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The possibility to apply passive radar sounding techniques to the Jovian icy moons, making use of Jupiter’s strong decametric emissions (DAMs), has recently garnered large research interest. In this article, we propose a radar sounding simulation approach being able to simulate and compare passive and active acquisitions for a given scenario. The proposed simulator is based on the Stratton–Chu integral

This article examined the spectral interference by heavy metal on the spectral signal of moisture content of heavy metal contaminated soils. Soil samples were collected from an abandoned mine area, and the chemical analysis revealed extremely high contamination amount of copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), and lead (Pb). The mineralogical analysis showed that the spectral signature

The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the Aqua and Terra satellites have been operated for nearly two decades, producing high-quality earth observation data sets suitable for a broad range of scientific studies regarding the earth’s land, ocean, and atmospheric processes. The high radiometric accuracy of MODIS reflective solar band (RSB) calibration has also

Hyperspectral images (HSIs) are typical high-dimensional and complex data. As such, the clustering of HSIs is a challenging task. Out of the motivation to find the low-dimensional structure representation of the high-dimensional data, sparse subspace clustering (SSC) methods have been proposed in recent studies. Sparse representation is an important technique in SSC, which is aimed at obtaining the

Sensor instability, dark currents, and other factors often cause stripe noise corruption in hyperspectral remote sensing images and severely limit their application in practical purposes. Previous studies have proposed numerous destriping algorithms that have yielded impressive results. Although most destriping algorithms are based on the premise of additive noise, a few studies have focused directly

Deep convolutional neural networks (DCNNs) have attracted much attention in remote sensing recently. Compared with the large-scale annotated data set in natural images, the lack of labeled data in remote sensing becomes an obstacle to train a deep network very well, especially in synthetic aperture radar (SAR) image interpretation. Transfer learning provides an effective way to solve this problem by

Polarimetric synthetic aperture radar (PolSAR) as a typical multi-channel sensor can obtain refined geometrical and geophysical information. In the PolSAR land cover classification task, feature extraction is regarded as a critical step for the final classification. It can employ multi-modal features from the original scattering data, polarimetric target decomposition, and other transformation space

Theoretically, hyperspectral images (HSIs) are capable of providing subtle spectral differences between different materials, but in fact, it is difficult to distinguish between background and anomalies because the samples of anomalous pixels in HSIs are limited and susceptible to background and noise. To explore the discriminant features, a spectral adversarial feature learning (SAFL) architecture

The accuracy of a physically based passive microwave precipitation retrieval algorithm is affected by the quality of the a priori knowledge it employs, which indicates the relationship between the precipitation information obtained from cloud-resolving models (CRMs) and the simulated brightness temperatures (TBs) from radiative transfer models. As various microphysical assumptions reflecting a wide

Polarimetric symmetry, including reflection symmetry, rotation symmetry, and azimuthal symmetry, are useful concepts for describing the scattering characteristics of polarimetric synthetic aperture radar (SAR) data. In this study, for the coherency matrices of multilook polarimetric SAR data, three new scattering models were first proposed based on the polarimetric symmetry. Then, a four-component

This article proposes a novel fusing filter algorithm based on a surface variation anisotropic diffusion (SVAD) filter and a ratio image filter to achieve good speckle reduction and edge preservation. The proposed algorithm can be divided into three steps. First, the proposed SVAD filter effectively calculates the diffusion coefficient of each pixel to obtain filtering results on different scales.

This article proposes an efficient and adaptive statistical superpixel merging approach with edge penalty for polarimetric synthetic aperture radar (PolSAR) image segmentation. Based on the initial superpixel over-segmentation result obtained by our previously proposed adaptive polarimetric superpixel generation algorithm (Pol-ASLIC), this work achieves efficient and accurate PolSAR image segmentation

Lower band ultrawideband (UWB) Doppler radar is promising for through-wall imaging, e.g., human body detection in rescue scenarios. The inherent problem with pulse-Doppler radar is the tradeoff between the Doppler velocity resolution and the resulting temporal resolution that makes it difficult to conduct real-time target tracking, because the separation of micro-Doppler velocities of the human body

Hyperspectral Image (HSI) visualization, which aims at displaying as much material information of original images as possible on a trichromatic monitor with natural color, plays an important role in image interpretation and analysis. However, most of the HSI visualization methods only focus on presenting the detail information of a scene without providing natural colors and distinguishing land covers

The objective of this article is to investigate a multiphase segmentation framework for synthetic aperture radar (SAR) images, which is proposed based on the idea of the fuzzy region competition-based method. The fuzzy region competition-based framework is highly efficient and can attain good segmentation performances for conventional images. The framework is achieved based on its convexity, which

Consistent estimation of water surface area from remote sensing remains challenging in regions such as South Asia with vegetation, mountainous topography, and persistent monsoonal cloud cover. High-resolution optical imagery, which is often used for global inundation mapping, is highly impacted by clouds, while synthetic aperture radar (SAR) imagery is not impacted by clouds and is affected by both

Unsupervised change detection based on supervised or semisupervised classifiers has achieved strong adaptability and robustness to obtain satisfactory change detection results. However, these methods suffer from an issue that it is hard to collect reliable training samples in an unsupervised manner. In this article, a group self-paced learning (GSPL) framework is proposed to mine the reliable training

Spaceborne scatterometers for ocean surface winds usually operate in Ku- or C-band. Rather strict quality control (QC) procedures are included in the Ku-band wind retrieval chain for labeling rain-contaminated observations. Existing QC factors represent the deviation of measurements from the wind geophysical model function (GMF) modeled measurement surface. Other QC indicators flag outliers by examining

Cross-domain scene classification refers to the scene classification task in which the training set (termed source domain) and the test set (termed target domain) come from different distributions. Various domain adaptation methods have been developed to reduce the distribution discrepancy between different domains. However, current domain adaptation methods assume that the source domain and target

Denoising is a common preprocessing step prior to the analysis and interpretation of hyperspectral images (HSIs). However, the vast majority of methods typically adopted for HSI denoising exploit architectures originally developed for grayscale or RGB images, exhibiting limitations when processing high-dimensional HSI data cubes. In particular, traditional methods do not take into account the high

Traditional point cloud registration methods require large overlap between scans, which imposes strict constraints on data acquisition. To facilitate registration, users have to carefully position scanners to ensure sufficient overlap. In this article, we propose to use high-level structural information (i.e., plane/line features and their interrelationship) for registration, which is capable of registering

The $2\times 2$ relative scattering matrix $[S]$ is characterized by five elements (three amplitudes and two relative phases). A more suitable representation of the data in terms of power expression for target identification exists in the $3\times 3$ Pauli-based polarimetric coherency matrix $[T]$ . The aim of this work is to expand the physical interpretation of $[T]$ elements in terms of a physical

Combining spectral and spatial features in hyperspectral image classification is a common practice due to the improvements in classification accuracy that can be obtained by extracting information from neighboring pixels. However, the resulting high dimensionality of the input data and the typically limited number of labeled samples are two key challenges that affect the overall performance of supervised

The availability of global high-resolution land cover maps provides promising a priori knowledge for characterizing subpixel heterogeneity and improving predictions of directional reflectance of coarse-resolution pixels. Due to mutual shadowing and sheltering effects between the adjacent forest and cropland patches, the spectral nonlinear mixing of patchy ecotones is significant, especially when the

LuTan-1 (LT-1) refers to an innovative mission of spaceborne bistatic synthetic aperture radar (BiSAR) adopting the phase synchronization scheme of pulse exchange, which is scheduled to be launched in 2020. In this mission, the multipath effect caused by the reflection of satellites and Doppler frequency shift resulting from the relative motion between satellites constitute two critically latent factors

During the saline-alkali land improvement process, the soil conductivities throughout the whole land area are usually investigated in advance to grade the soil salinization. As a new soil investigating technique, ground penetrating radar (GPR) has been widely used in geographical and agricultural applications. Nevertheless, there are still challenges in applying GPR to saline-alkali soil conductivity

The representation power of convolutional neural network (CNN) models for hyperspectral image (HSI) analysis is in practice limited by the available amount of the labeled samples, which is often insufficient to sustain deep networks with many parameters. We propose a novel approach to boost the network representation power with a two-stream 2-D CNN architecture. The proposed method extracts simultaneously

On account of the remarkable performance of convolutional neural network (CNN) features for natural image searches, utilizing it for other images collected with the anamorphic lens has become a research hotspot. This article selects the aurora images generated from a circular fisheye lens as a typical example. By considering the imaging principle and geomagnetic information, a saliency-weighted region

Separating structural components is important but also challenging for plant phenotyping and precision agriculture. Light detection and ranging (LiDAR) technology can potentially overcome these difficulties by providing high quality data. However, there are difficulties in automatically classifying and segmenting components of interest. Deep learning can extract complex features, but it is mostly used

Detecting moving objects from ground-based videos is commonly achieved by using background subtraction (BS) techniques. Low-rank matrix decomposition inspires a set of state-of-the-art approaches for this task. It is integrated with structured sparsity regularization to achieve BS in the developed method of low-rank and structured sparse decomposition (LSD). However, when this method is applied to

Owing to the large range cell migration (RCM) and fast time-variant Doppler frequency modulation (DFM) generated by rapidly spinning targets, it is difficult to efficiently obtain well-focused inverse synthetic aperture radar (ISAR) images via conventional algorithms because of the multidimensional search requirement. Inspired by the inherent azimuth spatial invariance in strip-map synthetic aperture

Ship detection is important for a wide range of applications. In this article, an airborne passive interferometric microwave sensor (PIMS) is proposed as a powerful complementary tool for ship detection. The fundamental of ship detection by the airborne PIMS is introduced. A specification called “the detection factor” is addressed to quantitatively assess the performance of the airborne PIMS for ship

Four-band multispectral remote sensing imagery is widely used for a variety of purposes and has a long historical data record. However, most of the existing bathymetry inversion methods are either unable to determine the optimal solution through theoretical or semianalytical models due to the limited number of bands, or their application is limited by the difficulty in obtaining in situ data. In this

The ability to detect oil spills remotely is important in marine environmental monitoring. The optical polarization remote sensing has the unique advantage of inversion of refractive index of spilled oils which is the key parameter for calculation of sunglint reflectance. Compared to nonpolarization optical image, the degree of linear polarization (DOLP) of spilled oil’s sunglint depends on the refractive

Change detection by comparing two bitemporal images is one of the most fundamental challenges for dynamic monitoring of the Earth surface. In this article, we propose a metric learning-based generative adversarial network (GAN) (MeGAN) to automatically explore seasonal invariant features for pseudochange suppressing and real change detection. To achieve this purpose, a seasonal invariant term is introduced

We use full-wave simulations of rough surfaces to calculate the emissivities of a polar ocean between 0.5 GHz and 2 GHz for applications in ocean salinity. High accuracy is required because the emissivity variations between flat and rough surfaces are at the order of magnitude of $10^{-3}$ . In addition, the changes of emissivity due to salinity change are only about $4.2\times 10^{-4}$ /psu to $2

Compact high-frequency (HF) antenna arrays are convenient to deploy. However, using a small-aperture HF surface wave radar for wind direction measurement is still a challenging problem, since an unsatisfactory array pattern degrades the performance of Bragg ratio estimation. To address this issue, a digital beamforming method based on a superdirective synthesis technique for an HF receiving array that

In remote sensing, each sensor can provide complementary or reinforcing information. It is valuable to fuse outputs from multiple sensors to boost overall performance. Previous supervised fusion methods often require accurate labels for each pixel in the training data. However, in many remote-sensing applications, pixel-level labels are difficult or infeasible to obtain. In addition, outputs from multiple

Recently, high-frequency surface wave radar (HFSWR) has been widely applied in ocean surface dynamic parameter measurement. However, the radar echoes backscattered from ocean surface tend to be contaminated by the external radio frequency interference (RFI), and the mitigation of RFI becomes an intractable problem, especially for wide beam HFSWR. The HFSWR measured data are multichannel. The higher-order

Soil moisture is a pivotal hydrological variable that links the terrestrial water, energy, and carbon cycles. In this article, a new soil moisture (SM) index (SMI), which aims to capture the temporal variability of SM, irrespective of cloud cover and solar illumination, was developed by using the L-band SM active passive (SMAP) radiometer observations. The SMI was proposed on the basis of two key foundations:

Practical and accurate estimation of 3-D wind fields is an ongoing challenge in radar meteorology. Multistatic (single transmitter/multiple receivers) radar architectures offer a cost-effective solution for obtaining the multiple Doppler measurements necessary to achieve such estimates. Existing multistatic weather radars, while less costly than comparable monostatic networks, have been constrained

The greatest advantage of a satellite-borne microwave radiometer (MWR) is that it can obtain information on the Earth’s surface with less influence by rain and clouds compared to an infrared radiometer; however, it has the disadvantage that the observing area is large so the spatial resolution is low. One example is the Advanced Microwave Scanning Radiometer-2 (AMSR2) loaded on the GCOM-W satellite

Hyperspectral unmixing (HU) is an active research topic in the remote-sensing community. It aims at modeling mixed pixels using a collection of pure constituent materials ( endmembers ) weighted by their corresponding fractional abundances. Among existing unmixing schemes, nonnegative matrix factorization (NMF) has drawn significant attention due to its unsupervised nature, as well as its capacity

The semiempirical kernel-driven model is commonly used for global surface reflectance characterization because of its simplicity and underlying physical meaning. However, the current kernel-driven reflectance models assume that the terrain is flat and homogeneous, and can induce significant errors in the surface reflectance estimation and subsequent parameter retrievals over rugged terrain. In this

With the rapid development of Earth observation technology, very-high-resolution (VHR) images from various satellite sensors are more available, which greatly enrich the data source of change detection (CD). Multisource multitemporal images can provide abundant information on observed landscapes with various physical and material views, and it is exigent to develop efficient techniques to utilize these

In this article, we proposed a thick cloud removal method for remote-sensing imagery based on multisource estimation. A convolutional-mapping-deconvolutional (CMD) network is proposed to estimate the cloud-free image directly from multisource reference images. Synthetic aperture radar (SAR) image and low-resolution heterogeneous (LRH) image, namely, image from a different optical sensor with lower

Stripe noise from different remote sensing imaging systems varies considerably in terms of response, length, angle, and periodicity. Due to the complex distributions of different stripes, the destriping results of previous methods may be oversmoothed or contain residual stripe. To overcome this key problem, we provide a comprehensive analysis of existing destriping methods and propose a deep convolutional

Multiple-input multiple-output (MIMO) radar imaging is a new technique to obtain the radar image of aerospace targets. Orthogonal waveform design is one of the important issues for MIMO radar imaging. However, the fully orthogonal waveforms in the same frequency and with the arbitrary time delay do not exist in practice. Thus, the imaging result using nonorthogonal waveforms based on matched filtering

Elastic full waveform inversion (FWI) is more suitable to process multicomponent seismic data and can provide more subsurface medium information than acoustic FWI often with lower efficiency. Except for the parallel algorithms, source-encoding methods are usually adopted to improve the efficiency of FWI, but it often includes crosstalk noise. Besides, the additional source estimation process, critical