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.

Capella's first commercial Synthetic Aperture Radar (SAR) satellite was launched in August 2020. After more than one year of successful operations, Capella plans a continuous growth of both number of satellites and satellite capabilities. New tasking patterns allow the collection of pairs and time series of very high resolution (VHR) SAR images. A novel repeat tasking request pattern will enable SAR applications such as change detection and the exploitation of interferometric SAR (InSAR) techniques. The combination of on-board GPUs and dedicated on-board processing algorithms will be used for rapid target detection and minimized satellite downlink. On-board processing, applicable in many quasi real-time operational scenarios, can also be used for tipping and cueing other satellites to immediately capture a high resolution imagery. Finally, Capella Space Open Data program started in 2021 with new images added each quarter.

Capella Space launched its first commercial Synthetic Aperture Radar (SAR) satellite in August 2020. After commissioning phase, Capella started commercial operations in January 2021, deploying a fully-functional SAR system that demonstrates three engineering milestones: an 8-m2deployable reflector mounted on small and agile satellite; the capability to process and timely deliver 0.5 m resolution spotlight images with 9 looks; and an automated tasking and delivery system that simplifies the ordering of high-quality SAR imagery for expert and novice users. In this paper, we present results from the commissioning and calibration/validation operations. We also compare images collected with the variety of imaging modes that Capella systems can collect.

We summarize the key elements of the operational readiness of Sequoia, the first commercially-operational satellite SAR system built and launched by Capella Space. We provide an overview the radar system, and the space operations and commissioning plan. Sample imagery collected with Capella radar hardware on an airborne platform is used to demonstrate the imaging capability of Sequoia.

The Capella constellation of 36 X-band SAR Satellites will provide global hourly imaging opportunities for the entire earth and InSAR collection intervals of 4 hours. With the successful launch of the first satellite last year, Capella is prepared to launch the first operational this year and the first incrementally launch 36 satelites over two years. In addition to providing short revisit times, Capella will provide game-changing image request and delivery services where users can request imagery on-demand and receive it in a matter of minutes not days or weeks.

The Capella constellation will consist of 36 agile synthetic aperture radars. Each radar will operate at an altitude of around 500 km in an approximately 90-minute polar orbit, providing average imaging revisit times of less than one hour. The radars will be single-pol X-band systems, capable of operating over a 500 MHz bandwidth in stripmap and spotlight imaging modes.

The traditional radar stop-and-hop assumption, that a radar is stationary during the transmission of a single pulse, can be violated with LFM-CW SAR. Because the stop-and-hop assumption is used as a basis for the most commonly used SAR processing methods, this can lead to significant degradation of the final image, particularly in low-altitude ultra-wide-band SAR. In this paper we extend previous work compensating for motion during a pulse to ultra-wide-band strip-map LFM-CW SAR.

In the summer 2009 NASA Characterization of Arctic Sea Ice Experiment (CASIE09), the microASAR, a small LFM-CW SAR, was operated on the NASA Sierra unmanned aerial system (UAS). An overview of the microASAR and its role in CASIE09 are described in [1, 2]. While the limitations in the motion measurements stored with the microASAR data during the CASIE09 mission originally precluded full motion compensation, motion data collected for other CASIE sensors can be employed to improve the SAR image focus and calibration. This paper describes the methodology developed to time-align this motion data and applies this data along with other algorithm improvements to the processing of the microASAR data. This paper also presents a concise description of the backprojection image processing used.

The Artemis microASAR is a flexible, robust SAR system built on the successful legacy of the BYU μSAR [1] [2] and other BYU SAR systems [3]. It is an LFM-CW SAR system designed for low-power operation on small, manned aircraft or UAVs. This paper describes the high-level methodology used in designing the microASAR system and contains a description of the hardware specifications. Performance projections are also calculated and presented.