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For all of the tumultuous revolutions in wireless technology over the past several decades, there have been a couple of constants. One is the overcrowding of radio bands, and the other are attempts to escape that congestion by exploiting higher and higher frequencies. And today, as engineers roll out 5G and plan for 6G wireless, they find themselves at a crossroads.

Wireless connectivity is regarded as a fundamental need for our society. Between 2020 and 2030, it is forecast that the data traffic of the global internet protocol will increase by 55% each year, eventually reaching 5016 EB, with data rates scaling up to 1 Tb/s. Besides supporting very high data rates, future wireless networks are expected to offer several other heterogeneous services, which include

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The Ever-Increasing evolution of technologies in communication, artificial intelligence (AI), manufacturing, etc., is promoting a new wave of industrial revolution. Industrial Internet of Things (IIoT) has been considered as a critical stimulator for both science and economics by amounts of countries.

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Many studies have confirmed that the microwave radiation of rock materials will change with compressive stress and pore water. However, how the presence of pore water in rock volume influences the stress-induced change in microwave radiation ( $\Delta T_{B}$ ) is not clear. In this study, both the air-dry and humid (partially saturated) sandstone specimens were loaded compressively and the C-band (6

Due to the high-speed movement of a spaceborne radar (SBR) platform, the geographic clutter spectrum expands severely, resulting in the useful moving target signal submerged by the main-lobe clutter background. To deal with this issue, the equipped multichannel arrays in an SBR system provide sufficient spatial degrees, and as a consequence, the space-time adaptive processing (STAP) technology is often

In this article, we explore the possibility of detecting polar lows in C-band synthetic aperture radar (SAR) images by means of deep learning. Specifically, we introduce a novel dataset consisting of Sentinel-1 images divided into two classes, representing the presence and absence of a maritime mesocyclone, respectively. The dataset is constructed using the ECMWF reanalysis version 5 (ERA5) dataset

Challenges still exist in the task of object detection in remote sensing images with densely distributed objects due to large variation in scale and neglect of the relative position and correlation. To address these issues, a correlation learning detector based on transformer (CLT-Det) is proposed for detecting dense objects in remote sensing images. A transformer attention module (TAM) is designed

Landslide inventory mapping (LIM) is an important application in remote sensing for assisting in the relief of landslide geohazards. However, while conducting LIM tasks performing change detection analysis using bitemporal very high-resolution (VHR) remote sensing images, due to landslide usually occurred in a mountain area, the phenological difference and outcrop rock may bring pseudochanges to LIM

The leaf chlorophyll content (LCC) is an important plant physiological trait and is critical for accurate modeling of vegetation photosynthesis over time and space. To date, there is still a lack of a global long time-series dataset of LCC. In this study, we developed an algorithm to retrieve global LCC from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data from 2000 to

The exploration capacity of full waveform inversion (FWI) for deep targets is affected by the acquisition geometry, the complexity of overlying strata, and the dip angle of the target. It is of great importance to analyze the relationship between the dip angle of the target reflector and the incident/scattered angle of the wavefields near the target, to achieve the illumination distribution and improve

Homogeneous pixel selection (HPS) plays an important role in the application of multitemporal synthetic aperture radar interferometry (InSAR). The statistical goodness-of-fit testing of the temporal samples has been widely used for HPS. However, the detection rates of the existing methods are unsatisfactory under small datasets. In this article, a stacked autoencoder (SAE) network-based method is proposed

The azimuth cutoff wavelength of synthetic aperture radar (SAR) is an important parameter for retrieval of sea surface wind and wave. Earlier studies have fully demonstrated the substantial dependence of azimuth cutoff wavelength on polarization, but the present studies only focus on horizontal and vertical (H-V) linear polarization bases [horizontal-horizontal (HH), horizontal-vertical (HV)/vertical-horizontal

Convolutional neural networks (CNNs) have dominated the research of hyperspectral image (HSI) classification, attributing to the superior feature representation capacity. Fast patch-free global learning (FPGA) as a fast learning framework for HSI classification has received wide interest. Despite their promising results from the perspective of fast inference, recent works have difficulty modeling spectral–spatial

Reconstructing a high-resolution hyperspectral image (HR-HSI) using a single low-resolution hyperspectral image (LR-HSI) is a significant technique for increasing the spatial resolution of HSIs and overcoming the physical limitation of the HSI sensor. Most single HSI super-resolution methods have achieved great success recently. However, owning to the difficulty of acquiring an HSI, the available training

In photoelectric countermeasure systems, the infrared imaging of missiles is critical for automatic recognition and tracking technology of aerial targets. However, complex and newly emerging infrared interference signals severely hinder the recognition performance and lock the target ability of infrared thermal imaging systems. Although considerable progress has been achieved in the development of

Object tracking in satellite videos faces various challenges such as target occlusion, target rotation, and background clutter. This study proposes a Correlation particle filter (CPF) algorithm with motion estimation (ME) for object tracking in satellite videos. The tracker, called correlation particle Kalman filter (CPKF), combines the strengths of the correlation, particle, and Kalman filters. Compared

Urban informal settlements (UISs) are high-density population settlements with low standards of living and supply. UIS semantic segmentation, which identifies pixels corresponding to informal settlements in remote sensing images, is crucial to the estimation of poor communities, urban management, resource allocation, and future planning, particularly in megacities. However, most studies on informal

Inverse synthetic aperture ladar (ISAL) can perform high-resolution imaging for satellites. However, due to the short wavelength of the laser, satellite microvibration will introduce space-variant vibration phase error (SVVPE) and space-invariant vibration phase error (SIVVPE) in the echoes, which seriously blurs the ISAL image. In this article, we propose a noise-robust vibration phase compensation

This is the third of three articles that quantify the high added value of frequent satellite microwave observations of the atmosphere (with a “refresh” time on the order of 1 min) to capture the dynamics of weather systems. Recent advances in small-satellite and microwave miniaturization, such as the “Temporal Experiment for Storms and Tropical systems” (TempEST) millimeter-wave radiometer developed

In the above article [1] , some errors appear in equations due to incorrect fonts. First, the matrix variables should be denoted by uppercase boldface letters, e.g., $\boldsymbol{U}_{l}^{m}$ and $\boldsymbol{S}_{g}$ . The vector variables should be denoted as lowercase boldface letters, e.g., $c$ and $c^{\prime}$ .

Deep learning networks have facilitated the automated scene recognition of landforms based on geomorphogenesis. However, current genetic landform classification methods do not consider regional geological context, which can more accurately reflect the formation and evolution mechanism of geomorphic landforms than local ones. Therefore, this study proposes a multimodal, deep learning landform recognition

Traditional forward-looking imaging methods of real aperture radar yield unsatisfactory performance in the presence of outliers. In this article, a method based on variational Bayesian (VB) is proposed to obtain forward-looking imaging in the presence of outliers. First, considering the non-Gaussian property of the imaging noise due to the outliers, we propose to use the Student- $t$ distribution to

Phase unwrapping, also known as ambiguity resolution, is an underdetermined problem in which assumptions must be made to obtain a result in SAR interferometry (InSAR) time series analysis. This problem is particularly acute for distributed scatterer InSAR, in which noise levels can be so large that they are comparable in magnitude to the signal of investigation. In addition, deformation rates can be

Single hyperspectral image (HSI) super-resolution (SR) methods using a auxiliary high-resolution (HR) RGB image have achieved great progress recently. However, most existing methods aggregate the information of RGB image and HSI early during input or shallow feature extraction, whose difference between two images has not been treated and discussed. Although a few methods combine both the image features

Shadow removal is a challenging problem in hyperspectral remote sensing images due to its spatial-variant properties and diverse patterns. In this work, a shadow removal framework with multiexposure fusion is proposed for hyperspectral remote sensing images, which consists of three major steps. First, a color space conversion method is exploited to detect the shadow regions. Second, the principle of

Although deep learning has made semantic segmentation of very-high-resolution (VHR) remote sensing (RS) images practical and efficient, its large-scale application is still limited. Given the diversity of imaging sensors, acquisition conditions, and regional styles, a deep learning network well-trained on one source domain dataset often suffers from drastic performance drops when applied to other target

Traditional data-driven algorithms suffer from data reliance, hyperparameter sensitivity, and faint characteristics in infrared (IR) “low, slow, and small” unmanned aerial vehicle target detection and recognition, resulting in performance degradation in complex backgrounds. Inspired by model-driven methods, this article proposes a learnable feature modulation module that uses prior knowledge to enhance

Supervised deep learning models depend on massive labeled data. Unfortunately, it is time-consuming and labor-intensive to collect and annotate bitemporal samples containing desired changes. Transfer learning from pretrained models is effective to alleviate label insufficiency in remote sensing (RS) change detection (CD). We explore the use of semantic information during pretraining. Different from

Recently, a variety of super-resolution (SR) methods have been devoted to enhancing the angular resolution of real beam mapping (RBM) imagery in modern microwave remote sensing applications. When addressing large-scale datasets, however, they suffer from notably high computational complexity due to high-dimensional matrix inversion, multiplication, or singular value decomposition (SVD). To overcome

Coherent microwave radar processes sea echoes by using a direct relationship between wave orbital velocity and wave spectrum instead of backscattered intensity to obtain wave parameter. However, the measurements of wave orbital velocity are susceptible to ocean conditions (e.g., low sea state and interference), and thus the method’s performance is often uneven under the mixed conditions. To solve this

Impulse radio ultrawideband (IR-UWB) radar has high range resolution, strong anti-jamming ability, and low power consumption and has been widely used in target detection and recognition. Currently, existing studies always extract artificial features of echo signals, such as time–frequency images, Doppler features, or time-domain features, and then distinguish these features through well-designed deep

Convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification tasks because of their excellent local spatial feature extraction capabilities. However, because it is difficult to establish dependencies between long sequences of data for CNNs, there are limitations in the process of processing hyperspectral spectral sequence features. To overcome these limitations

Marine aquaculture is an important natural resource exploration that requires rational planning to avoid environmental damage. Synthetic aperture radar (SAR) images are essential in remote sensing to monitor the marine ecological environment. Unsupervised methods provide an adequate solution to avoid the cost of training sample collection. However, unsupervised methods often struggle to discover effective

Angular resolution of real aperture radar (RAR) can be improved using deconvolution methods to achieve enhanced target information based on the convolution relationship between target scatterings and an antenna pattern. However, depending on the wide scanning scope and dense sampling angular interval, the computational complexity of the deconvolution methods will drastically increase as the dimension

Convolutional neural networks (CNNs) with excellent spatial feature extraction abilities have become popular in remote sensing (RS) image change detection (CD). However, CNNs often focus on the extraction of spatial information but ignore important spectral and temporal sequences for hyperspectral images (HSIs). In this article, we propose a joint spectral, spatial, and temporal transformer for hyperspectral

Due to inadequate energy captured by the hyperspectral camera sensor in poor illumination conditions, low-light hyperspectral images (LHSIs) usually suffer from low visibility, spectral distortion, and various noises. A range of hyperspectral image (HSI) restoration methods have been developed, yet their effectiveness in enhancing low-light HSIs is constrained. This work focuses on the low-light HSI

The fusion of a low-spatial-and-high-spectral resolution hyperspectral image (HSI) with a high-spatial-and-low-spectral resolution multispectral image (MSI) allows synthesizing a high-resolution image (HRI), supporting remote sensing applications, such as disaster management, material identification, and precision agriculture. Unlike existing variational methods using low-rank regularizations separately

When segmenting the continuous proliferation of unlabeled remotely sensed images, unsupervised domain adaptation (UDA) has become one of the most critical techniques and achieved significant performance. But, in fact, there still exists a large performance gap between the existing UDA frameworks and supervised learning methods, for the majority of UDA frameworks do not consider the intra-domain gap

Three-dimensional object detection from light detection and ranging (LiDAR) point clouds has gained great attention in recent years due to its wide applications in smart cities and autonomous driving. Cascade framework shows its advancement in 2-D object detection but is less investigated in 3-D space. Conventional cascade structures use multiple separate subnetworks to sequentially refine region proposals

Change detection is a long-standing and challenging problem in remote sensing. Very often, features about changes are difficult to model beforehand, thus making the collection of changed samples a challenging task. In comparison, it is much easier to collect numerous no-change samples. It is possible to define a change detection approach using only easily available annotated no-change samples, which

Quasi-seismic imaging is a popular form of electromagnetic (EM) imaging technology. In most cases, it is considered that it will yield more obvious layered characteristics if the EM response is transformed into the virtual wave domain. However, the time profile of the virtual wave cannot effectively show the underground EM structure before migration. In this article, we make an improvement to process

The 3-D point cloud is a common 3-D data representation that has received increasing attention for remote sensing applications. However, processing 3-D point cloud semantics, especially local semantic information, has always been a challenge and has attracted much attention. In this article, we propose a novel enhanced local semantic learning transformer for 3-D point cloud analysis, which aims to

Global land cover (GLC) products can be utilized to provide geographical supervision for remote sensing representation learning, which has significantly improved downstream tasks’ performance and decreased the demand of manual annotations. However, the time differences between remote sensing images and GLC products may introduce deviations in geographical supervision. In this article, we propose a

Generative adversarial network (GAN) has been rapidly developed because of its powerful generating ability. However, imbalanced class distribution of hyperspectral images (HSIs) easily causes mode collapse in GAN. Moreover, limited training samples in HSIs restrict the generating ability of GAN. These issues may further deteriorate the classification performance of the discriminator. To conquer these

Hyperspectral images (HSIs) can finely discriminate distinct objects with a high spectral resolution, and they are widely employed in various applications. However, mixed noise severely degrades the quality of HSI and restricts the performance of subsequent tasks. As one of the critical preprocessing steps, HSI denoising has been developed rapidly, among which low-rank (LR) prior-based methods have

Hyperspectral image (HSI) clustering is an extremely fundamental but challenging task with no labeled samples. Deep clustering methods have attracted increasing attention and have achieved remarkable success in HSI classification. However, most existing clustering methods are ineffective for large-scale HSI, due to their poor robustness, adaptability, and feature presentation. In this article, to address

The Hydrometeorological Center of Russia receives agrometeorological information from about 950 stations one time per ten days and the remote sensing Advanced Scatterometer (ASCAT) data from three Meteorological Operational (MetOp) satellites. We suggest a combined objective analysis (OA) of the available water content based on the available water content measurements at agrometeorological stations