In this study, we present an unsupervised methodology for detecting moving targets using Capella Space’s latest generation SAR sensor. The sensor has the capability to dwell on a target for an extended period of time in its spot-light (SP) mode, which we take advantage of to track moving objects in an acquisition. An approach that combines a signal-processing-based workflow with an image-domain-based one is presented. From one side, the long-dwell SAR image is exploit by doing an interfeometric processing of different azimuth sub-apertures from the long-dwell. From another side, a kernelized cross correlation technique is used. By combining intermediate results from these complementary workflows, a smooth and robust track is obtained on the targets. The algorithm is demonstrated on a long-dwell spotlight obtained over a busy shipping channel with watercraft both small and large successfully tracked.

The ability to automatically detect hard targets is highly beneficial for an imagery analyst. Their initial task in the information gathering process is to identify and interpret these targets in a scene. By automating the detection process, the workflow can be expedited. This paper presents an algorithm for detecting hard targets in long dwell spotlight (SP) Capella Space SAR images. The spectrum of the SP image is divided into non-overlaping sub-bands in order to compute the interferometric coherence between them. The proposed algorithm is tested on real spaceborne Capella SAR data showing the potential to clearly identify hard targets such as buildings, vehicles, and other artificial man-made objects.

In this work we explore the potential of time series of high-resolution radar imagery from Capella Space to enable crop monitoring in areas characterised by very small agricultural fields. Thanks to the spatial resolution provided by the Capella Space SAR images, adjacent fields are well separated in the analysis and, consequently, can be properly monitored and classified. The high-resolution feature complements the all-weather and sun independent operation of radar, hence being an excellent asset for this application.

Taking advantage of Capella’s ability to dwell on a target for an extended period of time (nominally 30s) in its spotlight (SP) mode, an unsupervised methodology for detecting moving targets in this data is presented in this paper. By colourizing short segments (sub-apertures) of the total imaging time, a colourised sub-aperture image (CSI) can be formed. This can be used in conjunction with well-established computer vision techniques to detect moving targets and track them in the SP image. In essence, the moving target detection problem is transformed from temporal image stack identification to colour segmentation in a single image. The presented detection and tracking are wholly unsupervised. Additionally, computer-vision-based tracking algorithms are demonstrated on detected movers and qualitatively assessed for accuracy of tracking.

Capella Space is the first US commercial company to build, launch, and operate a constellation of synthetic aperture radar satellites capable of collecting very high resolution SAR imagery. All satellites in the constellation carry an X-band radar capable of acquiring imagery in spotlight, sliding spotlight, and stripmap modes. In 2023, Capella will launch the first of a new generation of satellites names Acadia. These satellites will provide high quality imagery and lay the platform for advanced SAR data products, such as interferometric SAR and bistatic imagery.

Synthetic aperture radar tomography (TomoSAR) at lower frequencies allows the reconstruction of the 3-D radar reflectivity of volume scatterers allowing access to their physical 3-D structure by means of multiangular SAR acquisitions. The performance of the reconstruction critically depends on the number and (spatial) distribution of the tomographic acquisitions (tracks). This dependence is addressed in this letter with respect to forest applications (volume scatters) at L-band. The letter discusses the optimum definition of tomographic configurations based on the peak sidelobe level (PSL) of the point spread function (PSF). For demonstration, a tomographic data set consisting of 15 acquisitions acquired by the DLR’s F-SAR system at L-band over the Traunstein test site in Germany is used, complemented by airborne LiDAR measurements. Three different reconstruction algorithms (Fourier beamforming, Capon beamforming, and compressive sensing) are implemented and compared to each other for scenarios with a reduced number of acquisitions. Although the limitation of the specific forest, the results show the potential of using the PSL of the PSF to define tomographic configurations optimized for forest structure applications.

The TerraSAR-X add on for Digital Elevation Measurement (TanDEM-X) mission provides Interferometric Synthetic Aperture Radar (InSAR) wall-to-wall data (not sparse) at high resolution and at global scale. In addition, the NASA Global Ecosystem Dynamics Investigation (GEDI) is a new spaceborne system that provides (from 51.6°N and 51.6°S) sparse measurements (not images) through LiDAR waveforms. Both systems are sensitivity to the canopy structure such as the forest height but with their own limitations. The TanDEM-X single polarization (HH) interferometric coherence magnitude at X-band provides a continuous mapping of the forest while GEDI provides accurate (but sparse) measurements of the forest. In this paper a methodology of how to combine both systems to estimated forest height is presented and applied to more than 900 TanDEM-X scenes over Gabon in Africa. The forest height results over an area of 1° by 1° are shown and compared respect to GEDI. Finally, a wall-to-wall forest map over the entire country of Gabon is presented as an example of large scale mapping towards a potential global (entire earth) forest height map.

Polarimetric SAR Interferometry (Pol-InSAR) is a SAR remote sensing discipline with unique and powerful applications related to the vertical structure of natural and man-made volume scatterers. The coherent combination of single- or multi -baseline interferograms acquired at different polarisations provides sensitivity to the vertical distribution of scattering processes and allows their characterisation by using the associated (volume) interferometric coherences [1]–[5].

TanDEM-X experiments have shown that forest height can be estimated with single polarization X-band interferometric coherences. An external digital terrain model (DTM) not only allows to use both coherence magnitude and phase information, but also to overcome X-band penetration limitations. However, DTM information is not available for large areas. Using coherence magnitudes makes height inversion feasible, but it requires a model relating coherence to height. Here we report an experiment using the X-band local phase center variations. Results over a tropical forest site show that in those stands in which the low X-band penetration is not a limitation, the there is a good correlation between the obtained TanDEM-X heights and the heights from Lidar measurements.

Forest structure is a key parameter for forest applications, but it is difficult to be estimated at the required spatial and temporal scales. In this context, synthetic aperture radar Tomography (TomoSAR) that allows, at lower frequencies, the 3-D imaging of natural volume scatterers with high spatial and temporal resolution may be a game changer. The aim of this article is to evaluate three TomoSAR algorithms, Fourier beamforming (FB), Capon beamforming (CB), and compressive sensing (CS) with respect to their performance in the reconstruction of the 3-D forest reflectivity. The implications of volumetric forest scattering, as well as the temporal decorrelation of scatterers, are analyzed. The algorithms are compared on a set of simulated scenarios and then evaluated on an experimental L-band data set composed by four acquisition dates, each one consisting of five tomographic tracks. The data were acquired in 2014, within a time span of two months, over the Traunstein forest (Germany) using the F-SAR system. Additionally, discrete airborne Lidar has been used for a qualitative evaluation. The results indicate that the CS reconstruction is, for many practical cases, superior when compared to FB or CB reconstructions as they achieve higher vertical resolution, especially in cases with a lower number of acquisitions and complex forest scenarios. By combining acquisitions performed at different days, the effect of temporal decorrelation on each algorithm for two different tomographic implementations (repeat-pass vs. single-pass) has been assessed. The results indicate that simultaneously acquired image pairs allow a better reconstruction of the 3-D forest reflectivity.

Synthetic Aperture Radar Tomography techniques provide 3D information of the forest due to the ability of microwaves to penetrate through vegetation. Recent studies link the radar 3D information to forest 3D structure in order to translate the tomographic results to an ecological interpretation. However, due to the undersampled nature of tomographic acquisitions, the number and distribution of acquisitions can change the estimated 3D radar reflectivity and as a consequence the forest structure estimates. This paper explores the results for radar as well as for forest structure for different number and distribution of acquisitions in order to analyse the potential and limitations of the estimation of forest structure in future space borne scenarios where the number and distribution of acquisitions will be suboptimal. In this context, the paper analyses a tomographic campaign of 15 tracks acquired over Traunstein (Germany) in 2017 together with ground measurements and Lidar.

Polarimetric SAR Interferometry (Pol-InSAR) is a SAR remote sensing discipline with unique and powerful applications related to the vertical structure of natural and man-made volume scatterers. The coherent combination of single- or multi-baseline interferograms acquired at different polarisations provides sensitivity to the vertical distribution of scattering processes and allows their characterisation by using the associated (volume) interferometric coherences [1]-[5].

TanDEM-X forms together with TerraSAR-X the first single-pass polarimetric interferometer in space. This allows for the first time the acquisition and analysis of Single-, Dual-, and Quad-Pol-InSAR data without the disturbing effect of temporal decorrelation globally. For this reason, the exploration of TanDEM-X data for forestry is constantly increasing especially concerning forest height estimation, biomass classification and structure characterization. This paper reports the results of recent experiments aimed at investigating the potentials of TanDEM-X in characterizing quantitatively the spatial variability of the canopy top and phase center height, which is a proxy to horizontal structure. It is shown that such characterization can allow to differentiate among e.g. different successional and / disturbance stages in tropical forests.

Systems based on Synthetic Aperture Radar Tomography at low frequencies offer 3D imaging capabilities, appropriate for forest monitoring. However the extraction of an ecologically meaningful measure of forest structure from the 3D reflectivity is not straightforward and several considerations need to be carefully taken into account, in order to avoid misinterpretations of the nature of the information reflected in the tomograms. Besides, it should be noted that the methodology employed in the TomoSAR inversion has a significant effect on the overall performance of the TomoSAR system to estimate forest structure. In this framework, this paper discusses the potential and limitations of TomoSAR systems for forest structure estimation.

Synthetic Aperture Radar Tomography provides the 3D reflectivity of the observed scene. Hence, in a forest scenario, it reflects information relevant to forest structure. However, how and to which extent this information is reflected and which are possible ways to quantify it are still open questions. This paper explores the link between ecological measures of forest structure and the 3D spatial distribution of the peaks in the reflectivity profiles at L-band and, with this, proposes measures for forest structure estimation from tomographic SAR data.