IEEE Geoscience and Remote Sensing Letters最新文献

{"title":"OYOLO: An Optimized YOLO Method for Complex Objects in Remote Sensing Image Detection","authors":"Tianyi Xie, Wen Han, Sheng Xu","doi":"10.1109/lgrs.2023.3312168","DOIUrl":"https://doi.org/10.1109/lgrs.2023.3312168","url":null,"abstract":"","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"6 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62508894","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}

{"title":"Maritime Radar Target Detection in Sea Clutter Based on CNN With Dual-Perspective Attention","authors":"Jingang Wang, Songbin Li","doi":"10.1109/LGRS.2022.3230443","DOIUrl":"https://doi.org/10.1109/LGRS.2022.3230443","url":null,"abstract":"Radar-based maritime target detection plays an important role in ocean monitoring. Considering the practical application, pulse-compression radar is widely used in terms of civilian offshore surface target detection. The existence of sea clutter will greatly interfere the detection performance of pulse-compression radar. This leads to the low detection performance of traditional algorithms like constant false alarm rate (CFAR). Deep learning methods have made strides in many fields recently, such as natural language processing and speech recognition. Inspired by this idea, we propose a maritime radar target detection method in sea clutter based on convolution neural network (CNN) and dual-perspective attention (DPA). The proposed method first encodes the radar echo in high-dimensional space and then extracts the correlation features from the global and local perspectives through the attention mechanism. We deployed the X-band pulse-compression radar on the coast of Hainan, China, and collected a lot of measured data. Experimental results demonstrate that the detection performance of our method outperforms the traditional CFAR methods and the latest deep learning-based methods. In the measured dataset, our proposed method can reach a detection probability of 93.59% under a false alarm rate (FAR) of $1e-3$ , reaching the practical application level.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"20 1","pages":"1-5"},"PeriodicalIF":4.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62499431","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}

{"title":"Comprehensive microRNA-seq transcriptomic profiling across 11 organs, 4 ages, and 2 sexes of Fischer 344 rats.","authors":"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","doi":"10.1038/s41597-022-01285-7","DOIUrl":"10.1038/s41597-022-01285-7","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"17 1","pages":"201"},"PeriodicalIF":5.8,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9098487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57531317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frequently used therapeutic antimicrobials and their resistance patterns on <i>Staphylococcus aureus</i> and <i>Escherichia coli</i> in mastitis affected lactating cows.","authors":"Eaftekhar Ahmed Rana, Md Abul Fazal, Mohammad Abdul Alim","doi":"10.1080/23144599.2022.2038494","DOIUrl":"10.1080/23144599.2022.2038494","url":null,"abstract":"<p><p>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 <i>Staphylococcus aureus</i> and <i>Escherichia coli</i>, 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 <i>S. aureus</i> was 39.13% (n = 9) and 47.97%(n = 83) while, <i>E. coli</i> 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 <i>S. aureus</i> showed resistance against ampicillin, oxacillin, and tetracycline and more than 60% of <i>E. coli</i> exhibited resistance against oxacillin and sulfamethoxazole-trimethoprim. Selected antimicrobial resistance genes (<i>mec</i>A, <i>tet</i>K, <i>tet</i>L, <i>tet</i>M, <i>tet</i>A, <i>tet</i>B, <i>tet</i>C, <i>sul</i>1, <i>sul</i>2 and <i>sul</i>3) were identified from <i>S. aureus</i> and <i>E. coli</i>. Surprisingly, 7 (7.61%) <i>S. aureus</i> carried the <i>mec</i>A gene and were confirmed as methicillin-resistant <i>S. aureus</i> (MRSA). The most prevalent resistance genes were <i>tet</i>K 18 (19.57%) and <i>tet</i>L 13 (14.13%) for <i>S. aureus</i>, whereas <i>sul</i>1 16 (47.06%), <i>tet</i>A 12 (35.29%), <i>sul</i>2 11 (32.35%) and <i>tet</i>B 7 (20.59%) were the most common resistance genes in <i>E. coli</i>. Indiscriminate use of antimicrobials and the presence of multidrug-resistant bacteria suggest a potential threat to public health.</p>","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1-10"},"PeriodicalIF":2.8,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8890510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78428113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"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","authors":"Vijay Kumar Sagar, Mahesh Pathakoti, Mahalakshmi D.V., Rajan K.S., S. M.V.R., F. Hase, D. Dubravica, M. Sha","doi":"10.36227/techrxiv.19137242.v1","DOIUrl":"https://doi.org/10.36227/techrxiv.19137242.v1","url":null,"abstract":"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 (<inline-formula> <tex-math notation=\"LaTeX\">$X$ </tex-math></inline-formula>) 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 <inline-formula> <tex-math notation=\"LaTeX\">$X$ </tex-math></inline-formula>CO₂, <inline-formula> <tex-math notation=\"LaTeX\">$X$ </tex-math></inline-formula>CH₄, and <inline-formula> <tex-math notation=\"LaTeX\">$X$ </tex-math></inline-formula>CO covering the period from December 2020 to May 2021 are analyzed. The maximum values of <inline-formula> <tex-math notation=\"LaTeX\">$X$ </tex-math></inline-formula>CO₂, <inline-formula> <tex-math notation=\"LaTeX\">$X$ </tex-math></inline-formula>CH₄, and <inline-formula> <tex-math notation=\"LaTeX\">$X$ </tex-math></inline-formula>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. <inline-formula> <tex-math notation=\"LaTeX\">$X$ </tex-math></inline-formula>CH₄ and <inline-formula> <tex-math notation=\"LaTeX\">$X$ </tex-math></inline-formula>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 <inline-formula> <tex-math notation=\"LaTeX\">$X$ </tex-math></inline-formula>CH₄ and <inline-formula> <tex-math notation=\"LaTeX\">$X$ </tex-math></inline-formula>CO, with the S5P/TROPOMI products, are 0.75 and 0.94, respectively.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":" ","pages":"1-5"},"PeriodicalIF":4.8,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44352659","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}

{"title":"SAR Coregistration by Robust Selection of Extended Targets and Iterative Outlier Cancellation","authors":"L. Pallotta, G. Giunta, C. Clemente, J. Soraghan","doi":"10.1109/lgrs.2021.3132661","DOIUrl":"https://doi.org/10.1109/lgrs.2021.3132661","url":null,"abstract":"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.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"19 1","pages":"1-5"},"PeriodicalIF":4.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62484488","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}

{"title":"SAR Target Recognition Based on Efficient Fully Convolutional Attention Block CNN","authors":"Rui Li, Xiaodan Wang, Jian Wang, Yafei Song, Lei Lei","doi":"10.1109/LGRS.2020.3037256","DOIUrl":"https://doi.org/10.1109/LGRS.2020.3037256","url":null,"abstract":"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.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"19 1","pages":"1-5"},"PeriodicalIF":4.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2020.3037256","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62474993","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}

{"title":"CYGNSS Soil Moisture Estimations Based on Quality Control","authors":"F. Tang, Songhua Yan","doi":"10.1109/lgrs.2021.3119850","DOIUrl":"https://doi.org/10.1109/lgrs.2021.3119850","url":null,"abstract":"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 <italic>in situ</italic> SM was obtained compared with ~0.03 before quality control, resulting in better SM estimation accuracy compared with that of <italic>in situ</italic> measurements (from <inline-formula> <tex-math notation=\"LaTeX\">$R = 0.67$ </tex-math></inline-formula> to 0.87).","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"19 1","pages":"1-5"},"PeriodicalIF":4.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62482543","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}

{"title":"Ship Detection Method Based on Scattering Contribution for PolSAR Image","authors":"Xueli Pan, Zhenhua Wu, Lixia Yang, Zhixiang Huang","doi":"10.1109/lgrs.2021.3138796","DOIUrl":"https://doi.org/10.1109/lgrs.2021.3138796","url":null,"abstract":"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.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"19 1","pages":"1-5"},"PeriodicalIF":4.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62485860","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}

{"title":"Meta Self-Supervised Learning for Distribution Shifted Few-Shot Scene Classification","authors":"Tengfei Gong, Xiangtao Zheng, Xiaoqiang Lu","doi":"10.1109/lgrs.2022.3174277","DOIUrl":"https://doi.org/10.1109/lgrs.2022.3174277","url":null,"abstract":"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.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"19 1","pages":"1-5"},"PeriodicalIF":4.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62490567","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}