Xiao Xiang Zhu

Tomographic SAR Inversion by

Synthetic aperture radar (SAR) tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. The resolution in the elevation direction depends on the size of the elevation aperture, i.e., on the spread of orbit tracks. Since the orbits of modern meter-resolution spaceborne SAR systems, like TerraSAR-X, are tightly controlled, the tomographic elevation resolution is at least an order of magnitude lower than in range and azimuth. Hence, super-resolution reconstruction algorithms are desired. The high anisotropy of the 3-D tomographic resolution element renders the signals sparse in the elevation direction; only a few pointlike reflections are expected per azimuth-range cell. This property suggests using compressive sensing (CS) methods for tomographic reconstruction. This paper presents the theory of 4-D (differential, i.e., space-time) CS TomoSAR and compares it with parametric (nonlinear least squares) and nonparametric (singular value decomposition) reconstruction methods. Super-resolution properties and point localization accuracies are demonstrated using simulations and real data. A CS reconstruction of a building complex from TerraSAR-X spotlight data is presented.

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Data provided by most optical Earth observation satellites such as IKONOS, QuickBird, and GeoEye are composed of a panchromatic channel of high spatial resolution (HR) and several multispectral channels at a lower spatial resolution (LR). The fusion of an HR panchromatic and the corresponding LR spectral channels is called “pan-sharpening.” It aims at obtaining an HR multispectral image. In this paper, we propose a new pan-sharpening method named Sparse F usion of Images (SparseFI, pronounced as “sparsify”). SparseFI is based on the compressive sensing theory and explores the sparse representation of HR/LR multispectral image patches in the dictionary pairs cotrained from the panchromatic image and its downsampled LR version. Compared with conventional methods, it “learns” from, i.e., adapts itself to, the data and has generally better performance than existing methods. Due to the fact that the SparseFI method does not assume any spectral composition model of the panchromatic image and due to the super-resolution capability and robustness of sparse signal reconstruction algorithms, it gives higher spatial resolution and, in most cases, less spectral distortion compared with the conventional methods.

-Norm Regularization—The Compressive Sensing Approach

Tomographic synthetic aperture radar (SAR) inversion, including SAR tomography and differential SAR tomography, is essentially a spectral analysis problem. The resolution in the elevation direction depends on the elevation aperture size, i.e., on the spread of orbit tracks. Since the orbits of modern meter-resolution spaceborne SAR systems, such as TerraSAR-X, are tightly controlled, the tomographic elevation resolution is at least an order of magnitude lower than in range and azimuth. Hence, super-resolution (SR) reconstruction algorithms are desired. Considering the sparsity of the signal in elevation, a compressive sensing based super-resolving algorithm, named “Scale-down by L1 norm Minimization, Model selection, and Estimation Reconstruction” (SL1MMER, pronounced “slimmer”), was proposed by the authors in a previous paper. The ultimate bounds of the technique on localization accuracy and SR power were investigated. In this paper, the essential role of SR for layover separation in urban infrastructure monitoring is indicated by geometric and statistical analysis. It is shown that double scatterers with small elevation distances are more frequent than those with large elevation distances. Furthermore, the SR capability of SL1MMER is demonstrated using TerraSAR-X real data examples. For a high rise building complex, the percentage of detected double scatterers is almost doubled compared to classical linear estimators. Among them, half of the detected double scatterer pairs have elevation distances smaller than the Rayleigh elevation resolution. This confirms the importance of SR for this type of applications.

A Sparse Image Fusion Algorithm With Application to Pan-Sharpening

Layover is frequent in imaging and monitoring with synthetic aperture radar (SAR) areas characterized by a high density of scatterers with steep topography, e.g., in urban environment. Using medium-resolution SAR data tomographic techniques has been proven to be capable of separating multiple scatterers interfering (in layover) in the same pixel. With the advent of the new generation of high-resolution sensors, the layover effect on buildings becomes more evident. In this letter, we exploit the potential of the 4-D imaging applied to a set of TerraSAR-X spotlight acquisitions. Results show that the combination of high-resolution data and advanced coherent processing techniques can lead to impressive reconstruction and monitoring capabilities of the whole 3-D structure of buildings.

Demonstration of Super-Resolution for Tomographic SAR Imaging in Urban Environment

In the differential synthetic aperture radar tomography (D-TomoSAR) system model, the motion history appears as a phase term. In the case of nonlinear motion, this phase term is no longer linear and, hence, cannot be retrieved by spectral estimation. We propose the “time warp” method that rearranges the acquisition dates such that a linear motion is pretended. The multicomponent generalization of time warp rewrites the D-TomoSAR system model to an (M + 1)-dimensional standard spectral estimation problem, where M indicates the user-defined motion model order and, hence, enables the motion estimation for all possible complex motion models. Both simulations and real data (from TerraSAR-X spotlight) examples demonstrate the applicability of the method and show that linear and periodic (seasonal) motion components can be separated and retrieved.

Tomographic Imaging and Monitoring of Buildings With Very High Resolution SAR Data

The new class of high resolution spaceborne SAR systems, like TerraSAR-X and COSMO-Skymed opens new possibilities for SAR interferometry. The 1m resolution is particularly helpful when 2D, 2.5D, 3D, or 4D (space-time) imaging of buildings and urban infrastructure is required, where the non-interferometric interpretation of SAR imagery is difficult. Structure and defor-mation of individual buildings can be mapped, rather than only coarse deformation patterns of areas. The paper demonstrates several new developments in high resolution SAR interferometry using Ter-raSAR-X as an example. Of particular interest is the very high resolution spotlight mode, which requires some care in interferometric processing. Results from interferometry, Persistent Scatterer Interferometry (PSI), and tomographic SAR in urban environment are presented. The high resolution of TerraSAR-X also supports accurate speckle and feature tracking. An example of glacier monitoring is shown and discussed.

Let's Do the Time Warp: Multicomponent Nonlinear Motion Estimation in Differential SAR Tomography

Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage remote-sensing scientists to bring their expertise into deep learning and use it as an implicit general model to tackle unprecedented, large-scale, influential challenges, such as climate change and urbanization.

Interferometric Potential of High Resolution Spaceborne SAR

With reference to the current status of VHR spaceborne tomographic SAR inversion presented in this article, the following conclusions can be drawn: VHR tomographic SAR inversion is able to reconstruct the shape and motion of individual buildings and entire city areas. SR is crucial and possible, e.g., using CS, for VHR tomographic SAR inversion for urban infrastructure. The motion or deformation of buildings is often nonlinear (periodic, accelerating, stepwise, etc.). Multicomponent nonlinear motion of multiple scatterers can be separated. The 4-D point clouds retrieved by VHR TomoSAR has a point density comparable to LiDAR and can be potentially used for dynamic city model reconstruction.

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Recent advances in very high resolution tomographic synthetic aperture radar inversion (TomoSAR) using multiple data stacks from different viewing angles enables us to generate 4-D (space-time) point clouds of the illuminated area from space with a point density comparable to LiDAR. They can be potentially used for facade reconstruction and deformation monitoring in urban environment. In this paper, we present the first attempt to reconstruct facades from this class of data: First, the facade region is extracted using the density estimates of the points projected to the ground plane, the extracted facade points are then clustered into individual facades by means of orientation analysis, surface (flat or curved) model parameters of the segmented building facades are further estimated, and the geometric primitives such as intersection points of the adjacent facades are determined to complete the reconstruction process. The proposed approach is illustrated and validated by examples using TomoSAR point clouds generated from stacks of TerraSAR-X high-resolution spotlight images from two viewing angles, i.e., both ascending and descending orbits. The performance of the proposed approach is systematically analyzed. To explore the possible applications, we refine the elevation estimate of each raw TomoSAR point by using its more accurate azimuth and range coordinates and the corresponding reconstructed building facade model. Compared to the raw TomoSAR point clouds, significantly improved elevation positioning accuracy is achieved. Finally, a first example of the reconstructed 4-D city model is presented.

Superresolving SAR Tomography for Multidimensional Imaging of Urban Areas: Compressive sensing-based TomoSAR inversion

In recent years, vector-based machine learning algorithms, such as random forests, support vector machines, and 1-D convolutional neural networks, have shown promising results in hyperspectral image classification. Such methodologies, nevertheless, can lead to information loss in representing hyperspectral pixels, which intrinsically have a sequence-based data structure. A recurrent neural network (RNN), an important branch of the deep learning family, is mainly designed to handle sequential data. Can sequence-based RNN be an effective method of hyperspectral image classification? In this paper, we propose a novel RNN model that can effectively analyze hyperspectral pixels as sequential data and then determine information categories via network reasoning. As far as we know, this is the first time that an RNN framework has been proposed for hyperspectral image classification. Specifically, our RNN makes use of a newly proposed activation function, parametric rectified tanh (PRetanh), for hyperspectral sequential data analysis instead of the popular tanh or rectified linear unit. The proposed activation function makes it possible to use fairly high learning rates without the risk of divergence during the training procedure. Moreover, a modified gated recurrent unit, which uses PRetanh for hidden representation, is adopted to construct the recurrent layer in our network to efficiently process hyperspectral data and reduce the total number of parameters. Experimental results on three airborne hyperspectral images suggest competitive performance in the proposed mode. In addition, the proposed network architecture opens a new window for future research, showcasing the huge potential of deep recurrent networks for hyperspectral data analysis.