IEEE Transactions on Geoscience and Remote Sensing

The 3-D seismic data reconstruction can be understood as an underdetermined inverse problem, and thus, some additional constraints need to be provided to achieve reasonable results. A prevalent scheme in 3-D seismic data reconstruction is to compute the best low-rank approximation of a formulated Hankel matrix by rank-reduction methods with a rank constraint. However, the predefined Hankel structure is easily damaged by the low-rank approximation, which leads to harming its recovery performance. In this article, we present a structured tensor completion (STC) framework to simultaneously exploit both the Hankel structure and the low-tubal-rank constraint to further enhance the performance. Unfortunately, under the assumption of elementwise sampling used by existing methods, STC is intractable to be solved since Hankel constraints cannot be expressed as linear tensor equations. Instead, tubal sampling is proposed to describe the missing trace behavior more accurately and further build a bridge between tensor and matrix completion (MC) to overcome the solving issue in two aspects: through the bridge from tensor to MC, STC can be solved efficiently using MC from random samplings of each frontal slice in the Fourier domain. Through the bridge from matrix to tensor completion, various tensor models within the framework can be developed from noise-specific MC to meet the need for data reconstruction in changeable noise environments. Moreover, alternating-minimization and alternating-direction methods of multipliers are developed to solve the proposed STC. The superior performance of STC is demonstrated in both synthetic and field seismic data.

Full waveform inversion (FWI) is a powerful tool to understand the real complicated earth model. As FWI is a highly nonlinear problem and depends strongly on the initial model, how to effectively retrieve the large-scale background model is critical for the success of FWI. For elastic FWI (EFWI), the inversion challenge increases because the P-wave and S-wave are coupled together if no mode separation technologies are applied. In this article, we develop a new EFWI strategy, where we simultaneously implement the angle decomposition and mode separation for the wavefield. Based on the analysis of radiation patterns of different parameters and the fact that small scattering angles correspond to large-scale model perturbations, we can retrieve the large-scale background model of the P-wave velocity with pure small scattering angle P-P mode wavefield. On the other hand, the pure small scattering angle S-S, S-P, and P-S mode wavefields are used to estimate the large-scale background model of the S-wave velocity. The correctly retrieved large-scale background models further guarantee the success of subsequent fine structure retrieving for the P- and S-wave velocity models by using different wave modes. The proposed method is able to reduce the cycle-skipping problem and the multiparameter crosstalk problem simultaneously. Numerical examples show that the proposed method provides much improved inversion results than the conventional EFWI, which demonstrates the validity of the proposed method.

Seismic image, especially for the prestack image, performs a blurred version of the reflectivity image due to spatial aliasing, poor acquisition aperture, and nonuniform illumination. The blurring effects can be quantified by the point spread function (PSF). We herein adopt an explicit space-variant PSF formula, which can be defined as a sequential application of the modeling and migration operators with the asymptotic Green’s function. The deblurred images are restored using the nonstationary deconvolution with total variation regularization in which the blurred images are described by the convolution between the space-variant PSF and the reflectivity image. However, nonstationary deconvolution is computationally challenging. We introduce an extending primal–dual hybrid gradient (E-PDHG) method to decompose the complex problem into a sequence of simple subproblems that have closed-form solutions. Numerical results on synthetic data and field data demonstrate that the proposed E-PDHG method outperforms the basic PDHG method in the prestack seismic image deblurring.

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Over the past few years making use of deep networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), classifying hyperspectral images has progressed significantly and gained increasing attention. In spite of being successful, these networks need an adequate supply of labeled training instances for supervised learning, which, however, is quite costly to collect. On the other hand, unlabeled data can be accessed in almost arbitrary amounts. Hence it would be conceptually of great interest to explore networks that are able to exploit labeled and unlabeled data simultaneously for hyperspectral image classification. In this article, we propose a novel graph-based semisupervised network called nonlocal graph convolutional network (nonlocal GCN). Unlike existing CNNs and RNNs that receive pixels or patches of a hyperspectral image as inputs, this network takes the whole image (including both labeled and unlabeled data) in. More specifically, a nonlocal graph is first calculated. Given this graph representation, a couple of graph convolutional layers are used to extract features. Finally, the semisupervised learning of the network is done by using a cross-entropy error over all labeled instances. Note that the nonlocal GCN is end-to-end trainable. We demonstrate in extensive experiments that compared with state-of-the-art spectral classifiers and spectral–spatial classification networks, the nonlocal GCN is able to offer competitive results and high-quality classification maps (with fine boundaries and without noisy scattered points of misclassification).

Two leaf optical property models, PROSPECT-D and ABM-B, were compared to determine their respective parameter sensitivities and to correlate their parameters. ABM-B was used to generate 150 leaf spectra with various input parameters, and the inversion of PROSPECT-D was used to estimate leaf parameters from these spectra. Wavelength-specific sensitivities were described, and correlations were developed between the leaf pigments and structure parameters of the two models. Of particular importance was the correlation of PROSPECTD's structure parameter (N) which is a generalized parameter integrating several leaf-level and cell-level characteristics. At the leaf-level, N showed correlations with the leaf thickness and the mesophyll percentage, and at the cell-level, N was affected by the cell cap aspect ratios defined in ABM-B. The estimated value of N also varied substantially with changes in the angle of incidence specified in ABM-B. All of these correlations were nonlinear, and it is unclear how these parameters are combined to affect the final value for N. The correlations developed in this article indicate that additional structural parameters (possibly separated into leaf-level and cell-level) should be considered in future model development that aims to maintain inversion potential while providing more information about the leaf.

Attenuated backscatter measurements from a Vaisala CL31 ceilometer and a modified form of the well-known slope method are used to derive the ceilometer extinction profiles during rain events, restricted to rainfall rates (RRs) below approximately 10 mm/h. RR estimates from collocated S-band radar and portable disdrometer are used to derive the RR-to-extinction correlation models for the ceilometer-radar and ceilometer-disdrometer combinations. Data were collected during an intensive observation period of the Verification of the Origins of Rotation in Tornadoes Experiment Southeast (VORTEX-SE) conducted in northern Alabama. These models are used to estimate the RR from the ceilometer observations in similar situations that do not have collocated radar or the disdrometer. Such correlation models are, however, limited by the different temporal and spatial resolutions of the measured variables, measurement capabilities of the instruments, and the inherent assumption of a homogeneous atmosphere. An empirical method based on extinction and RR uncertainty scoring and covariance fitting are proposed to solve, in part, these limitations.

In this article, we generate a regional mapping of space-borne carbon dioxide (CO2) concentration through a data fusion approach, including emission estimates and Land Use and Land Cover (LULC) information. NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite measures the column-averaged CO2 dry air mole fraction (XCO2) as contiguous parallelogram footprints. A major hindrance of this data set, specifically with its Level-2 observations, is missing footprints at certain time instants and the sparse sampling density in time. This article aims to generate Level-3 XCO2 maps on a regional scale for different locations worldwide through spatial interpolation of the OCO-2 retrievals. To deal with the sparse OCO-2 sampling, the cokriging-based spatial interpolation methods are suitable, which models auxiliary densely-sampled variables to predict the primary variable. In this article, a cokriging-based approach is applied using auxiliary emission data sets and the principles of the semantic kriging (SemK) method. Two global high-resolution emission data sets, the Open-source Data Inventory for Anthropogenic CO2 (ODIAC) and the Emissions Database for Global Atmospheric Research (EDGAR), are used here. The ontology-based semantic analysis of the SemK method quantifies the interrelationships of LULC classes for analyzing the local XCO2 pattern. Validations have been carried out in different regions worldwide, where the OCO-2 and the Total Carbon Column Observing Network (TCCON) measurements coexist. It is observed that the modeling of auxiliary emission data sets enhances the prediction accuracy of XCO2. This article is one of the initial attempts to generate Level-3 XCO2 mapping of OCO-2 through a data fusion approach using emission data sets.

Semantic segmentation is one of the fundamental tasks in understanding and applying urban scene point clouds. Recently, deep learning has been introduced to the field of point cloud processing. However, compared to images that are characterized by their regular data structure, a point cloud is a set of unordered points, which makes semantic segmentation a challenge. Consequently, the existing deep learning methods for semantic segmentation of point cloud achieve less success than those applied to images. In this article, we propose a novel method for urban scene point cloud semantic segmentation using deep learning. First, we use homogeneous supervoxels to reorganize raw point clouds to effectively reduce the computational complexity and improve the nonuniform distribution. Then, we use supervoxels as basic processing units, which can further expand receptive fields to obtain more descriptive contexts. Next, a sparse autoencoder (SAE) is presented for feature embedding representations of the supervoxels. Subsequently, we propose a regional relation feature reasoning module (RRFRM) inspired by relation reasoning network and design a multiscale regional relation feature segmentation network (MS-RRFSegNet) based on the RRFRM to semantically label supervoxels. Finally, the supervoxel-level inferences are transformed into point-level fine-grained predictions. The proposed framework is evaluated in two open benchmarks (Paris-Lille-3D and Semantic3D). The evaluation results show that the proposed method achieves competitive overall performance and outperforms other related approaches in several object categories. An implementation of our method is available at: https://github.com/HiphonL/MS_RRFSegNet.

The objective of this article is a systematic investigation of the sensitivity of C- and X-band emissions to leaf shape and orientation for various growth stages of corn. To simulate these effects, we used the model developed at Tor Vergata University (TOV model), which is based on a matrix doubling algorithm considering multiple scattering. Corn leaves have specific properties of shape, curvature, and orientation. We have compared different approaches, including segmented elliptical disk oriented following leaf curvature, unique elliptical disk per leaf, and segmented circular disk with size determined by the shorter leaf dimension and following the leaf curvature. Moreover, widespread leaf inclination angle distribution functions combined with in situ measurements of leaf inclination angle are adopted. The scatterers' phase matrix calculations are based on the physical optics approximation. Simulations are conducted with the ground-measured soil and vegetation properties as inputs and evaluated against the corresponding ground-based, multifrequency radiometer observations carried out in four different years over Chinese sites. The investigations show that in most cases the segmented circular disk assumption shows the best correspondence to the measurements over intermediate growth stages when the vegetation heights lie between 50 and 200 cm, and the unique elliptical disk model achieves the best correspondence for the later growth stages when the vegetation heights are larger than 200 cm with prefer-erectophile distribution of leaf orientation. The use of in situ leaf inclination angle measurements can improve the model accuracy by up to 25 K for tall vegetation heights compared with random distribution assumption.

Wintertime Arctic surface emissivities are retrieved from Advanced Technology Microwave Sounder (ATMS) passive microwave measurements at 88.2, 165.5, and 183.31 GHz. Surface emitting layer temperatures are simultaneously retrieved at 183.31 GHz. Random errors in emissivities are estimated to be 2.0%, 2.0%, and 3.5% at 88.2, 165.5, and 183.31 GHz, respectively, and the random errors in surface emitting layer temperatures are 4.3 K. A series of tests on the retrieved products reveal that land and sea ice are Lambertian reflectors and ocean is a specular reflector. The retrieved emissivities show broad agreement with products from published databases, with differences partly due to the uncertainties in surface emitting layer temperatures. The geographical distribution of 165.5/183.31 GHz surface reflectance ratios over land and sea ice, which is important for the retrieval of microwave satellite water vapor column (WVC), is presented. Neglecting the geographical variations leads to random errors in retrieved wintertime Arctic WVCs of approximately 1.8% and 25% in the mid (1.5 - 9 kgm-2) and extended (8 - 15 kgm-2) slant column retrieval regimes, respectively. Choosing specular instead of Lambertian reflection in the surface emissivity retrievals over land and sea ice causes systematic WVC retrieval errors of up to -4.1%.

By analyzing the motion characteristics and the radar observation model of triaxial stabilized space targets, a new 3-D geometry reconstruction method is proposed based on the energy accumulation of inverse synthetic aperture radar (ISAR) image sequence. According to the radar line of sight (LOS), we first construct the projection vectors of the 3-D geometry of a space target on the imaging planes. Then, by projecting the 3-D scatterer candidates on each imaging plane, we can accumulate the scattering energy of the corresponding 2-D projection position in each image. The 3-D scatterer candidates occupying the larger accumulated energy will be reserved as the real scatterers. To improve the efficiency, the real 3-D scatterers will be searched by using the particle swarm optimization (PSO) algorithm one by one. Compared with traditional 3-D geometry reconstruction methods, the proposed one never needs the 2-D scatterer extraction and trajectory association, which remains the challenges in ISAR image processing. Experimental results based on the simulated point target and electromagnetic data are presented to validate the effectiveness and robustness of the proposed method.

Remote sensing image (RSI) classification is one of the most important fields in RSI processing. It is well known that RSIs are very complicated due to its various kinds of contents. Therefore, it is very difficult to distinguish different scene categories with similar visual contents, like desert and bare land. To address hard negative categories, an attribute-cooperated convolutional neural network (ACCNN) is proposed to exploit attributes as additional guiding information. First, the classification branch extracts convolutional neural network feature, which is then utilized to recognize the RSI scene categories. Second, the attribute branch is proposed to make the network distinguish scene categories efficiently. The proposed attribute branch shares feature extraction layers with the classification branch and makes the classification branch aware of extra attribute information. Finally, the relationship branch constraints the relationship between the classification branch and the attribute branch. To exploit the attribute information, three attribute-classification data sets are generated (AC-AID, AC-UCM, and AC-Sydney). Experimental results show that the proposed method is competitive to state-of-the-art methods. The data sets are available at https://github.com/CrazyStoneonRoad/Attribute-Cooperated-Classification-Data sets.

Recently, super-resolution (SR) of satellite videos has received increasing attention as it can overcome the limitation of spatial resolution in applications of satellite videos to dynamic analysis. The low quality of satellite videos presents big challenges to the development of the spatial SR techniques, e.g., accurate motion estimation and motion compensation for multiframe SR. Therefore, reasonable image priors in maximum a posteriori (MAP) framework, where motion information among adjacent frames is involved, are needed to regularize the solution space and generate the corresponding high-resolution frames. In this article, an effective satellite video SR framework based on locally spatiotemporal neighbors and nonlocal similarity modeling is proposed. Firstly, local prior knowledge is represented by means of adaptively exploiting spatiotemporal neighbors. In this way, implicitly local motion information can be captured without explicit motion estimation. Secondly, the nonlocal spatial similarity is integrated into the proposed SR framework to enhance texture details. Finally, the locally spatiotemporal regularization and nonlocal similarity modeling bring out a complex optimization problem, which is solved via the iterated reweighted least squares in the proposed SR framework. The videos from the Jilin-1 satellite and the OVS-1A satellite are used for evaluating the proposed method. Experimental results show that the proposed method demonstrates better SR performance in preserving edges and texture details compared with the-state-of-art video SR methods.

Hyperspectral image (HSI) super-resolution refers to enhancing the spatial resolution of a 3-D image with many spectral bands (slices). It is a seriously ill-posed problem when the low-resolution (LR) HSI is the only input. It is better solved by fusing the LR HSI with a high-resolution (HR) multispectral image (MSI) for a 3-D image with both high spectral and spatial resolution. In this article, we propose a novel nonnegative and nonlocal 4-D tensor dictionary learning-based HSI super-resolution model using group-block sparsity. By grouping similar 3-D image cubes into clusters and then conduct super-resolution cluster by cluster using 4-D tensor structure, we not only preserve the structure but also achieve sparsity within the cluster due to the collection of similar cubes. We use 4-D tensor Tucker decomposition and impose nonnegative constraints on the dictionaries and group-block sparsity. Numerous experiments demonstrate that the proposed model outperforms many state-of-the-art HSI super-resolution methods.