Persistent scatterer interferometry (PSI) has the ability to acquire submeter-scale digital elevation model (DEM) and millimeter-scale deformation. A limitation to the application of PSI is that only single persistent scatterers (SPSs) are detected, and pixels with multiple dominant scatterers from different sources are discarded in PSI processing. Synthetic aperture radar (SAR) tomography is a promising technique capable of resolving layovers. In this paper, new approaches based on a novel two-tier network aimed at robust and efficient detection of persistent scatterers (PSs) are presented. The calibration of atmospheric phase screen (APS) and the detection of PSs can be jointly implemented in the novel two-tier network. A residue-to-signal ratio (RSR) estimator is proposed to evaluate whether the APS is effectively calibrated and to select reliable PSs with accurate estimation. In the first-tier network, a Delaunay triangulation network is constructed for APS calibration and SPS detection. RSR thresholding is used to adjust the first-tier network by discarding arcs and SPS candidates (SPSCs) with inaccurate estimation, yielding more than one main network in the first-tier network. After network adjustment, we attempt to establish reliable SPS arcs to connect the main isolated networks, and the expanded largest connected network is then formed with more manual structure information subtracted. Furthermore, rather than the weighted least square (WLS) estimator, a network decomposition WLS (ND-WLS) estimator is proposed to accelerate the retrieval of absolute parameters from the expanded largest connected network, which is quite useful for large network inversion. In the second-tier network, the remaining SPSs and all the double PSs (DPSs) are detected and estimated with reference to the expanded largest connected network. Compared with traditional two-tier network-based methods, more PSs can be robustly and efficiently detected by the proposed new approaches. Experiments on interferometric high resolution TerraSAR-X SAR images are given to demonstrate the merits of the new approaches.

Severe sidelobe interference is one of the major problems with traditional Synthetic Aperture Radar (SAR) imaging. In the observation scene of sea areas, the number of targets in the observation scene is so small that targets can be regarded as sparse. Taking this into account, a method of sidelobe suppression, on the basis of sparsity constraint regularization, is proposed to reduce sidelobes of Gaofen-3 (GF-3) images in sea areas of the image domain. This proposed method has a prominent sidelobe suppression effect with resolution maintenance and without destruction of amplitude and phase information. This method can also be applied to SAR images of other satellites. In addition to the employment of peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR) in evaluating sidelobe suppression level, AE (amplitude error) and PE (phase error) are firstly defined for the evaluation of amplitude and phase-preserving quality, respectively. Through the proposed method, AE and PE values are nearly unchanged and the PSLR and ISLR are significantly reduced. The method, as an important part of the quality-improvement project of GF-3, has been successfully applied to the sidelobe suppression of GF-3 data.

Spaceborne multistatic synthetic aperture radar (SAR) tomography (SMS-TomoSAR) systems take full advantage of the flexible configuration of multistatic SAR in the space, time, phase, and frequency dimensions, and simultaneously achieve high-precision height resolution and low-deformation measurement of three-dimensional ground scenes. SMS-TomoSAR currently poses a series of key issues to solve, such as baseline optimization, spatial transmission error estimation and compensation, and the choice of imaging algorithm, which directly affects the performance of height-dimensional imaging and surface deformation measurement. This paper explores the impact of baseline distribution on height-dimensional imaging performance for the baseline optimization issue, and proposes a feasible baseline optimization method. Firstly, the multi-base multi-pass baselines of an SMS-TomoSAR system are considered equivalent to a group of multi-pass baselines from monostatic SAR. Secondly, we establish the equivalent baselines as a symmetric-geometric model to characterize the non-uniform characteristic of baseline distribution. Through experimental simulation and model analysis, an approximately uniform baseline distribution is shown to have better SMS-TomoSAR imaging performance in the height direction. Further, a baseline design method under uniform-perturbation sampling with Gaussian distribution error is proposed. Finally, the imaging performance of different levels of perturbation is compared, and the maximum baseline perturbation allowed by the system is given.

Synthetic aperture radar (SAR) tomography is a powerful multibaseline technique allowing to reconstruct volume structures by using datasets of the same area with slightly different viewing angles. Nowadays, the reference network technique (RNT) is widely applied in SAR tomography to detect single and double scatterers with no need for preliminary removal of atmospheric phase screen (APS). Therein, reference network (RN) construction as the basis of RNT is vitally important for effective APS calibration to successfully detect persistent scatterers (PSs) over the entire scene. Aiming at whole-scene PS detection, RN should be constructed with global distribution throughout the whole built environment. For the moment, standard RNT cannot achieve this goal because of the damage to network connectivity in the RN construction process. In this article, an ant colony search algorithm (ACSA) is designed for whole-scene RN construction via step-by-step exploring and retaining reliable single scatterers around each ant. The effectiveness of the proposed ACSA for RN construction is demonstrated through the three-dimensional SAR tomography experiments on high-resolution TerraSAR-X SAR data stack in Shenzhen, China.

Aerial scene recognition is a fundamental task in remote sensing and has recently received increased interest. While the visual information from overhead images with powerful models and efficient algorithms yields considerable performance on scene recognition, it still suffers from the variation of ground objects, lighting conditions etc. Inspired by the multi-channel perception theory in cognition science, in this paper, for improving the performance on the aerial scene recognition, we explore a novel audiovisual aerial scene recognition task using both images and sounds as input. Based on an observation that some specific sound events are more likely to be heard at a given geographic location, we propose to exploit the knowledge from the sound events to improve the performance on the aerial scene recognition. For this purpose, we have constructed a new dataset named AuDio Visual Aerial sceNe reCognition datasEt (ADVANCE). With the help of this dataset, we evaluate three proposed approaches for transferring the sound event knowledge to the aerial scene recognition task in a multimodal learning framework, and show the benefit of exploiting the audio information for the aerial scene recognition. The source code is publicly available for reproducibility purposes.