IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第6页

Land Surface Emissivity Retrieval From Landsat 9 Data in Combination With Land Cover Data and Spectral Library 结合土地覆盖数据和光谱库的Landsat 9数据地表发射率反演

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-21 DOI: 10.1109/LGRS.2025.3601391

Qi Zhang;Yonggang Qian;Kun Li;Qiyao Li;Jianmin Wang;Dacheng Li

{"title":"Land Surface Emissivity Retrieval From Landsat 9 Data in Combination With Land Cover Data and Spectral Library","authors":"Qi Zhang;Yonggang Qian;Kun Li;Qiyao Li;Jianmin Wang;Dacheng Li","doi":"10.1109/LGRS.2025.3601391","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601391","url":null,"abstract":"Land surface emissivity (LSE) is crucial for retrieving land surface temperature (LST) from Landsat 9 TIRS-2 thermal infrared (TIR) data. However, the single-band LSE product (band 10) provided officially is insufficient for the split-window (SW) algorithm requiring dual-band emissivity inputs. This letter proposes a land cover and channel transformed-LSE (LCCT-LSE) method to estimate band 11 LSE and enables LST retrieval using the SW algorithm on Google Earth Engine. Cross-validation with MOD21 LSE products showed that the LCCT-LSE method achieved a mean absolute error (MAE) of 0.004 and a root mean square error (RMSE) of 0.005, outperforming the classification-based method, NDVI threshold method, and vegetation cover vegetation cover-based method (VCM) methods. In situ validation showed SW-retrieved LST attains MAE/RMSE of 1.27/2.13 K, with consistent accuracy across diverse land covers (water: 0.86 K, soil: 1.58 K, desert: 1.71 K, sand: 1.80 K, and vegetation: 0.87 K). A comparison with the official Landsat 9 LST product indicated that the bias of retrieved LST is within 1 K for all land cover classes (cropland, forest, grassland, shrubland, water, barren, and impervious) in Beijing. These results demonstrated that the LCCT-LSE method is capable of estimating the LSE in Landsat 9 band 11 with a reliable and accurate result. This study provides a new insight for LST retrieval from Landsat 9 data.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Tone-Based Flicker Noise Mitigation Technique for Broadband Digital Microwave Radiometers 宽带数字微波辐射计基于音调的闪烁噪声抑制技术

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-20 DOI: 10.1109/LGRS.2025.3596542

Omkar Pradhan;Ahmed Soliman;Alan B. Tanner;Akim Babenko;Pekka Kangaslahti;Shannon T. Brown

{"title":"A Tone-Based Flicker Noise Mitigation Technique for Broadband Digital Microwave Radiometers","authors":"Omkar Pradhan;Ahmed Soliman;Alan B. Tanner;Akim Babenko;Pekka Kangaslahti;Shannon T. Brown","doi":"10.1109/LGRS.2025.3596542","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3596542","url":null,"abstract":"High-frequency microwave radiometers with low-noise amplifier (LNA) front-ends commonly suffer from gain instability, or so-called “flicker” noise. This noise has a <inline-formula> <tex-math>$1/f$ </tex-math></inline-formula> energy spectrum and hence is also commonly referred to as <inline-formula> <tex-math>$1/f$ </tex-math></inline-formula> noise. The effect of this noise on a passive instrument is to degrade its sensitivity and introduce postprocessing calibration errors such as “striping.” In this letter, we present a <inline-formula> <tex-math>$1/f$ </tex-math></inline-formula> noise mitigation technique using a combination of single-frequency tone injection and high spectral resolution digital signal detection. This technique can be used in radiometers with sufficient information redundancy so that a limited portion of the detected signal spectrum can be dedicated to noise mitigation. A key requirement of implementing this technique is application specific integrated circuit (ASIC) or field programmable gate array (FPGA)-based spectral decomposition of the radio-frequency energy. A proof-of-concept hardware setup and signal processing steps required to implement such a technique are presented in this letter. Measurements presented here show a reduction up to <inline-formula> <tex-math>$87~%$ </tex-math></inline-formula> in <inline-formula> <tex-math>$1/f$ </tex-math></inline-formula> noise energy using this technique and are applicable to airborne and ground-based instruments.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-4"},"PeriodicalIF":4.4,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dust Aerosol Optical Centroid Height (AOCH) Over Bright Surface: First Retrieval From TROPOMI Oxygen A and B Absorption Bands 明亮表面尘埃气溶胶光学质心高度（AOCH）：首次从TROPOMI氧A和B吸收带反演

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-20 DOI: 10.1109/LGRS.2025.3601046

Xi Chen;Jun Wang;Xiaoguang Xu;Meng Zhou

{"title":"Dust Aerosol Optical Centroid Height (AOCH) Over Bright Surface: First Retrieval From TROPOMI Oxygen A and B Absorption Bands","authors":"Xi Chen;Jun Wang;Xiaoguang Xu;Meng Zhou","doi":"10.1109/LGRS.2025.3601046","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601046","url":null,"abstract":"The vertical distribution of dust layers can influence dust transport, radiative forcing, deposition, and ultimately, surface particulate matter mass concentration. Although many dust aerosol layer height (ALH) products from passive satellite measurements have been developed, most of them are applicable on dark surfaces. Here, building on the absorbing aerosol optical centroid height (AOCH) retrieval from hyperspectral O<sub>2</sub> A and B absorption band measurements of the tropospheric monitoring instrument (TROPOMI) for dark target, we further develop dust AOCH retrieval over bright surfaces. Key updates include: 1) the thresholds in cloud mask tests are refined with consideration of the different spectral characteristics of bright surface reflectance; and 2) the assumption of Lambertian surface is modified to the Ross–Li bidirectional reflectance distribution function (BRDF) model to consider the angular dependence of surface reflectance. The validation against the cloud-aerosol lidar with orthogonal polarization (CALIOP) for several dust plumes over the Saharan Desert illustrates that TROPOMI AOCH has ~1 km uncertainty and ~0.1-km mean bias, better than ~1 km underestimated dust-layer mean altitude (ALT) from the infrared atmospheric sounder interferometer (IASI). With this implementation of bright surfaces, our algorithm is ready for global retrieval and will be applicable for similar hyperspectral instruments in the future.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11131137","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Characterizing Thermal Anomaly in Pierazzo Crater Based on CELMS and Multispectral Data 基于CELMS和多光谱数据的Pierazzo陨石坑热异常表征

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-20 DOI: 10.1109/LGRS.2025.3601005

Xiaoyue Wang;Zhanchuan Cai

引用次数: 0

DFWA-Net: Dual-Domain Feature-Enhanced With Wavelet Attention Network for SAR Ship Detection 基于双域特征增强的小波注意网络的SAR舰船检测

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-20 DOI: 10.1109/LGRS.2025.3601026

Shuaiqi Liu;Wenjing Jiang;Yue Yu;Bing Li;Yudong Zhang;Qi Hu

{"title":"DFWA-Net: Dual-Domain Feature-Enhanced With Wavelet Attention Network for SAR Ship Detection","authors":"Shuaiqi Liu;Wenjing Jiang;Yue Yu;Bing Li;Yudong Zhang;Qi Hu","doi":"10.1109/LGRS.2025.3601026","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601026","url":null,"abstract":"Synthetic aperture radar (SAR) is a high-resolution remote sensing technology widely employed for ground and sea surface target detection. However, due to the unique imaging mechanism and information representation of SAR images, conventional spatial-domain feature extraction methods often struggle to fully capture their discriminative features. To address this limitation, this letter introduces the wavelet domain as an additional feature extraction space and proposes a dual-domain feature-enhanced network based on wavelet attention for SAR ship detection. Specifically, two wavelet attention modules are designed to independently and jointly compute attention for high-frequency and low-frequency features in the wavelet domain. Meanwhile, an embedding grouping strategy is adopted to reduce computational costs while enhancing the model’s detailed perception and global understanding of ship targets. Furthermore, a dynamic domain fusion (DDF) module is proposed to more effectively integrate wavelet-domain and spatial-domain information, enriching feature representation. Comprehensive experiments on two widely used SAR ship datasets demonstrate that the proposed method outperforms many other state-of-the-art detectors. The source code is available at <uri>https://github.com/Wenjing-Jiang-hbu/DFWA-Net</uri>","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Infrared Small-Target Detection Based on Holistic Interframe Interaction and Spatiotemporal Local Contrast Method 基于整体帧间交互和时空局部对比方法的红外小目标检测

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-20 DOI: 10.1109/LGRS.2025.3600996

Yunqiao Xi;Dongyang Liu;Renke Kou;Yinhu Wu;Junping Zhang

{"title":"Infrared Small-Target Detection Based on Holistic Interframe Interaction and Spatiotemporal Local Contrast Method","authors":"Yunqiao Xi;Dongyang Liu;Renke Kou;Yinhu Wu;Junping Zhang","doi":"10.1109/LGRS.2025.3600996","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3600996","url":null,"abstract":"Infrared (IR) small-target detection (ISTD) plays a crucial role in IR search and tracking systems. However, current detection methods are limited by the small-target size and low signal-to-noise ratio of IR imagery. Furthermore, motion features for target detection are difficult to extract using simple frame subtraction due to poor imaging conditions. Therefore, we focus on the holistic interframe interaction to enhance the temporal feature and propose a spatiotemporal local contrast method in this letter. First, the motion-enhanced density peak clustering (ME-DPC) is employed to determine the robust localization of candidate targets, in which the density feature maps are generated by the preprocessing of nonconsecutive three-frame difference after image registration. Second, to reliably exploit interframe interactions across both nonconsecutive and successive frames, a temporal-domain saliency map is computed based on local regions from successive frames. Moreover, a spatial-domain saliency map is obtained using a novel trilayer local contrast measure (TLLCM). By fusing results from both domains, the IR small targets are detected through adaptive threshold segmentation. The experimental results on four real sequences demonstrate that the proposed method can achieve better detection performance by target enhancement and background suppression than other spatiotemporal algorithms.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial–Spectral Hypergraph Dynamic Gating MLP Network for Hyperspectral Image Classification 用于高光谱图像分类的空间光谱超图动态门控MLP网络

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-20 DOI: 10.1109/LGRS.2025.3600896

Yang-Jun Deng;Yanglan Li;Longfei Ren;Si-Qiao Tan;Qian Du

{"title":"Spatial–Spectral Hypergraph Dynamic Gating MLP Network for Hyperspectral Image Classification","authors":"Yang-Jun Deng;Yanglan Li;Longfei Ren;Si-Qiao Tan;Qian Du","doi":"10.1109/LGRS.2025.3600896","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3600896","url":null,"abstract":"The advancement of spaceborne hyperspectral remote sensing technology has led to the widespread use of hyperspectral imaging, due to its ability to detect subtle spectral differences. Most of the traditional machine-learning (ML) methods and popular deep-learning (DL) architectures for hyperspectral image (HSI) classification either fail to capture global features or demand high computational resources. While multilayer perceptron (MLP)-based models offer a computationally efficient alternative, they struggle to capture manifold structures and are susceptible to overfitting. To address these challenges, we propose a novel spatial–spectral hypergraph dynamic gating MLP (S2H-DGMLP) framework tailored for HSI classification. The spatial–spectral hypergraph enhances discriminative power by modeling high-order spatial and spectral correlations, jointly optimizing local spatial features and global spectral features to produce more separable feature representations in the embedding space. Within this framework, the channel and spatial projections are statically parameterized using MLP, while the dynamic gating MLP (DGMLP) block captures global contextual information. The dynamic gating mechanism within the DGMLP block automatically adjusts the segmentation ratio to balance spatial and spectral contributions, while incorporating complex nonlinear combinations to improve feature representation. Experimental results on the Pavia University and Houston datasets demonstrate that S2H-DGMLP significantly improves classification performance, confirming its effectiveness in HSI classification tasks.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semi-Supervised Learning for Spatio-Temporal Landcover Monitoring 基于半监督学习的土地覆盖时空监测

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-19 DOI: 10.1109/LGRS.2025.3600458

Boris Flach;Tomáš Dlask;Lukáš Brodský

引用次数: 0

RATE: A Retrieval-Augmented Transformer for Regional Earthquake Early Warning 区域地震预警的检索增强变压器RATE

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-13 DOI: 10.1109/LGRS.2025.3598322

Wen-Wei Lin;Kuan-Yu Chen;Da-Yi Chen

引用次数: 0

A Shift-Reduced Sample Expansion Domain Generalization Network for Hyperspectral Image Cross-Domain Classification 高光谱图像跨域分类的平移减少样本扩展域泛化网络

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-13 DOI: 10.1109/LGRS.2025.3598295

Yunxiao Qi;Dongyang Liu;Junping Zhang

{"title":"A Shift-Reduced Sample Expansion Domain Generalization Network for Hyperspectral Image Cross-Domain Classification","authors":"Yunxiao Qi;Dongyang Liu;Junping Zhang","doi":"10.1109/LGRS.2025.3598295","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3598295","url":null,"abstract":"In practical applications, the variations in imaging conditions along with changes in ground object states cause spectral shifts within the same class across different domains of hyperspectral images (HSIs), resulting in substantial domain distribution discrepancies. Additionally, the annotation process for HSIs is time-consuming, yielding an insufficient amount of labeled data relative to the needs of strong models, making them prone to overfitting during training. To address these issues, the shift-reduced sample expansion domain generalization network (SSEDGnet) is proposed. Sample diversity is first enhanced by generating expanded domain (ED) samples. Then, feature extraction is jointly performed on multiple source-domain (SD) samples and ED samples to learn domain-invariant representations, which enhances adaptability to unseen target domains (TDs). Specifically, by modeling the full imaging process from stimulation to response, including signal transmission and ground object reflection, the ground object reflection is separately extracted and used to directly generate ED samples through stimulation, thereby obtaining samples with reduced domain shift. Subsequently, feature extraction and fusion at different levels are carried out on both the SDs and EDs. Finally, the classifier conducts the classification. The experimental results on four public HSI datasets show that the proposed method effectively learns a model with superior generalization ability and stability, outperforming state-of-the-art methods. The code will be released soon on the site of <uri>https://github.com/Cherrieqi/SSEDGnet</uri>","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0