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L${}_{\text{2}}$min${}^{\text{2/2s}}$: Efficient Linear Reconstruction Filter for Incremental Delta-Sigma ADCs IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2023-09-18 Bo Wang, Man-Kay Law, Jens Schneider
While it becomes more challenging to improve the energy efficiency of incremental delta-sigma data converters (IDCs) from the analog circuit design perspective, we propose two novel linear reconstruction filters for IDCs to enhance their performance in a digital way, including the L ${}_{\mathbf{2}}$ min ${}^{\mathbf{2}}$ filter and its symmetric version, the L ${}_{\mathbf{2}}$ min ${}^{\mathbf{2s}}$
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Category-Specific Prototype Self-Refinement Contrastive Learning for Few-Shot Hyperspectral Image Classification IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-19 Quanyong Liu, Jiangtao Peng, Na Chen, Weiwei Sun, Yujie Ning, Qian Du
Deep learning (DL) has been extensively used for hyperspectral image classification (HSIC) with significant success, but the classification of high-dimensional hyperspectral image (HSI) datasets with a limited amount of labeled samples is still a great challenge. Few-shot learning (FSL) has shown excellent performance in solving small-sample classification problems. However, most of the existing FSL
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FHIC: Fast Hyperspectral Image Classification Model Using ETR Dimensionality Reduction and ELU Activation Function IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-18 Dalal Al-Alimi, Zhihua Cai, Mohammed A. A. Al-qaness
Hyperspectral images (HSIs) are typically utilized in a wide variety of practical applications. HSI is replete with spatial and spectral information, which provides precise data for material detection. HSIs are characterized by a high degree of variations and undesirable pixel distributions, providing major processing challenges. This article introduces the fast hyperspectral image classification (FHIC)
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UnDAT: Double-Aware Transformer for Hyperspectral Unmixing IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-12 Yuexin Duan, Xia Xu, Tao Li, Bin Pan, Zhenwei Shi
Deep-learning-based methods have attracted increasing attention on hyperspectral unmixing, where the transformer models have shown promising performance. However, recently proposed deep-learning-based hyperspectral unmixing methods usually tend to directly apply visual models, while ignoring the characteristics of hyperspectral imagery. In this article, we propose a novel double-aware transformer for
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Transformer-Based Masked Autoencoder With Contrastive Loss for Hyperspectral Image Classification IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-15 Xianghai Cao, Haifeng Lin, Shuaixu Guo, Tao Xiong, Licheng Jiao
In recent years, in order to solve the problem of lacking accurately labeled hyperspectral image data, self-supervised learning has become an effective method for hyperspectral image classification. The core idea of self-supervised learning is to define a pretext task, which helps to train the model without the labels. By exploiting both the information of the labeled and unlabeled samples, self-supervised
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MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-14 Mohamad Jouni, Mauro Dalla Mura, Lucas Drumetz, Pierre Comon
Hyperspectral unmixing allows representing mixed pixels as a set of pure materials weighted by their abundances. Spectral features alone are often insufficient, so it is common to rely on other features of the scene. Matrix models become insufficient when the hyperspectral image (HSI) is represented as a high-order tensor with additional features in a multimodal, multifeature framework. Tensor models
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SFL-NET: Slight Filter Learning Network for Point Cloud Semantic Segmentation IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-13 Xu Li, Zhenxin Zhang, Yong Li, Mingmin Huang, Jiaxin Zhang
In recent years, point clouds have been widely used in power-line inspection, smart cities, autonomous driving, and other fields. Deep learning-based point cloud processing methods have achieved some impressive results in point cloud semantic segmentation, which has attracted more and more attention. However, there are still some problems that need to be solved, such as the efficiency of point cloud
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THE Benchmark: Transferable Representation Learning for Monocular Height Estimation IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-13 Zhitong Xiong, Wei Huang, Jingtao Hu, Xiao Xiang Zhu
Generating 3-D city models rapidly is crucial for many applications. Monocular height estimation (MHE) is one of the most efficient and timely ways to obtain large-scale geometric information. However, existing works focus primarily on training and testing models using unbiased datasets, which does not align well with real-world applications. Therefore, we propose a new benchmark dataset to study the
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Stereo Cross-Attention Network for Unregistered Hyperspectral and Multispectral Image Fusion IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-13 Yujuan Guo, Xiyou Fu, Meng Xu, Sen Jia
The necessary prerequisite for effective data fusion is the strict registration of low-resolution hyperspectral images (LR-HSIs) and high-resolution multispectral images (HR-MSIs). However, registration requires a complex process that takes into account the effects of light, imaging angle, and geometric distortion of the image during acquisition. Therefore, to avoid complex registration, we focused
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UCDFormer: Unsupervised Change Detection Using a Transformer-Driven Image Translation IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-13 Qingsong Xu, Yilei Shi, Jianhua Guo, Chaojun Ouyang, Xiao Xiang Zhu
Change detection (CD) by comparing two bitemporal images is a crucial task in remote sensing. With the advantages of requiring no cumbersome labeled change information, unsupervised CD has attracted extensive attention in the community. However, existing unsupervised CD approaches rarely consider the seasonal and style differences incurred by the illumination and atmospheric conditions in multitemporal
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Daily Landscape Freeze/Thaw State Detection Using Spaceborne GNSS-R Data in Qinghai–Tibet Plateau IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-12 Wentao Yang, Fei Guo, Xiaohong Zhang, Tianhe Xu, Nazi Wang, Lili Jing
The freeze-thaw (F/T) process plays a significant role in climate change and ecological systems. The soil F/T state can now be determined using microwave remote sensing. However, its monitoring capacity is constrained by its low spatial resolution or long revisit intervals. In this study, spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data with high temporal and spatial resolutions
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ALNet: Auxiliary Learning-Based Network for Weakly Supervised Building Extraction From High-Resolution Remote Sensing Images IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-12 Xin Yan, Li Shen, Junjie Pan, Jicheng Wang, Chao Chen, Zhilin Li
Weakly supervised semantic segmentation (WSSS) based on image-level labels can reduce the expensive costs of annotating pixel-level labels, and it has achieved great progress by generating class activation maps (CAMs) as pseudo-labels for training a segmentation model. However, it is challenging to generate high-quality CAMs due to the large supervision gap between classification and segmentation.
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SAR ATR Method With Limited Training Data via an Embedded Feature Augmenter and Dynamic Hierarchical-Feature Refiner IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-12 Chenwei Wang, Siyi Luo, Yulin Huang, Jifang Pei, Yin Zhang, Jianyu Yang
Without sufficient data, the quantity of information available for supervised training is constrained, as obtaining sufficient synthetic aperture radar (SAR) training data in practice is frequently challenging. Therefore, current SAR automatic target recognition (ATR) algorithms perform poorly with limited training data availability, resulting in a critical need to increase SAR ATR performance. In
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Multiscale Neighborhood Attention Transformer With Optimized Spatial Pattern for Hyperspectral Image Classification IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-12 Xin Qiao, Swalpa Kumar Roy, Weimin Huang
Hyperspectral images (HSIs) provide hundreds of continuous spectral bands and have been widely used for fine identification of targets with similar appearances. In earlier studies, convolutional neural networks (CNNs) have been an effective method for HSIs’ classification due to their powerful feature extraction capabilities. Recently, self-attention (SA)-based vision transformer (ViT) architecture
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CMTFNet: CNN and Multiscale Transformer Fusion Network for Remote-Sensing Image Semantic Segmentation IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-12 Honglin Wu, Peng Huang, Min Zhang, Wenlong Tang, Xinyu Yu
Convolutional neural networks (CNNs) are powerful in extracting local information but lack the ability to model long-range dependencies. In contrast, the transformer relies on multihead self-attention mechanisms to effectively extract the global contextual information and thus model long-range dependencies. In this article, we propose a novel encoder–decoder structured semantic segmentation network
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On the Reconstruction and Prediction Improvements of the Deterministic Sea Wave Predictable Zone Using Spatio-Temporal Coherent Radar Measurements IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-12 Jiangheng He, Zezong Chen, Chen Zhao, Xi Chen
In the study of deterministic sea waves, the predictable zone refers to the area where the phase-resolvable wave field can be fully reconstructed and accurately predicted based on measured data. Under certain marine environmental and radar measurement parameter conditions, expanding the predictable zone is of great significance for domains such as quiescent period prediction (QPP) for ships and optimizing
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Joint Self-Training and Rebalanced Consistency Learning for Semi-Supervised Change Detection IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-12 Xueting Zhang, Xin Huang, Jiayi Li
Change detection (CD) is an important Earth observation task that can monitor change areas at two times from the view of space. However, fully supervised CD has a heavy dependence on numerous manually labeled data, limiting their applications in practice. Beyond the fully supervised setting, semi-supervised CD (SSCD), which uses a few labeled data to guide the unsupervised learning of dominant unlabeled
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Accelerating Neural Style-Transfer Using Contrastive Learning for Unsupervised Satellite Image Super-Resolution IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-11 Divya Mishra, Ofer Hadar
Contrastive learning is a self-supervised comparison of two samples to identify characteristics and traits that distinguish one data class from another, improving performance on visual tasks. The performance of existing super-resolution-based approaches degrades with increasing scaling factors, hence practically not useful for high-resolution (HR) imaging applications. We proposed a novel framework
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Near-Real-Time Estimation of Hourly All-Weather Land Surface Temperature by Fusing Reanalysis Data and Geostationary Satellite Thermal Infrared Data IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-11 Lirong Ding, Ji Zhou, Zhao-Liang Li, Xinming Zhu, Jin Ma, Ziwei Wang, Wei Wang, Wenbin Tang
It is urgently needed to obtain the hourly near-real-time all-weather land surface temperature (NRT-AW LST) for immediately monitoring the disaster and environmental changes. Nevertheless, studies on estimating hourly NRT-AW LST are in the preliminary stage. In this study, we proposed a Spatio-TEmporal Fusion (STEF) method for fusing the reanalysis dataset derived from the China Land Surface Data Assimilation
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An Adaptive Ionosphere Clutter Suppression and Target Detection Method for HFSWR Maritime Surveillance IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-11 Xiaowei Ji, Xiaochuan Wu, Dezhu Xiao, Qiang Yang
High-frequency surface wave radar (HFSWR) as a maritime surveillance facility faces the tough challenges. One of them is that various unwanted echo components constantly destroy the detection performance of HFSWR, especially the dynamically changing ionosphere clutter. This article proposes an adaptive ionosphere clutter suppression and target detection algorithm (AICSTD) based on improved higher order
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SPGAN-DA: Semantic-Preserved Generative Adversarial Network for Domain Adaptive Remote Sensing Image Semantic Segmentation IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-11 Yansheng Li, Te Shi, Yongjun Zhang, Jiayi Ma
Unsupervised domain adaptation for remote sensing semantic segmentation seeks to adapt a model trained on the labeled source domain to the unlabeled target domain. One of the most promising ways is to translate images from the source domain to the target domain to align the spectral information or imaging mode by the generative adversarial network (GAN). However, source-to-target translation often
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Graph-Based Active Learning for Nearly Blind Hyperspectral Unmixing IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-11 Bohan Chen, Yifei Lou, Andrea L. Bertozzi, Jocelyn Chanussot
Hyperspectral unmixing (HSU) is an effective tool to ascertain the material composition of each pixel in a hyperspectral image with typically hundreds of spectral channels. In this article, we propose two graph-based semisupervised unmixing methods. The first one directly applies graph learning to the unmixing problem, while the second one solves an optimization problem that combines the linear unmixing
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AS3ITransUNet: Spatial–Spectral Interactive Transformer U-Net With Alternating Sampling for Hyperspectral Image Super-Resolution IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-11 Qin Xu, Shiji Liu, Jiahui Wang, Bo Jiang, Jin Tang
Single hyperspectral image (HSI) super-resolution (SR) is an important topic in the remote-sensing field. However, existing HSI SR methods mainly use the feed-forward upsampling technique and convolutional neural network (CNN) to learn the feature representation, failing to learn the complex mapping relationship between low-resolution (LR) and high-resolution (HR) and long-range joint spectral and
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Neighborhood Spatial Aggregation MC Dropout for Efficient Uncertainty-Aware Semantic Segmentation in Point Clouds IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-11 Chao Qi, Jianqin Yin, Yingchun Niu, Jinghang Xu
Uncertainty-aware semantic segmentation of the point clouds includes predictive uncertainty estimation and uncertainty-guided model optimization. One key challenge in the task is the efficiency of pointwise predictive distribution establishment. The widely used Monte Carlo (MC) dropout establishes the distribution by computing the standard deviation of samples using multiple stochastic forward propagations
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Microseismic Events Recognition via Joint Deep Clustering With Residual Shrinkage Dense Network IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-11 Qiang Feng, Liguo Han, Binghui Zhao, Qiang Li
Recognition of microseismic events is the primary task of microseismic monitoring. Aiming at the low signal-to-noise ratio (SNR) of weak microseismic events and the high cost of labeling them, an unsupervised learning method for recognizing microseismic events is proposed. The method first recognizes microseismic events from monitoring data segments by simultaneous deep clustering and then performs
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A Forest Leaf Area Index Inversion Scheme Using Polarimetric SAR IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-11 Wenxue Fu, Jian Wang, Huadong Guo
A novel forest leaf area index (LAI) inversion scheme using polarimetric synthetic aperture radar (PolSAR) images is proposed. The advantage of inversion using synthetic aperture radar (SAR) images over conventional optical methods is the ability to perform all-weather and all-time forest observation, especially in the tropical rainforest area. In this article, leaves were assumed to be isotropic media
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CODE-MM: Convex Deep Mangrove Mapping Algorithm Based on Optical Satellite Images IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-11 Chia-Hsiang Lin, Man-Chun Chu, Po-Wei Tang
Mangrove mapping (MM) is a critical satellite remote sensing technology since mangrove forests have a large capacity for carbon storage among the blue carbon ecosystems. However, we surprisingly found that benchmark MM methods are all index-based ones, completely ignoring the spatially neighboring information on the one hand and quite sensitive to the threshold setting on the other hand. Deep learning
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Revolutionizing Remote Sensing Image Analysis With BESSL-Net: A Boundary-Enhanced Semi-Supervised Learning Network IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-11 Zhiyu Yi, Yuebin Wang, Liqiang Zhang
Deep learning (DL) has become increasingly popular in remote sensing (RS) change detection (CD), leading to the development of massive networks that surpass traditional methods in accuracy and automation. However, the need for enormous amounts of annotated data remains a major concern, and accurate boundary segmentation in RS images is challenging due to their complexity and heterogeneity. Moreover
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Combining Hilbert Feature Sequence and Lie Group Metric Space for Few-Shot Remote Sensing Scene Classification IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-11 Xiliang Chen, Guobin Zhu, Chan Ji
Few-shot learning (FSL) is a method that does not require a large number of labeled samples and has the ability to identify unseen samples. Recently, many excellent FSL methods have been developed for remote sensing scene classification (RSSC). However, for complex remote sensing scenes, the following problems have not been well addressed: 1) the order of local features and the spatial correlation
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ADCG: A Cross-Modality Domain Transfer Learning Method for Synthetic Aperture Radar in Ship Automatic Target Recognition IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-11 Gui Gao, Yuxi Dai, Xi Zhang, Dingfeng Duan, Fei Guo
Due to the powerful feature extraction and expression ability of convolutional neural networks (CNNs), exceptional success has been achieved in the field of ship automatic target recognition (ATR) of synthetic aperture radar (SAR). However, the CNNs cannot work effectively with sparse labeled samples and imbalanced categories. This study proposes a new attention-dense-CycleGAN (ADCG) method that is
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Classification of Cloud Phase Using Combined Ground-Based Polarization Lidar and Millimeter Cloud Radar Observations Over the Tibetan Plateau IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-11 Yuxuan Bian, Liping Liu, Jiafeng Zheng, Songhua Wu, Guangyao Dai
The distributions of cloud phases play an important role in influencing the weather and climate system. The characteristics of clouds above the Tibetan Plateau (TP) can profoundly affect regional and global atmospheric circulation. To research the distributions of cloud phases in the TP region, a retrieval algorithm was developed based on the combination of polarization lidar and millimeter cloud radar
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Electrical–Elastic Joint Inversion Method for Fracture Characterization in Anisotropic Media IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-11 Chen Guo, Zhenzhen Fan, Zhifang Yang, Xinfei Yan, Bowen Ling
Fracture networks are omnipresent in unconventional energy reservoirs. The inversion of fractures is of vital importance to oil and gas exploration and production. Most of the existing inversion methods are developed based on homogeneous media theory and rely on a solitary physical descriptor. For instance, one commonly employed single-property inversion approach is the determination of water saturation
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Power-Law Distribution of Rapid Ionosphere Electron Content Fluctuations via GNSS Measurements IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-08 Enric Monte-Moreno, Manuel Hernández-Pajares, Heng Yang
The ionosphere is a dynamic region of the Earth’s upper atmosphere that exhibits various fluctuations in electron density. These fluctuations can be estimated using measurements from the global navigation satellite system (GNSS). In this article, our focus is on characterizing the probability distribution of total electron content (TEC) fluctuations and their temporal duration. The findings of this
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Spatiotemporal Correlation Characteristics Between Thermal Infrared Remote Sensing Obtained Surface Thermal Anomalies and Reconstructed 4-D Temperature Fields of Underground Coal Fires IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-08 Gang Yuan, Yunjia Wang, Feng Zhao, Shiyong Yan, Hua Zhang, Fengkai Lang, Ming Hao, Fei Cao, Bin Peng, Libo Dang, Yougui Feng
Underground coal fires are global catastrophes that result in energy waste, carbon emission, and eco-environment pollution. Remote sensing (RS) detection is essential for underground coal fire extinguishing engineering, and the most used is thermal infrared (TIR) RS. It can well obtain the thermal anomalies of land surface temperature (LST), which is the most direct surface feature of underground coal
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Influence of Temperature on Soil Dielectric Spectra in the 20 MHz–3 GHz Frequency Range IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-08 Agnieszka Szypłowska, Arkadiusz Lewandowski, Marcin Kafarski, Justyna Szerement, Andrzej Wilczek, Małgorzata Budzeń, Jacek Majcher, Wojciech Skierucha
Measurement of soil dielectric permittivity is frequently used as a basis for soil water content estimation by many commercial and experimental sensors. However, the relations between dielectric permittivity and soil water content may be affected by various other factors, including texture, salinity, and temperature. In this present article, spectra of complex dielectric permittivity of samples of
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Elaborated-Structure Awareness SAR Imagery Using Hessian-Enhanced TV Regularization IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-08 Lei Yang, Minghui Gai, Tengteng Wang, Mengdao Xing
Due to the sparse feature enhancement only concentrating on strong scatterers of target of interest, the conventional sparsity-driven synthetic aperture radar (SAR) imagery often encounters the loss of elaborated-structure features, where weak scatterers would be overlapped by the sidelobes of strong scatterers. In this article, an elaborated-structure awareness SAR (ESA-SAR) imaging algorithm is proposed
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3-D Sequential Joint Inversion of Magnetotelluric, Magnetic, and Gravity Data Based on Coreference Model and Wide-Range Petrophysical Constraints IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-08 Zhiwen Zeng, Jiangtao Han, Tianqi Wang, Lijia Liu, Xiao Chen
Due to the intricate and uncertain nature of petrophysical properties, the practical application of petrophysical joint inversion poses significant challenges, and the realization of multigeophysical joint inversion methods is particularly demanding. In this study, we present an effective and versatile joint inversion method based on a coreference model and wide-range petrophysical constraints, building
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Graph Attention Guidance Network With Knowledge Distillation for Semantic Segmentation of Remote Sensing Images IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-08 Wujie Zhou, Xiaomin Fan, Weiqing Yan, Shengdao Shan, Qiuping Jiang, Jenq-Neng Hwang
Deep learning has become a popular method for studying the semantic segmentation of high-resolution remote sensing images (HRRSIs). Existing methods have adopted convolutional neural networks (CNNs) to achieve better segmentation accuracy of HRRSIs, and the success of these models often depends on the model complexity and parameter quantity. However, the deployment of these models on equipment with
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Improved PROSPECT Model Based on Optimization of the Internal Blade Structure and Absorption Coefficient IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-07 Fenghua Yu, Shuang Xiang, Juchi Bai, Shengfan Zhu, Tongyu Xu
Radiation transfer is an important physical basis for establishing the relationship between spectral and biochemical information. In this article, the interior of the blade is regarded as superimposed by layers with different optical characteristics. The PIOSL (PROSPECT considers the internal optical structure of the leaves) model was proposed, and the element-specific absorption coefficients in the
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Task-Specific Heterogeneous Network for Object Detection in Aerial Images IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-07 Ying Yu, Xi Yang, Jie Li, Xinbo Gao
Object detection in aerial images has attracted increasing attention in recent years. Due to the complex background and arbitrary-oriented objects, it is challenging to accurately locate the objects of interest in the images. Many methods have been developed for improving localization accuracy of oriented objects. However, classification and localization tasks require different features due to the
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Fishing Vessel Classification in SAR Images Using a Novel Deep Learning Model IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-07 Yanan Guan, Xi Zhang, Siwei Chen, Genwang Liu, Yongjun Jia, Yi Zhang, Gui Gao, Jie Zhang, Zhongwei Li, Chenghui Cao
With the development of deep learning (DL), research on ship classification in synthetic aperture radar (SAR) images has made remarkable progress. However, such research has primarily focused on classifying large ships with distinct features, such as cargo ships, containers, and tankers. The classification of SAR fishing vessels is extremely challenging because of two main reasons: 1) the small size
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A Time-Domain Filtering Method Based on Intrapulse Joint Interpulse Coding to Counter Interrupted Sampling Repeater Jamming in SAR IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-07 Jingyi Wei, Yachao Li, Rui Yang, Endi Zhu, Jiabao Ding, Mingyue Ding, Pan Zhang
The interrupted sampling repeater jamming (ISRJ) can effectively degrade the image quality and affect the subsequent target recognition by creating deceptive multiple false targets on synthetic aperture radar (SAR) images. A time-domain filtering method based on pulse coding to counter ISRJ is proposed in this article. First, this coding method requires the radar to transmit a full pulse signal consisting
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Enhancing Snow Depth Estimations Through Iterative Satellite Elevation Range Selection in GNSS-IR to Account for Terrain Variation IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-07 Cemali Altuntas, Nursu Tunalioglu
The multipath effect in Global Navigation Satellite System (GNSS) has become a robust data source thanks to GNSS interferometric reflectometry (GNSS-IR), which provides environment-related features by considering the interference pattern of direct and reflected signals recorded simultaneously on the antenna phase center (APC) of the GNSS receiver. When analyzing the fluctuation of the signal strength
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Multispectral Image Pan-Sharpening Guided by Component Substitution Model IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-07 Huiling Gao, Shutao Li, Jun Li, Renwei Dian
Multispectral image pan-sharpening aims to increase the spatial details of multispectral images by fusing multispectral and panchromatic (PAN) images. Existing component substitution (CS)-based deep learning pan-sharpening is generally regarded as a black box and fails to mine the image interaction relation with physical significance in each step of pan-sharpening, which not only limits the improvement
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The Wind Effect on Interferometric Altimeter Validation Using Steric Method in South China Sea IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-06 Qianran Zhang, Xiaobo Zhang, Shengli Wang, Xinghua Zhou
Recently, interferometric altimeters (IAs), such as the surface water and ocean topography (SWOT) satellite, have been launched and will improve the observation of ocean dynamics. The validation of altimeters is a complex and important process that can effectively improve their observation accuracy. For the IA validation of sea surface height (SSH) in 2-D, it is normal to use the steric height (SH)
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CSnNet: A Remote Sensing Detection Network Breaking the Second-Order Limitation of Transformers With Recursive Convolutions IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-06 Chengcheng Chen, Weiming Zeng, Xiliang Zhang, Yuhao Zhou
In recent years, Transformer-based networks, known for their ability to model long-range dependencies, have been widely used in downstream computer vision tasks, surpassing certain neural network architectures. However, Transformer-based networks suffer from issues, such as large parameter size, high-computational complexity (CC), and difficulties in extending spatial and channel features to the third
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Multistep Question-Driven Visual Question Answering for Remote Sensing IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-06 Meimei Zhang, Fang Chen, Bin Li
Visual question answering (VQA) aims to build an interactive system that infers the answer according to the input image and text-based question. Recently, VQA for remote sensing has attracted considerable attention since it is essential and expedient for monitoring global resources and querying objective attributes. In reality, question-related semantic information is helpful for reasoning and understanding
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Multiscale Factor Joint Learning for Hyperspectral Image Super-Resolution IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-06 Qiang Li, Yuan Yuan, Qi Wang
Hyperspectral image super-resolution (SR) using auxiliary RGB image has obtained great success. Currently, most methods, respectively, train single model to handle different scale factors, which may lead to the inconsistency of spatial and spectral contents when converted to the same size. In fact, the manner ignores the exploration of potential interdependence among different scale factors in a single
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A Copula-Based Method for Change Detection With Multisensor Optical Remote Sensing Images IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-06 Chengxi Li, Gang Li, Xueqian Wang, Pramod K. Varshney
This article considers the problem of change detection (CD) with multisensor optical remote sensing (RS) images. Copulas are adopted to characterize the dependence structure between the image pair. For this problem, a conditional copula-based CD technique has been proposed in the literature; however, in this technique, it is difficult to select the best copula function in an analytical framework. Resulting
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Application of Supervised Descent Method for 3-D Gravity Data Focusing Inversion IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-06 Rongzhe Zhang, Haoyuan He, Xintong Dong, Tonglin Li, Cai Liu, Xinze Kang
Three-dimensional gravity inversion is an effective method for extracting underground density distribution from gravity data. However, traditional deterministic gravity inversion methods suffer from problems such as skin effect, low computational accuracy, and poor efficiency. Therefore, we propose a 3-D gravity data focusing inversion algorithm based on the supervised descent method (SDM). SDM is
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Near-Borehole Formation Acoustic Logging Imaging: A Full Waveform Inversion Algorithm in Cylindrical Coordinates IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-05 Da Chen, Chao Zhang, Wei Guan, Jun Wang
Acoustic logging is essential for probing formations surrounding a borehole. However, conventional logging signal processing methods cannot process inhomogeneous formation structures. Inspired by seismic full waveform inversion (FWI), this study proposed a novel borehole imaging algorithm called borehole full-waveform inversion (BFWI) to probe the near-formation properties surrounding boreholes. To
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Self-Attention-Based Transformer for Nonlinear Maneuvering Target Tracking IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-05 Lu Shen, Hongtao Su, Ze Li, Congyue Jia, Ruixing Yang
In the field of radar, nonlinearity has always been significant challenge in target tracking algorithms. It is evident in the complexity of the target motion model, observation model, and maneuverability of the target. Traditional model-based algorithms often rely on numerical approximations or simulations to obtain suboptimal solutions, which may lead to conversion errors and increase algorithm complexity
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Geographic True Navigation Based on Real-Time Measurements of Geomagnetic Fields IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-05 Xiaokang Qi, Kuiwen Xu, Zhiwei Xu, Huan Li, Lixin Ran
Inspired by animals’ long-distance migration behaviors, we proposed a novel and reliable long-distance true navigation method based on the measurement of geomagnetic fields. By establishing a 2-D gradient approaching a coordinate plane with geomagnetic intensity and inclination, long-distance true navigation can be achieved from any starting spot in this area. Without other calibration and auxiliary
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Inclusive Consistency-Based Quantitative Decision-Making Framework for Incremental Automatic Target Recognition IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-05 Sihang Dang, Zhaoqiang Xia, Xiaoyue Jiang, Shuliang Gui, Xiaoyi Feng
When new unknown samples are captured continually in the open-world environment, the concept diversity accumulation of existing classes and the identification/creation of new concept classes should be considered simultaneously. Since the initial training set of existent classes may be underprepared, adhering to immediate decisions will inevitably lead to reduced open-set recognition (OSR) performance
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Content-Aware Subspace Low-Rank Tensor Recovery for Hyperspectral Image Restoration IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-04 Xueyao Xiao, Wei Zhang, Yi Chang, Shuning Cao, Wei He, Houzhang Fang, Luxin Yan
The low-rank tensor model has made great progress for hyperspectral image (HSI) restoration. Recently, the low-rank tensor methods have further been boosted with subspace learning by transforming original HSI into a low-dimensional subspace with reduced computational burden and discriminative feature representation. However, existing subspace-based methods consistently employ a fixed subspace dimension
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Multitemporal and Multispectral Data Fusion for Super-Resolution of Sentinel-2 Images IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-04 Tomasz Tarasiewicz, Jakub Nalepa, Reuben A. Farrugia, Gianluca Valentino, Mang Chen, Johann A. Briffa, Michal Kawulok
Multispectral Sentinel-2 (S-2) images are a valuable source of Earth observation data; however, spatial resolution of their spectral bands limited to 10-, 20-, and 60-m ground sampling distance (GSD) remains insufficient in many cases. This problem can be addressed with super-resolution (SR), aimed at reconstructing a high-resolution (HR) image from a low-resolution (LR) observation. For S-2, spectral
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Broadband Nonlinear Elastic Impedance Inversion Based on Modified Convolution Model IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-04 Xiao Chen, Zhaoyun Zong, Yongjian Zeng, Yaming Yang
In the absence of a well-log-based low-frequency model, a broadband inversion strategy with a low-frequency model calculated in complicated frequency-domain inversion can successfully mine low-frequency seismic information and decrease the inversion results’ reliance on the starting model to some extent. Furthermore, from the standpoint of the equation, prestack nonlinear inversion based on the nonlinear
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Dielectric Dispersion of Hydrate-Bearing Artificial Sediment—Detection Method and Experimental Observations IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-04 Bin Wang, Xiaoxiao Li, Xiaomeng Liu, Lanchang Xing, Muzhi Gao, Liyun Lao, Jianqin Deng, Xinmin Ge, Zhoutuo Wei
In this article, we reported a critical improvement on the measurement method for the dielectric dispersion characteristics of hydrate-bearing sediment in a frequency range between 1 MHz and 3 GHz, and, with the use of the method, a systematic evaluation of the microwave dielectric dispersion of the hydrate-bearing artificial sediment and influence factors. First, the optimized and verified procedure
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A High-Resolution Velocity Inversion Method Based on Attention Convolutional Neural Network IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-04 Wenda Li, Hong Liu, Tianqi Wu, Shoudong Huo
Velocity model building is an indispensable part of seismic exploration, which can directly affect the accuracy of subsequent data processing. Traditional full-waveform inversion (FWI) is usually challenging to update the deep background velocity information. Moreover, deep learning (DL)-based velocity modeling efforts can face the problem of lacking generalization ability. Based on this, we propose
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Seismic Acoustic Impedance Inversion Based on Arctangent Total Variation and Hybrid Domain Constraints IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-09-04 Hao Wu, Liangsheng He, Xiaotao Wen, Yuqiang Zhang
Sparse acoustic impedance (AI) inversion methods are widely used in seismic processing and interpretation. Due to the insufficiency of the sparse representation and absence of time–frequency domain prior information in traditional sparse inversion methods, the inversion results have low accuracy in pinch-out points and thin layers. To overcome these barriers, a multitrace AI inversion method based