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.

The rapid delineation of water extent in a flood-type event can be very beneficial to disaster relief efforts, and Synthetic Aperture Radar (SAR) is a modality ideally suited for such mapping. However, in a rapid-response scenario, it is desirable to produce such maps independent of historical or external data. To this end, we have propose a scheme to produce flood event maps from a single high-resolution StripMap (SM) imagery acquired from the Capella Space X-band VHR SAR constellation. The learning algorithm is solely trained on publicly available ancillary data, without the use of any human generated labels. The flood-maps are validated quantitatively on non-event scenes against water-occurrence data and qualitatively over the course of a flood-event caused by Hurricane Ida's landfall.

Floods are among the most common and destructive extreme weather events in the world. Spaceborne synthetic aperture radar (SAR) systems are well-suited for monitoring flood events given their ability to operate in near all-weather and all-time conditions. We present a case study classifying imagery from both the commercial very high resolution (VHR) Capella Space X-band constellation and the public C-band Sentinel-1 mission. We use inferred flood extents from these disparate sources to investigate the progression of unprecedented inundation over Abbotsford, British Columbia in November - December 2021.

Owing to the unique all-weather, day-night imaging capabilities of Synthetic Aperture Radar (SAR) imaging, the modality is advantageous in the detection of anthropogenic activity. In this paper we present a fully unsupervised change detection framework that operates on Very High Resolution (VHR) SAR image pairs to produce a binary change map, without a need for per-image parameter setting. The framework is demonstrated on a pair of Capella-2 VHR X-band imagery acquired over San Diego, USA.

Many applications utilizing SAR data, such as change detection, segmentation and classification, are impaired by the multiplicative speckle interference inherent in the imagery. Thus despeckling of SAR imagery is a often the key to developing robust algorithms for scene understanding. In recent years numerous deep learning-based approaches to speckle reduction have been proposed. However, the performance of these methods has largely failed to meet the expectations of researchers and industry alike. A key reason for this is due to the lack of accurate SAR-based ground truth training data. In this paper we propose the use of very high-resolution (VHR), low speckle aerial imagery and an accurate speckle model, as a ground truth signal for training a despeckling network based on the DnCNN architecture. Furthermore, we investigate modifications to the training formulation and finally demostrate the approach on Capella-2 VHR X-band imagery.