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With the assumption of densely distributed vegetation in PolInSAR processing, the height parameters can be inversed by the conventional random volume over ground (RVoG) method. During the procedure of RVoG, the samples used to estimate the PolInSAR coherence are selected directly from the neighbouring areas. However, for sparsely distributed vegetation, the scattering statistics are different from those of densely distributed vegetation. Therefore, the inversion performance will be deteriorated if the samples are selected directly in the neighbouring areas. A new phase‐based method is proposed to select samples, whose positions represent the volume scattering pixels, for sparsely distributed vegetation height inversion. By analysing the scattering characteristics of sparsely distributed vegetation, the processing data vector is formulated based on the amplitude‐normalised interferograms. With the PolSAR classification, the generalised inner product (GIP) is iteratively used to select the non‐local samples based on the formulated phase data vectors, which are utilised for sparsely distributed vegetation height inversion. For the selected sample sets, the vegetation distribution can be approximately regarded as "dense distribution", and then the vegetation height parameters can be inversed by RVoG method. Compared to the height inversion performance based on different sample selection methods, the effectiveness of the proposed method is validated by the PolSARPro simulated data and the real airborne L‐band PolInSAR data. [ABSTRACT FROM AUTHOR]

Spaceborne interferometric synthetic aperture radar (InSAR) technology is an effective method to obtain digital elevation model (DEM) data. The bistatic InSAR configuration of the inclined‐geosynchronous (InGEO) transmitter with low earth orbit (LEO) receivers (InGEO‐LEO) is a novel InSAR system to acquire terrain information. This novel system is characterised with high resolution, wide swath and timeliness, but the Newton iterative method is time‐consuming to solve the bistatic InSAR equations for fast DEM generation. For the conventional LEO bistatic InSAR system, the closed‐form solution is an effective method to improve the efficiency of solving InSAR equations, which is invalid in the InGEO‐LEO InSAR system because of the significant geometry difference caused by the orbits of InGEO transmitter and LEO receivers. To address this issue, we analyse the bistatic InGEO‐LEO geometry in detail and exploit the bistatic InSAR equations to propose an approximate closed‐form solution (ACS) for the novel system. Compared with the general Newton iterative method, the ACS significantly improves the efficiency of geolocation for the bistatic InGEO‐LEO InSAR system with high precision. Simulation experiments are carried out to verify the effectiveness and superiority of ACS. [ABSTRACT FROM AUTHOR]

With the multi‐function need for airborne space‐time adaptive processing (STAP) radar, range synthesis is usually necessary for the follow‐up function of target recognition. Utilising the excellent performance on orthogonality and bandwidth synthesis of the frequency division multiple access (FDMA) waveform and the longer accumulation time of multiple‐input multiple‐output (MIMO) radar, a robust low‐sidelobe target synthesis method for airborne FDMA–MIMO STAP radar is developed. The spectra characteristics of target and clutter are first analysed. Then, the keystone transform is employed to mitigate velocity migration. To avoid gain fluctuation induced by multiple independent adaptive weight vectors, the robust STAP beamformer is developed. Finally, to decouple the range–angle coupling relationship resulted from the FDMA waveform, angle compensation is added in the robust STAP beamformer. Numerical examples are given to demonstrate the effectiveness of the presented method. [ABSTRACT FROM AUTHOR]

In multi‐channel high‐resolution and wide‐swath (HRWS) synthetic aperture radar (SAR) processing, range sampling time errors and phase mismatches severely degrade the reconstruction performance of unambiguous Doppler spectrum. To address this problem, an improved channel mismatch estimation algorithm for multi‐channel HRWS SAR is presented in this Letter. The technique of combining phase wrapping and weighted least‐squares fitting is capable of realising a robust estimation of range sampling time errors and constant phases between adjacent channels. Then, Doppler centroid as well as channel phase mismatches can be estimated from these constant phases based on the theory of spatial cross‐correlation coefficient (SCCC). Compared to the conventional algorithm, the improved performance can be achieved by using the proposed two‐step procedure. The effectiveness of the proposed algorithm is confirmed by experimental results based on airborne real data. [ABSTRACT FROM AUTHOR]

3D scene reconstruction from single‐pass circular synthetic aperture radar (CSAR) data has been a research hot spot in various remote sensing applications. Among existing literature, radargrammetry techniques and model assistant methods are two dominant approaches. However, the performance of these methods is limited in practical applications: radargrammetry techniques obtain merely one height value along vertical; model assistant approaches only work when valid models are given. A single‐pass CSAR 3D scene point cloud reconstruction method is proposed. First, the connection between an object and its projections in multi‐aspect SAR images is established with no auxiliary model. On the basis of the connection, voxels are classified into inner voxels (voxels beneath the target profile) and outside voxels (voxels outside of the target profile). Discarded all the outside voxels, the remained inner voxels are combined to form the 3D point cloud of the target. It is similar to the sculpturing process from a raw material to a piece of craft. Finally, a 3D scene is retrieved through assembling all reconstructed targets into a unified coordinate system. The feasibility of the proposed method is validated by the Gotcha Volumetric SAR Data Set. [ABSTRACT FROM AUTHOR]

Since the terrain slope cannot be neglected for forest height inversion with polarimetric synthetic aperture radar interferometry (PolInSAR), a sloped random volume over ground (S‐RVoG) model is proposed to correct the terrain distortion for forest parameters estimation. A significant model complexity reduction is achieved by aligning the reference frame along the local terrain slope and changing the corresponding radar geometrical configuration. The proposed S‐RVoG model inversion promises to provide much more accurate estimation of forest parameters and is validated with L‐band PolInSAR data produced by PolSARpro software developed by the European Space Agency (ESA). [ABSTRACT FROM AUTHOR]