IEEE Geoscience and Remote Sensing Letters最新文献_第3页

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

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

{"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}

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

IEEE Geoscience and Remote Sensing Letters Pub Date : 2022-01-01 DOI: 10.1109/lgrs.2021.3119850

F. Tang, Songhua Yan

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

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

{"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}

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

IEEE Geoscience and Remote Sensing Letters Pub Date : 2022-01-01 DOI: 10.1109/lgrs.2022.3174277

Tengfei Gong, Xiangtao Zheng, Xiaoqiang Lu

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

IEEE Geoscience and Remote Sensing Letters Pub Date : 2022-01-01 DOI: 10.1109/LGRS.2021.3121094

J. Dong, Wufan Zhao, Shuai Wang

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

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

引用次数: 5

Broadband Soil Permittivity Measurements Using a Novel De-Embedding Line–Line Method 一种新型去埋线-线法测量宽带土壤介电常数

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2022-01-01 DOI: 10.1109/lgrs.2021.3140097

Hafize Hasar, U. Hasar, Y. Kaya, T. Oztas, M. Y. Canbolat, Nevzat Aslan, M. Ertugrul, O. Ramahi

{"title":"Broadband Soil Permittivity Measurements Using a Novel De-Embedding Line–Line Method","authors":"Hafize Hasar, U. Hasar, Y. Kaya, T. Oztas, M. Y. Canbolat, Nevzat Aslan, M. Ertugrul, O. Ramahi","doi":"10.1109/lgrs.2021.3140097","DOIUrl":"https://doi.org/10.1109/lgrs.2021.3140097","url":null,"abstract":"A new de-embedding line–line method has been proposed for accurate complex relative permittivity (<inline-formula> <tex-math notation=\"LaTeX\">$varepsilon _{r}$ </tex-math></inline-formula>) determination of soil samples loaded into an EIA 1-5/8” coaxial transmission line measurement system. The method has three main features. First, it bypasses the requirement of calibration of this system by using only two identical coaxial lines with different lengths. Second, it does not need any numerical technique for <inline-formula> <tex-math notation=\"LaTeX\">$varepsilon _{r}$ </tex-math></inline-formula> determination. Third, it does not require knowledge of electromagnetic properties and thickness information of the bead used for supporting soil samples. The method is next validated by simulations performed using a full 3-D electromagnetic simulation program (CST Microwave Studio) and by <inline-formula> <tex-math notation=\"LaTeX\">$varepsilon _{r}$ </tex-math></inline-formula> measurement of a polyethylene (PE) material. Finally, <inline-formula> <tex-math notation=\"LaTeX\">$varepsilon _{r}$ </tex-math></inline-formula> values of three air-dried and water-saturated soil samples having 90% or more sand content with different electrical conductivities (ECs) and gathered from different areas of the city Gaziantep in Turkey, were measured.","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":"62486020","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: 4

High-Resolution Refocusing for Defocused ISAR Images by Complex-Valued Pix2pixHD Network 基于复值Pix2pixHD网络的离焦ISAR图像高分辨率重聚焦

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2022-01-01 DOI: 10.1109/LGRS.2022.3210036

Haoxuan Yuan, Hongbo Li, Yun Zhang, Yong Wang, Zitao Liu, Chenxi Wei, Chengxin Yao

{"title":"High-Resolution Refocusing for Defocused ISAR Images by Complex-Valued Pix2pixHD Network","authors":"Haoxuan Yuan, Hongbo Li, Yun Zhang, Yong Wang, Zitao Liu, Chenxi Wei, Chengxin Yao","doi":"10.1109/LGRS.2022.3210036","DOIUrl":"https://doi.org/10.1109/LGRS.2022.3210036","url":null,"abstract":"Inverse synthetic aperture radar (ISAR) is an effective detection method for targets. However, for the maneuvering targets, the Doppler frequency induced by an arbitrary scatterer on the target is time-varying, which will cause defocus on ISAR images and bring difficulties for the further recognition process. It is hard for traditional methods to well refocus all positions on the target well. In recent years, generative adversarial networks (GANs) achieve great success in image translation. However, the current refocusing models ignore the information of high-order terms containing in the relationship between real and imaginary parts of the data. To this end, an end-to-end refocusing network, named complex-valued pix2pixHD (CVPHD), is proposed to learn the mapping from defocus to focus, which utilizes complex-valued (CV) ISAR images as an input. A CV instance normalization layer is applied to mine the deep relationship between the complex parts by calculating the covariance of them and accelerate the training. Subsequently, an innovative adaptively weighted loss function is put forward to improve the overall refocusing effect. Finally, the proposed CVPHD is tested with the simulated and real dataset, and both can get well-refocused results. The results of comparative experiments show that the refocusing error can be reduced if extending the pix2pixHD network to the CV domain and the performance of CVPHD surpasses other autofocus methods in refocusing effects. The code and dataset have been available online (https://github.com/yhx-hit/CVPHD).","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":"62496447","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

Research on Shallow Groundwater Enrichment Assessment Based on RS and GIS Arid and Semi-Arid Areas 基于RS和GIS的干旱半干旱区浅层地下水富集评价研究

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2022-01-01 DOI: 10.23977/geors.2022.050106

Chuanyue Yang

引用次数: 0

D-SRCAGAN : DEM Super-resolution Generative Adversarial Network D-SRCAGAN: DEM超分辨率生成对抗网络

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2022-01-01 DOI: 10.1109/lgrs.2022.3224296

Xiaotong Deng, Weihua Hua, Xiuguo Liu, Siying Chen, Wen Zhang, Jianchao Duan

引用次数: 0