Detecting Building Layovers in a SAR Interferometric Processor Without External References

A novel technique for the derivation of building layovers is presented. It makes use of the behaviour of the geocoding processing stage embedded in an interferometric SAR processor for this particular case. It is shown how layover pixels create a regular pattern in the range mapping matrices, with a multiple mapping of a single SAR pixel for different DEM cells. The exploitation of these patterns yields a generation of a layover map without the use of external supports. The integration in an interferometric processor with a limited additional computational load and the capability to isolate building signatures are additional benefits. The algorithm is tested on a TanDEM-X spotlight acquisition over Berlin (Germany)

Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography

This book focuses on remote sensing for urban deformation monitoring. In particular, it highlights how deformation monitoring in urban areas can be carried out using Persistent Scatterer Interferometry (PSI) and Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Several contributions show the capabilities of Interferometric SAR (InSAR) and PSI techniques for urban deformation monitoring. Some of them show the advantages of TomoSAR in un-mixing multiple scatterers for urban mapping and monitoring. This book is dedicated to the technical and scientific community interested in urban applications. It is useful for choosing the appropriate technique and gaining an assessment of the expected performance. The book will also be useful to researchers, as it provides information on the state-of-the-art and new trends in this fiel

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Growing stock volume estimation in temperate forsted areas using a fusion approach with SAR Satellites Imagery

Forest monitoring plays a central role in the context of global warming mitigation and in the assessment of forest resources. To meet these challenges, significant efforts have been made by scientists to develop new feasible remote sensing techniques for the retrieval of forest parameters. However, much work remains to be done in this area, in particular in establishing global assessments of forest biomass. In this context, this Ph.D. Thesis presents a complete methodology for estimating Growing Stock Volume (GSV) in temperate forested areas using a fusion approach based on Synthetic-Aperture Radar (SAR) satellite imagery. The investigations which were performed focused on the Thuringian Forest, which is located in Central Germany. The satellite data used are composed of an extensive set of L-band (ALOS PALSAR) and X-band (TerraSAR-X, TanDEM-X, Cosmo-SkyMed) images, which were acquired in various sensor configurations (acquisition modes, polarisations, incidence angles). The available ground data consists of a forest inventory delivered by the local forest offices. Weather measurements and a LiDAR DEM complete the datasets. The research showed that together with the topography, the forest structure and weather conditions generally limited the sensitivity of the SAR signal to GSV. The best correlations were obtained with ALOS PALSAR (R2 = 0.61) and TanDEM-X (R2 = 0.72) interferometric coherences. These datasets were chosen for the retrieval of GSV in the Thuringian Forest and led with regressions to an root-mean-square error (RMSE) in the range of 100─200 m3ha-1. As a final achievement of this thesis, a methodology for combining the SAR information was developed. Assuming that there are sufficient and adequate remote sensing data, the proposed fusion approach may increase the biomass maps accuracy, their spatial extension and their updated frequency. These characteristics are essential for the future derivation of accurate, global and robust forest biomass maps

High-Resolution InSAR Building Layovers Detection and Exploitation

Layover affects the quality of urban interferometric synthetic aperture radar (InSAR) digital elevation models. Moreover, it is generally difficult to interpret because of the superposition of several contributions in a single SAR pixel. In this paper, a novel technique for the extraction of building layovers is first presented. It makes use of the geocoding stage embedded in the InSAR processor. It is shown that building layovers create a regular pattern in the mapping counter, a map describing the number of occurrences of a SAR pixel in the elevation model. Its exploitation yields a generation of a layover map without the use of external supports. The integration in the processor with a limited additional computational load and the capability to isolate layover signatures are additional benefits. Layover patches are then individually analyzed toward a better understanding of the complex urban signal return. A spectral estimation framework is employed to assess the slopes superimposed in the patches. Fringe-frequency estimation is involved. A set of simulations made for a nonparametric (fast Fourier transform) and a parametric (multiple signal classification) technique is performed prior to testing on real data. It is demonstrated that in X-band, for a single interferogram, just one layover contributor, when it dominates over the others, can be extracted with a sufficient accuracy. The algorithms are tested on a TanDEM-X spotlight acquisition over Berlin (Germany)