Synspective, a leading Japanese space tech company, is building a constellation of Synthetic Aperture Radar (SAR) satellites. This paper presents an overview of the state of the constellation as of mid-2023, focusing on key aspects such as radar payload calibration, validation through sea wind retrieval, and the implementation of an automatic quality control system. The paper highlights the calibration procedures employed by Synspective, emphasizing their importance in enabling effective cross-satellite and cross-constellation image analysis. Furthermore, it demonstrates how SAR data can be utilized to retrieve sea wind information and validate the accuracy of calibration parameters. Given the substantial volume of images generated by the constellation, Synspective has implemented an automatic quality control system. This system is seamlessly integrated with the SAR-focusing software, allowing for efficient and reliable monitoring of data quality throughout the satellite’s lifetime to ensure the delivery of high-quality SAR imagery and data products.

We synthesize both optical RGB and synthetic aperture radar (SAR) remote sensing images from land cover maps and auxiliary raster data using generative adversarial networks (GANs). In remote sensing, many types of data, such as digital elevation models (DEMs) or precipitation maps, are often not reflected in land cover maps but still influence image content or structure. Including such data in the synthesis process increases the quality of the generated images and exerts more control on their characteristics. Spatially adaptive normalization layers fuse both inputs and are applied to a full-blown generator architecture consisting of encoder and decoder to take full advantage of the information content in the auxiliary raster data. Our method successfully synthesizes medium (10 m) and high (1 m) resolution images when trained with the corresponding data set. We show the advantage of data fusion of land cover maps and auxiliary information using mean intersection over unions (mIoUs), pixel accuracy, and Fréchet inception distances (FIDs) using pretrained U-Net segmentation models. Handpicked images exemplify how fusing information avoids ambiguities in the synthesized images. By slightly editing the input, our method can be used to synthesize realistic changes, i.e., raising the water levels. The source code is available athttps://github.com/gbaier/rs_img_synth, and we published the newly created high-resolution data set athttps://ieee-dataport.org/open-access/geonrw.

This work critically analyzes the problems arising from differ-modality building semantic segmentation in the remote sensing domain. With the growth of multimodality datasets, such as optical, synthetic aperture radar (SAR), light detection and ranging (LiDAR), and the scarcity of semantic knowledge, the task of learning multimodality information has increasingly become relevant over the last few years. However, multimodality datasets cannot be obtained simultaneously due to many factors. Assume that we have SAR images with reference information in one place and optical images without reference in another; how to learn relevant features of optical images from SAR images? We refer to it as differ-modality learning (DML). To solve the DML problem, we propose novel deep neural network architectures, which include image adaptation, feature adaptation, knowledge distillation, and self-training (SL) modules for different scenarios. We test the proposed methods on the differ-modality remote sensing datasets (very high-resolution SAR and RGB from SpaceNet 6) to build semantic segmentation and to achieve a superior efficiency. The presented approach achieves the best performance when compared with the state-of-the-art methods.

We propose a framework that estimates the inundation depth (maximum water level) and debris-flow-induced topographic deformation from remote sensing imagery by integrating deep learning and numerical simulation. A water and debris-flow simulator generates training data for various artificial disaster scenarios. We show that regression models based on Attention U-Net and LinkNet architectures trained on such synthetic data can predict the maximum water level and topographic deformation from a remote sensing-derived change detection map and a digital elevation model. The proposed framework has an inpainting capability, thus mitigating the false negatives that are inevitable in remote sensing image analysis. Our framework breaks limits of remote sensing and enables rapid estimation of inundation depth and topographic deformation, essential information for emergency response, including rescue and relief activities. We conduct experiments with both synthetic and real data for two disaster events that caused simultaneous flooding and debris flows and demonstrate the effectiveness of our approach quantitatively and qualitatively. Our code and data sets are available athttps://github.com/nyokoya/dlsim.

Interferometric phase restoration has been investigated for decades and most of the state-of-the-art methods have achieved promising performances for InSAR phase restoration. These methods generally follow the nonlocal filtering processing chain, aiming at circumventing the staircase effect and preserving the details of phase variations. In this article, we propose an alternative approach for InSAR phase restoration, that is, Complex Convolutional Sparse Coding (ComCSC) and its gradient regularized version. To the best of the authors' knowledge, this is the first time that we solve the InSAR phase restoration problem in a deconvolutional fashion. The proposed methods can not only suppress interferometric phase noise, but also avoid the staircase effect and preserve the details. Furthermore, they provide an insight into the elementary phase components for the interferometric phases. The experimental results on synthetic and realistic high- and medium-resolution data sets from TerraSAR-X StripMap and Sentinel-1 interferometric wide swath mode, respectively, show that our method outperforms those previous state-of-the-art methods based on nonlocal InSAR filters, particularly the state-of-the-art method: InSAR-BM3D. The source code of this article will be made publicly available for reproducible research inside the community.

Outliers and speckle both corrupt time series of synthetic aperture radar (SAR) acquisitions. Owing to the coherence between SAR acquisitions, their speckle can no longer be regarded as independent. In this study, we propose an algorithm for nonlocal low-rank time series despeckling, which is robust against outliers and also specifically addresses speckle correlation between acquisitions. By imposing total variation regularization on the signal's speckle component, the correlation between acquisitions can be identified, facilitating the extraction of outliers from unfiltered signals and the correlated speckle. This robustness against outliers also addresses matching errors and inaccuracies in the nonlocal similarity search. Such errors include mismatched data in the nonlocal estimation process, which degrade the denoising performance of conventional similarity-based filtering approaches. Multiple experiments on real and synthetic data assess the performance of the approach by comparing it with state-of-the-art methods. It provides filtering results of comparable quality but is not adversely affected by outliers. The source code is available at https://github.com/gbaier/nllrtv.

This paper presents a novel application of remote sensing data and machine learning technologies for damage classification in a real-world cross-domain application. The proposed methodology trains models to learn the building damage characteristics recorded in the 2011 Tohoku Tsunami from multi-sensor and multi-temporal remote sensing images. Then, the trained models are tested in the recent 2018 Sulawesi Tsunami. Additionally, a simulation of high-resolution SAR image was carried to deal with missing data modality. Our initial results show that the ResNet-derived features from optical images acquired after the disaster together with moderate- and high-resolution synthetic aperture radar (SAR) post-event intensity data showed significant accuracy in classifying two levels of tsunami-induced damage, with an average f-score of approximately 0.72. Taking into account that no training data from the 2018 Sulawesi Tsunami was used, our methodology shows excellent potential for future implementation of a rapid response system based on a database of building damage constructed from previous majors disasters.

We present a nonlocal low-rank denoising algorithm for synthetic aperture radar (SAR) image stacks. The method extends the widely known DespecKS algorithm by integrating low-rank approximation, outlier removal, and total variation (TV) regularization into the estimation process. Preliminary experiments shows increased robustness against outliers and comparable performance to state-of-the-art stack despeckling algorithms.

This study employed Sentinel-1A C-band and Sentinel-2A multispectral data combined with the decision tree ensemble algorithms to map the spatial distribution of five mangrove communities in a coastal area in North Vietnam. The results show that the rotation forests (RoFs) model achieved better overall accuracy and kappa coefficient in mapping mangrove species than those of the canonical correlation forests (CCFs) and the random forests (RFs) models. This research demonstrates the potential of using optical and SAR data together with machine learning techniques to map mangrove species in tropical areas.

This paper presents building damage mapping based on transfer learning techniques. Due to the different spatial resolutions of optical (WorldView, 0.5m) and SAR (Sentinel-1, 10m), we adopt different methods: pixel-level for moderate-resolution SAR images, and patch-level for very high-resolution optical images. For SAR images, the performance of fast unsupervised transfer learning methods, such as overall centroid alignment (OCA) and CORrelation ALignment (CORAL), are investigated. For the optical images, two public databases are used to predict the building damage mapping of Palu with WorldView-3 images via ResNet50. Experimental results indicate the effectiveness of transfer learning on the building damage mapping using different data sources.

This paper presents a nonlocal interferometric synthetic aperture radar (InSAR) filter with the goal of generating digital elevation models (DEMs) of higher resolution and accuracy from bistatic TanDEM-X strip map interferograms than with the processing chain used in production. The currently employed boxcar multilooking filter naturally decreases the resolution and has inherent limitations on what level of noise reduction can be achieved. The proposed filter is specifically designed to account for the inherent diversity of natural terrain by setting several filtering parameters adaptively. In particular, it considers the local fringe frequency and scene heterogeneity, ensuring proper denoising of interferograms with considerable underlying topography as well as urban areas. A comparison using synthetic and TanDEM-X bistatic strip map data sets with existing InSAR filters shows the effectiveness of the proposed techniques, most of which could readily be integrated into existing nonlocal filters. The resulting DEMs outclass the ones produced with the existing global TanDEM-X DEM processing chain by effectively increasing the resolution from 12 to 6 m and lowering the noise level by roughly a factor of two.

We investigate the feasibility of generating highly accurate digital elevation models (DEM) from TanDEM-X interferograms by using nonlocal filters for phase denoising. Some of the shortcomings of existing nonlocal filters that render them not applicable to our goal are briefly described and a new filter is proposed that alleviates these problems. The most significant new properties are addressing the slope dependent denoising performance of existing nonlocal InSAR filters and several measures to bolster denoising near edgelike features. We evaluate the proposed filter using synthetic interferograms and by visual inspection of a DEM generated from a TanDEM-X interferogram.

This paper presents the first results generated with the TanDEM-X mission for the monitoring of the topographical changes caused by the series of earthquakes that hit central Italy between summer and autumn 2016. For the purpose, two 300 km long data takes acquired between the Tyrrhenian and the Adriatic coasts and covering the October, 30, earthquake epicenter location have been considered. The takes have an about 5 years' temporal baseline, thus helpful to reveal the large and small scale terrain changes occurred after the 2016 seismic events.

In the past few years nonlocal filters have emerged as a serious contender for denoising synthetic aperture radar (SAR) images, offering superior noise reduction and detail preservation compared to many other filters. In this manuscript we analyze how nonlocal filters, whose computational costs were so far prohibitive for large scale processing, can be implemented efficiently on graphics processing units (GPU). As a case study NL-SAR, a state of the art SAR filter, is implemented to run on a NVIDIA Tesla K40. We describe the appeal of GPUs, or any other coprocessor, for nonlocal filters. Nonlocal filtering of TanDEM-X interferograms for generating digital elevation models with a higher resolution and accuracy is given as an application that benefits from efficient and fast nonlocal filtering.

This paper proposes a nonlocal filter variant that replaces the conventional static search window of nonlocal InSAR filters with an adaptive region growing based search window. The region growing approach has the allure that it preselects only similar pixels for the averaging process and that it may find a larger number of statistically homogeneous pixels than a traditional, fixed search window. A Monte-Carlo simulation shows the possible benefits that could be realized with the region growing approach for InSAR filtering. The proposed method is also experimentally evaluated for rural and urban test sites.