This work proposes a deep learning (DL) architecture for estimating full-field deformation maps from ground-based complex radar data. Unlike existing methods that focus on estimating deformation time series at selected points, the proposed model directly estimates the entire deformation map from a single-frame complex radar observation. This capability enables faster, spatially comprehensive monitoring, which is crucial for real-time early warning systems (EWSs) in Internet of Things (IoT)-enabled environments. The primary input to the model is a 2-channel matrix formed by the real and imaginary components of the complex radar data. We propose a dual cross-attentive recurrent U-Net architecture (DCRU-Net) consisting of two networks trained simultaneously. The two networks, a primary and an auxiliary network, are based on long short-term memory (LSTM) units arranged in a U-Net architecture, where the model inputs are spatially restructured into sequences, enabling the model to learn deformation-related phase variations across the scene rather than modeling temporal dependencies as in conventional approaches. The primary network processes the 2-channel complex radar data, which is the leading network of the model. In contrast, the auxiliary network is guided by the amplitude dispersion index (ADI) to emphasize coherent scattering regions during training. A cross-attention mechanism enables interaction between the two networks, allowing the auxiliary network to guide the primary network toward physically meaningful regions and suppress clutter. The motivation behind this architecture is to predict the main features of the deformation maps while minimizing the impact of slight variations, which can result in clutter-induced distortions. The model is evaluated against benchmark methods, and the results demonstrate that our model outperforms existing methods and has better alignment with the ideal deformation maps, showing its potential for real-world radar applications.

The Vietnamese Mekong Delta (VMD) has been affected by land subsidence for more than one decade, which is the result of different drivers, including groundwater over-exploitation and natural compaction of holocene layers. The exact share of the contributions is so far unknown. In this work, we systematically study the initial settlement of newly built constructions and built-up areas in two cities in the VMD by incorporating Temporary Persistent Scatterers (TPS) into an existing PS-InSAR approach. We find that the portion of appearing TPS with high initial subsidence rates is larger than the portion of PS and fading TPS with high subsidence rates. We furthermore cluster appearing TPS by their location and appearing date to identify and analyse new constructions and built-up areas and find that they can have really high initial settlement rates which statistically decrease over time. We attribute this signal to the loading-induced initial settlement of constructions which lack a proper foundation. We assume settlement of this kind to contribute noticeably to the measured total subsidence all across the VMD, especially due to the recent high building activity.

The persistent scatterer interferometry (PSI) is a powerful remote sensing technique to monitor displacements of the Earth’s surface. It is based on identifying and analyzing phase-stable scatterers undergoing marginal decorrelation over time. Most PSI approaches constrain the analysis to scatterers which are coherent over the whole considered time series (PS) and neglect scatterers which are only temporary persistent (TPS). Here, we propose a method to fully integrate TPS into an existing PSI approach which is characterized by an iterative parameter estimation with subsequent phase unwrapping. We build on a previously published method to identify TPS and estimate their coherent lifetime based on amplitude statistics. A phase-based likelihood ratio test is proposed to iteratively refine the appearing/fading dates of TPS during the parameter estimation. Finally, we jointly unwrap the phase observations of PS and TPS to receive displacement time series. The temporal datum of TPS is re-defined if their lifetime does not cover the selected master scene. Experimental results based on Sentinel-1 data in the Vietnamese city of Ca Mau show that the change date refinement significantly increases the average coherence and number of identified TPS. The densification of the observation point network by incorporating TPS helps to better detect and understand displacement phenomenons. The displacement time series of TPS are beneficial to analyze nonlinear motion in connection with urban development, like initial settlement of newly constructed buildings.

Land subsidence in urban areas is frequently characterized by high spatial heterogeneity, caused by variability in the local lithology, land-use history as well as different load and foundation depths of infrastructure. This effect can, for example, be observed in the Mekong Delta, which is subsiding on large scale with rates up to several centimeters per year. We estimated subsidence rates using PS-InSAR and systematically analyzed differential subsidence between bridges, which are usually built with pile foundations, and the surrounding land surface. We used GIS information on bridge locations from OpenStreetMap and automatically determined the differential subsidence rates at these GIS objects on a delta-wide scale. We show that most of the considered bridges are affected by lower subsidence rates than their surroundings. These results can help in future studies to constrain the depth of soil compaction and better understand the drivers of land subsidence.

A framework is proposed that allows for a joint description and optimization of both binary polar coding and the multilevel coding (MLC) approach for 2m-ary digital pulse-amplitude modulation (PAM). The conceptual equivalence of polar coding and multilevel coding is pointed out in detail. Based on a novel characterization of the channel polarization phenomenon, rules for the optimal choice of the bit labeling in this coded modulation scheme employing polar codes are developed. Simulation results for the AWGN channel are included.

A framework is proposed that allows for a joint description and optimization of both binary polar coding and 2m-ary digital pulse-amplitude modulation (PAM) schemes. For the latter, the multilevel coding (MLC) approach as well as bit-interleaved coded modulation (BICM) are considered. The conceptual equivalence of polar coding and multilevel coding is covered in detail. Based on an alternative characterization of the channel polarization phenomenon, rules for the optimum choice of the labeling in coded modulation schemes employing polar codes are developed. Simulation results regarding the error performance of the proposed schemes on the AWGN channel are included.

Information processing characteristics (IPC) provide a general framework for the analysis of a coding scheme. In this paper, we utilize IPCs to obtain performance estimates for the end-to-end coded channel for bit-interleaved coded modulation (BICM) using a given modulation format and coding scheme, i.e., its bit error rate and capacity. The proposed method enables to fully decouple the analysis of the coding scheme from the analysis of the higher-order modulation scheme and delivers very good performance estimates. Exemplarily, this is validated by means of numerical simulations for BICM using convolutional- and LDPC-coded amplitude-shift keying.

In this paper we present decision-feedback differential detection (DF-DD) schemes for autocorrelation-based detection in impulse-radio ultra-wideband (IR-UWB) systems, a signaling scheme regarded as a promising candidate in particular for low-complexity wireless sensor networks. To this end, we first discuss ideal noncoherent sequence estimation and approximations thereof based on block-wise multiple-symbol differential detection (MSDD) and the Viterbi algorithm (VA) from the perspective of tree-search/trellis decoding. Exploiting relations well-known from tree-search decoding, we are able to derive the novel decision-feedback differential detection (DF-DD) schemes. A comprehensive comparison with respect to performance and complexity of the presented schemes in a typical IR-UWB scenario reveals-along with novel insights in techniques for complexity reduction of the sphere decoder applied for MSDD- that sorted DF-DD achieves close-to-optimum performance at very low, and in particular constant receiver complexity.

The appearance of objects and surfaces in synthetic aperture radar (SAR) images significantly differs from the human perception of the environment. In addition, the quality of SAR data is degraded by speckle noise, superposing the true radiometric and textural information of the radar image. Hence, the interpretation of SAR images is considered to be more challenging compared to the analysis of optical data. However, in this paper, we demonstrate how information on the local development of speckle can be used for the differentiation of basic land cover (LC) types in a single-polarized SAR image. For that purpose, we specify the speckle characteristics of the following LC types: 1) water; 2) open land (farmland, grassland, bare soil); 3) woodland; and 4) urban area by means of an unsupervised analysis of scatter plots and standardized histograms of the local coefficient of variation. Next, we use this information for the implementation of a straightforward preclassification of single-polarized TerraSAR-X stripmap images by combining information on the local speckle behavior and local backscatter intensity. The output is either provided as a discrete classification or as a color composite image whose bands can be interpreted in terms of a fuzzy classification. The results of this paper show that unsupervised speckle analysis in high-resolution SAR images supplies valuable information for a differentiation of the water, open land, woodland, and urban area LC types. While the color composite image supports the visual interpretation of SAR data, the outcome of the fully automated discrete LC classification procedure represents a valuable preclassification image, showing overall accuracies of 77%-86%.

Speckle is a SAR specific noise effect caused by constructive and destructive interference from multiple scattering within the resolution cell of the imaging radar system that superposes the true radiometric and textural information of SAR images as a grainy `salt-and-pepper' pattern. Fully developed speckle basically follows circular Gaussian image statistics. However, this assumption is not applicable for very high resolution SAR systems and for data showing sceneries with a significant amount of directional backscatter - e.g. urban areas. In those cases the multiple scattering processes within a resolution cell show - subject to the true structuring of the imaged area - rather a directional behavior than a random distribution. Consequently, the speckle is no longer fully developed. In our study we demonstrate how information on the local development of speckle can be used to differentiate between basic land cover types such as water, open land - meaning farmland, grassland and bare soil -, woodland and urban area in single-polarized, single-date VHR SAR images. The research is based on the analysis of a TerraSAR-X scene of Munich, Germany.

Power efficiency of noncoherent receivers for impulse-radio ultra-wideband (IR-UWB) transmission systems can significantly be improved, on the one hand, by employing multiple-symbol differential detection (MSDD), and, on the other hand, by providing reliability information to the subsequent channel decoder. In this paper, we combine these two techniques. Incorporating the computation of the soft information into a single-tree-search sphere decoder (SD), the application of this soft-output MSDD in a typical IR-UWB system imposes only a moderate complexity increase at, however, improved performance over hard-output MSDD, and in particular, over conventional symbol-by-symbol noncoherent differential detection.

With a spatial resolution of up to 1 m, the German radar satellite TerraSAR-X (TSX) has significantly increased the usability of spaceborne synthetic aperture radar (SAR) imagery in the context of urban applications. This paper presents an approach toward the semiautomated detection of built-up areas (BAs) based on single-polarized TSX images. The proposed methodology includes a specific preprocessing of the SAR data and an automated image analysis procedure. The preprocessing focuses on the analysis of local speckle characteristics in order to provide a texture layer that highlights BAs. In the context of an object-oriented image analysis, this texture layer is used along with the original intensity information to automatically extract settlements. The technique is tested on the basis of 12 TSX scenes covering representative urban agglomerations distributed throughout the world. Overall, accuracies between 76% and 96% for the derived city footprints demonstrate the high potential of both the TSX imagery and the proposed analysis approach in detecting BAs. In order to demonstrate the robustness and transferability of the image analysis concept, we finally transferred the classification strategy from the object-oriented domain to a more general and simplified pixel-based approach.