性，超过 60% 的大肠杆菌对氧西林和磺胺甲噁唑-三甲氧苄青霉素产生耐药性。从金黄色葡萄球菌和大肠杆菌中发现了部分抗菌药耐药基因（mecA、tetK、tetL、tetM、tetA、tetB、tetC、sul1、sul2 和 sul3）。令人惊讶的是，7 个（7.61%）金黄色葡萄球菌携带 mecA 基因，并被确认为耐甲氧西林金黄色葡萄球菌（MRSA）。金黄色葡萄球菌最常见的耐药基因是 tetK 18（19.57%）和 tetL 13（14.13%），而大肠杆菌最常见的耐药基因是 sul1 16（47.06%）、tetA 12（35.29%）、sul2 11（32.35%）和 tetB 7（20.59%）。抗菌素的滥用和耐多药细菌的存在对公众健康构成了潜在威胁。

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引用次数: 0

Ground-Based Remote Sensing of Total Columnar CO2, CH4, and CO Using EM27/SUN FTIR Spectrometer at a Suburban Location (Shadnagar) in India and Validation of Sentinel-5P/TROPOMI 在印度郊区（沙德纳格尔）使用EM27/SUN FTIR光谱仪对总柱状CO2、CH4和CO进行地面遥感，并验证Sentinel-5P/TROPOMI

IF 4.8 3区地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Geoscience and Remote Sensing Letters

Pub Date : 2022-02-22 DOI: 10.36227/techrxiv.19137242.v1

Vijay Kumar Sagar, Mahesh Pathakoti, Mahalakshmi D.V., Rajan K.S., S. M.V.R., F. Hase, D. Dubravica, M. Sha

Greenhouse gases (GHGs) play an important role in controlling local air pollution as well as climate change. In this study, we retrieved column-averaged dry-air (

$X$

) mole fractions of carbon dioxide (CO₂), methane (CH₄), and carbon monoxide (CO) using a ground-based EM27/SUN Fourier transform infrared spectrometer (FTIR). The EM27/SUN spectrometers are widely in use in the COllaborative Carbon Column Observing Network (COCCON). The PROFFAST software provided by COCCON has been used to analyze the measured atmospheric solar absorption spectra. In this letter, the diurnal variation and the time series of daily averaged

$X$

CO₂,

$X$

CH₄, and

$X$

CO covering the period from December 2020 to May 2021 are analyzed. The maximum values of

$X$

CO₂,

$X$

CH₄, and

$X$

CO are observed to be 420.57 ppm, 1.93 ppm, and 170.40 ppb, respectively. Less diurnal but clear seasonal changes are observed during the study period.

$X$

CH₄ and

$X$

CO from the Sentinel-5Precursor (S5P)/TROPOspheric Monitoring Instrument (TROPOMI) are compared against the EM27/SUN retrievals. The correlation coefficient for the EM27/SUN retrieved

$X$

CH₄ and

$X$

CO, with the S5P/TROPOMI products, are 0.75 and 0.94, respectively.

温室气体在控制当地空气污染和气候变化方面发挥着重要作用。在这项研究中，我们使用地面EM27/SUN傅里叶变换红外光谱仪（FTIR）检索了二氧化碳（CO2）、甲烷（CH4）和一氧化碳（CO）的柱平均干空气（$X$）摩尔分数。EM27/SUN光谱仪广泛用于实验室碳柱观测网络（COCCON）。利用COCCON提供的PROFFAST软件对实测的大气太阳吸收光谱进行了分析。在这封信中，分析了2020年12月至2021年5月期间日均$X$CO2、$X$CH4和$X$CO的日变化和时间序列。观测到$X$CO2、$X$CH4和$X$CO的最大值分别为420.57 ppm、1.93 ppm和170.40 ppb。在研究期间，观察到的昼夜变化较少，但季节变化明显$将Sentinel-5前体（S5P）/TROPOSphere监测仪器（TROPOMI）的X$CH4和$X$CO与EM27/SUN反演结果进行比较。EM27/SUN回收的$X$CH4和$X$CO与S5P/TROPOMI产品的相关系数分别为0.75和0.94。

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引用次数: 2

SAR Coregistration by Robust Selection of Extended Targets and Iterative Outlier Cancellation 基于扩展目标鲁棒选择和迭代离群值抵消的SAR共配准

IF 4.8 3区地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Geoscience and Remote Sensing Letters

Pub Date : 2022-01-01 DOI: 10.1109/lgrs.2021.3132661

L. Pallotta, G. Giunta, C. Clemente, J. Soraghan

This letter extends the constrained least-squares (CLS) optimization method developed to coregister multitemporal synthetic aperture radar (SAR) images affected by a joint rotation effect and range/azimuth shifts enforcing the absence of zooming effects. To take advantage of the structural information extracted from the scene, the method starts with a detection stage that identifies extended targets/areas in the images. The selected tie-points allow the CLS problem to be reformulated to find its (initial) solution based on a robust subset of image blocks. Then, the mean square error (MSE) of each equation evaluated from the initial solution allows to implement an iterative cancellation procedure to further skim the CLS equation set. The effectiveness of the proposed procedure is validated on real SAR data in comparison with the standard CLS.

这封信扩展了约束最小二乘(CLS)优化方法，该方法开发用于受联合旋转效应和距离/方位角位移影响的多时相合成孔径雷达(SAR)图像的共配准，从而强制缺少缩放效应。为了利用从场景中提取的结构信息，该方法从识别图像中扩展目标/区域的检测阶段开始。所选的结合点允许重新定义CLS问题，以便根据图像块的健壮子集找到其(初始)解决方案。然后，从初始解评估的每个方程的均方误差(MSE)允许实现迭代消去程序，以进一步撇去CLS方程集。通过与标准CLS的比较，在实际SAR数据上验证了该方法的有效性。

引用次数: 5

SAR Target Recognition Based on Efficient Fully Convolutional Attention Block CNN 基于高效全卷积注意块CNN的SAR目标识别

IF 4.8 3区地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Geoscience and Remote Sensing Letters

Pub Date : 2022-01-01 DOI: 10.1109/LGRS.2020.3037256

Rui Li, Xiaodan Wang, Jian Wang, Yafei Song, Lei Lei

Attention mechanisms have recently shown strong potential in improving the performance of convolutional neural networks (CNNs). This letter proposes a fully convolutional attention block (FCAB) that can be combined with a CNN to refine important features and suppress unnecessary ones in synthetic aperture radar (SAR) images. The FCAB consists of a channel attention module and a spatial attention module. For the channel attention module, we use average-pooling and max-pooling to learn complementary features, and apply group convolution to aggregate the information of the two types of channels. Global average-pooling is then used to encode the channel-wise importance. For the spatial attention module, the average-pooling and max-pooling along the channel axis are used to generate two spatial feature maps, and then two very lightweight convolutional layers are used to encode the spatial weight map. Experimental results on SAR images demonstrate that our FCAB can focus on important channels and object regions. It uses relatively few parameters and is computationally efficient, while bringing about significant performance gain for SAR recognition.

注意机制最近在提高卷积神经网络(cnn)的性能方面显示出强大的潜力。这封信提出了一种全卷积注意块(FCAB)，它可以与CNN相结合，以精炼合成孔径雷达(SAR)图像中的重要特征并抑制不必要的特征。FCAB由信道注意模块和空间注意模块组成。对于通道关注模块，我们使用平均池化和最大池化学习互补特征，并使用组卷积对两类通道的信息进行聚合。然后使用全局平均池来编码通道重要性。对于空间注意力模块，采用沿通道轴的平均池化和最大池化生成两个空间特征图，然后使用两个非常轻量级的卷积层对空间权重图进行编码。在SAR图像上的实验结果表明，FCAB能够对重要通道和目标区域进行聚焦。它使用相对较少的参数，计算效率高，同时为SAR识别带来显著的性能提升。

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引用次数: 15

CYGNSS Soil Moisture Estimations Based on Quality Control 基于质量控制的CYGNSS土壤水分估算

IF 4.8 3区地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Geoscience and Remote Sensing Letters

Pub Date : 2022-01-01 DOI: 10.1109/lgrs.2021.3119850

F. Tang, Songhua Yan

In this letter, we proposed a method based on cyclone global navigation satellite system (CYGNSS) for improving the accuracy of soil moisture (SM) estimation through the selection of auxiliary data and a four-step quality control. We investigate the impact of elevation and vegetation, as well as evaluating the quality of Doppler delay map (DDM) and different ground terrains. The program adopts the support vector machine (SVM) algorithm, input CYGNSS, and auxiliary data. With the hourly SM data on Wuhan Baoxie site from January 2020 to August 2020 as an example, the effectiveness of quality control was verified. A substantial improvement in correlation coefficient of ~0.46 for average between CYGNSS reflectivity and in situ SM was obtained compared with ~0.03 before quality control, resulting in better SM estimation accuracy compared with that of in situ measurements (from

$R = 0.67$

to 0.87).

本文提出了一种基于气旋全球导航卫星系统(CYGNSS)的土壤湿度估算方法，通过辅助数据的选择和四步质量控制来提高土壤湿度估算的精度。我们研究了海拔和植被对多普勒延迟图(DDM)质量的影响，并对不同地形的多普勒延迟图(DDM)质量进行了评价。程序采用支持向量机(SVM)算法，输入CYGNSS，辅助数据。以武汉宝榭站点2020年1 - 8月逐时SM数据为例，验证了质量控制的有效性。与质量控制前的~0.03相比，CYGNSS反射率与原位SM之间的平均相关系数显著提高~0.46，从而使SM估计精度优于原位测量(从$R = 0.67$到0.87 $)。

引用次数: 1

Ship Detection Method Based on Scattering Contribution for PolSAR Image 基于散射贡献的PolSAR图像舰船检测方法

IF 4.8 3区地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Geoscience and Remote Sensing Letters

Pub Date : 2022-01-01 DOI: 10.1109/lgrs.2021.3138796

Xueli Pan, Zhenhua Wu, Lixia Yang, Zhixiang Huang

Due to the differentiation of polarimetric scattering mechanisms between ships and sea surface, designing the ship detection method in polarimetric synthetic aperture radar (PolSAR) is a potential promising technique and has been paid extensive attention. The complexity of sea clutter and weak scattering of small ships result in a great challenge for high-precision ship detection. In this letter, we investigate the scattering mechanisms of ships to improve the detection performance and propose a novel ship detection method based on the principal contribution of scattering mechanisms. First, the seven-component model-based decomposition (SCMD) is used to analyze the scattering mechanisms of ships. Second, the primary scattering contribution and local contrast (SCLC) mechanism are used to enhance ships, especially small ships. Finally, the threshold segmentation is used to realize the extraction of ships. Experimental results by real PolSAR data not only verify the rationality and effectiveness of the constructed detection metric but also show the clear superiority of the proposed detection method, which can encourage further application of polarimetric scattering mechanisms in ship detection.

由于船舶与海面之间的极化散射机制存在差异，设计极化合成孔径雷达(PolSAR)中的船舶探测方法是一项极具发展前景的技术，受到了广泛的关注。海杂波的复杂性和小型船舶的弱散射给高精度船舶探测带来了很大的挑战。本文研究了舰船的散射机制以提高探测性能，并提出了一种基于散射机制主贡献的舰船探测新方法。首先，采用基于七分量模型的分解(SCMD)方法对舰船散射机理进行分析。其次，利用初级散射贡献和局部对比度(SCLC)机制对船舶特别是小型船舶进行增强。最后，利用阈值分割实现船舶的提取。真实PolSAR数据的实验结果不仅验证了所构建的检测度量的合理性和有效性，而且表明了所提检测方法的明显优越性，为极化散射机制在舰船检测中的进一步应用提供了理论依据。

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引用次数: 3

Meta Self-Supervised Learning for Distribution Shifted Few-Shot Scene Classification 分布移位少镜头场景分类的元自监督学习

IF 4.8 3区地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Geoscience and Remote Sensing Letters

Pub Date : 2022-01-01 DOI: 10.1109/lgrs.2022.3174277

Tengfei Gong, Xiangtao Zheng, Xiaoqiang Lu

Few-shot classification tries to recognize novel remote sensing image categories with a few shot samples. However, current methods assume that the test dataset shares the same domain with the labeled training dataset where prior knowledge is learned. It is infeasible to collect a training dataset for each domain, since remote sensing images may come from various domains. Exploiting the existing labeled dataset from another domain (source domain) to help the target dataset (target domain) classification would be efficient. In this letter, both meta-learning and self-supervised learning are jointly conducted for few-shot classification. Specifically, meta-learning is executed over a pretrained network for few-shot classification. Furthermore, self-supervised learning is exploited to fit the target domain distribution by training on unlabeled target domain images. Experiments are conducted on NWPU, EuroSAT and Merced datasets to validate the effectiveness.

少照分类是利用少量的遥感影像样本来识别新的遥感影像类别。然而，目前的方法假设测试数据集与学习先验知识的标记训练数据集共享相同的域。由于遥感图像可能来自不同的领域，因此不可能为每个领域收集一个训练数据集。利用其他领域(源领域)的已有标记数据集来帮助目标数据集(目标领域)分类将是有效的。在这封信中，我们将元学习和自监督学习结合起来进行few-shot分类。具体来说，元学习是在预训练的网络上执行的，用于少量分类。此外，通过对未标记的目标域图像进行训练，利用自监督学习拟合目标域分布。在NWPU、EuroSAT和Merced数据集上进行了实验，验证了该方法的有效性。

引用次数: 5

Multiscale Context Aggregation Network for Building Change Detection Using High Resolution Remote Sensing Images 基于多尺度上下文聚合网络的高分辨率遥感影像建筑变化检测

IF 4.8 3区地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Geoscience and Remote Sensing Letters

Pub Date : 2022-01-01 DOI: 10.1109/LGRS.2021.3121094

J. Dong, Wufan Zhao, Shuai Wang

The existing methods of building change detection (CD) using remote sensing (RS) images are still deficient in handling scale variation and class imbalance problems, indicating a decrease in the robustness of small-object detection and pseudo-change information. Thus, a novel building CD framework called the multiscale context aggregation network (MSCANet) is proposed. The high-resolution network is integrated into the feature extracting stage to maintain high-resolution representations throughout the whole process. Then, multiscale context information is aggregated using a scale-aware feature pyramid module (FPM). Recognition performance can be improved from discriminant feature representation learning by using a channel–spatial attention module. Furthermore, a class-balanced loss is proposed to reduce the impact of class imbalance in long-tail datasets. Experimental results from using the LEVIR-CD and SZTAKI AirChange benchmark datasets prove the superiority of the MSCANet over the other baseline methods, with improved maximum F1 scores of 5.28 and 8.47, respectively.

现有的基于遥感影像的建筑变化检测方法在处理尺度变化和类不平衡问题方面存在不足，小目标检测和伪变化信息的鲁棒性较差。为此，本文提出了一种新型的多尺度上下文聚合网络(MSCANet)。将高分辨率网络集成到特征提取阶段，在整个过程中保持高分辨率表示。然后，使用尺度感知特征金字塔模块(FPM)对多尺度上下文信息进行聚合。通过使用通道-空间注意模块，可以提高识别性能。在此基础上，提出了一种类平衡损失方法来减少长尾数据集中类不平衡的影响。使用levirc - cd和SZTAKI AirChange基准数据集的实验结果证明了MSCANet比其他基准方法的优越性，最大F1分数分别提高了5.28和8.47。

引用次数: 8

Graph Domain Adversarial Network With Dual-Weighted Pseudo-Label Loss for Hyperspectral Image Classification 基于双加权伪标签损失的图域对抗网络用于高光谱图像分类

IF 4.8 3区地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Geoscience and Remote Sensing Letters

Pub Date : 2022-01-01 DOI: 10.1109/lgrs.2021.3135310

Yi Kong, Xuesong Wang, Yuhu Cheng, Yangchi Chen, C. L. P. Chen

A hyperspectral image (HSI) classification method named graph domain adversarial network with dual-weighted pseudo-label loss (GDAN-DWPL) is proposed in this letter. First, in order to extract more discriminative features, GDAN is applied to the transfer task of HSI. Then, a more reliable spectral–spatial graph is constructed by comprehensively utilizing the abundant spectral features and spatial contextual information. Finally, due to the misalignment of probability distribution on class-level caused by inaccurate pseudo-labels of target domain, a dual-weighted pseudo-label loss is proposed from the perspective of spatiality and confidence. By assigning larger weights to more reliable pixels and eliminating pixels with false pseudo-labels, the negative impact on learning process of prediction model can be reduced. Experimental results on four real HSI datasets show the superiority of GDAN-DWPL.

提出了一种基于双加权伪标签损失的图域对抗网络(GDAN-DWPL)的高光谱图像分类方法。首先，为了提取更多的判别特征，将GDAN应用于恒生指数的传递任务。然后，综合利用丰富的光谱特征和空间背景信息，构建更可靠的光谱-空间图。最后，针对目标域伪标签不准确导致类水平概率分布不一致的问题，从空间性和置信度的角度提出了双加权伪标签损失算法。通过对更可靠的像素分配更大的权重，并消除带有虚假伪标签的像素，可以减少对预测模型学习过程的负面影响。在4个真实HSI数据集上的实验结果表明了GDAN-DWPL的优越性。

引用次数: 5

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