Using Anisotropic Diffusion for Coherence Estimation

In this paper we will present a new coherence estimation technique for SAR interferometry products that adapts the estimation window size and shape during processing. This is of particular interest for sensors with medium spatial resolution, like the ASAR WS mode, where the estimator shall cope with the spatial variability of the targets in the imaged area. This method is designed to remove low coherence magnitude bias while keeping a good spatial resolution. Finally, this new approach will be compared to an existing algorithm for quantifying resulting quality improvement

Subsidence Detected by Multi-Pass Differential SAR Interferometry in the Cassino Plain (Central Italy): Joint Effect of Geological and Anthropogenic Factors?

In the present work, the Differential SAR Interferometry (DInSAR) technique has been applied to study the surface movements affecting the sedimentary basin of Cassino municipality. Two datasets of SAR images, provided by ERS 1-2 and Envisat missions, have been acquired from 1992 to 2010. Such datasets have been processed independently each other and with different techniques nevertheless providing compatible results. DInSAR data show a subsidence rate mostly located in the northeast side of the city, with a subsidence rate decreasing from about 5–6 mm/yr in the period 1992–2000 to about 1–2 mm/yr between 2004 and 2010, highlighting a progressive reduction of the phenomenon. Based on interferometric results and geological/geotechnical observations, the explanation of the detected movements allows to confirm the anthropogenic (surface effect due to building construction) and geological causes (thickness and characteristics of the compressible stratum

Two target height effects on interferometric synthetic aperture radar coherence

Useful products generated from interferometric synthetic aperture radar (IFSAR) complex data include height measurement, coherent change detection, and classification. The IFSAR coherence is a spatial measure of complex correlation between two collects, a product of IFSAR signal processing. A tacit assumption in such IFSAR signal processing is that one height target exists in each range-Doppler cell. This paper presents simulations of IFSAR coherence if two targets with different heights exist in a given range-Doppler cell, a condition in IFSAR collections produced by layover. It also includes airborne IFSAR data confirming the simulation results. The paper concludes by exploring the implications of the results on IFSAR classification and height measurements

Incorporating interferometric coherence into lulc classification of airborne polsar-images using fully convolutional networks

Inspired by the application of state-of-the-art Fully Convolutional Networks (FCNs) for the semantic segmentation of high-resolution optical imagery, recent works transfer this methodology successfully to pixel-wise land use and land cover (LULC) classification of PolSAR data. So far, mainly single PolSAR images are included in the FCN-based classification processes. To further increase classification accuracy, this paper presents an approach for integrating interferometric coherence derived from co-registered image pairs into a FCN-based classification framework. A network based on an encoder-decoder structure with two separated encoder branches is presented for this task. It extracts features from polarimetric backscattering intensities on the one hand and interferometric coherence on the other hand. Based on a joint representation of the complementary features pixel-wise classification is performed. To overcome the scarcity of labelled SAR data for training and testing, annotations are generated automatically by fusing available LULC products. Experimental evaluation is performed on high-resolution airborne SAR data, captured over the German Wadden Sea. The results demonstrate that the proposed model produces smooth and accurate classification maps. A comparison with a single-branch FCN model indicates that the appropriate integration of interferometric coherence enables the improvement of classification performance

Multi-Annual Evaluation of Time Series of Sentinel-1 Interferometric Coherence as a Tool for Crop Monitoring

Interferometric coherence from SAR data is a tool used in a variety of Earth observation applications. In the context of crop monitoring, vegetation indices are commonly used to describe crop dynamics. The most frequently used vegetation indices based on radar data are constructed using the backscattered intensity at different polarimetric channels. As coherence is sensitive to the changes in the scene caused by vegetation and its evolution, it may potentially be used as an alternative tool in this context. The objective of this work is to evaluate the potential of using Sentinel-1 interferometric coherence for this purpose. The study area is an agricultural region in Sevilla, Spain, mainly covered by 18 different crops. Time series of different backscatter-based radar vegetation indices and the coherence amplitude for both VV and VH channels from Sentinel-1 were compared to the NDVI derived from Sentinel-2 imagery for a 5-year period, from 2017 to 2021. The correlations between the series were studied both during and outside the growing season of the crops. Additionally, the use of the ratio of the two coherences measured at both polarimetric channels was explored. The results show that the coherence is generally well correlated with the NDVI across all seasons. The ratio between coherences at each channel is a potential alternative to the separate channels when the analysis is not restricted to the growing season of the crop, as its year-long temporal evolution more closely resembles that of the NDVI. Coherence and backscatter can be used as complementary sources of information, as backscatter-based indices describe the evolution of certain crops better than coherence.This research work was supported by the the European Space Agency under Project SEOM-S14SCI-Land (SInCohMap), and by the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) and the European Funds for Regional Development (Project PID2020-117303GB-C22)

Ground instability detection using PS-InSAR in Lanzhou, China

This paper reports on the application of radar satellite data and Persistent Scatterer Interferometry (PS-InSAR) techniques for the detection of ground deformation in the semi-arid loess region of Lanzhou, northwestern China. Compared with Synthetic Aperture Radar Interferometry (InSAR), PS-InSAR overcomes the problems of temporal and geometric de-correlation and atmospheric heterogeneities by identifying persistent radar targets (PS) in a series of interferograms. The SPINUA algorithm was used to process 40 ENVISAT ASAR images for the study period 2003–2010. The analysis resulted in the identification of over 140000 PS in the greater Lanzhou area covering some 300 km2. The spatial distribution of moving radar targets was checked during a field campaign and highlights the range of ground instability problems that the Lanzhou area faces as urban expansion continues to accelerate. The PS-InSAR application detected ground deformations with rates up to 10 mm a−1; it resulted in the detection of previously unknown unstable slopes and two areas of subsidence. Lanzhou is the capital of Gansu Province and is one of the most important industrial cities in NW China (Fig. 1). The 12th Five-Year Plan and the 2011 National Economic and Social Development Statistical Bulletin of Lanzhou City indicate that the gross domestic product (GDP) of Lanzhou more than doubled in the last decade, reaching some 136 billion Yuan (c. £13.6 billion). This is associated with a rapid increase in the urban population and current forecasts suggest that the remaining undeveloped land can sustain further development for only some 10–15 years (Yao 2008). Increasingly, people have to encroach on marginal areas having a greater potential for ground instability. Since 1949, a variety of geohazards (mainly comprising landslides, debris flows, soil collapse, subsidence and floods) in Lanzhou have caused some 676 deaths and an estimated cumulative direct economic loss of some 756 million Yuan (Ding & Li 2009; Dijkstra et al. 2014). It is expected that further casualties and economic impacts will result in this unstable landscape unless a better understanding of the spatial distribution and causes of typical geohazards involving ground instability can be implemented in the development of land-use management practices, urban planning and the design of mitigation strategies. Satellite-based radar interferometry provides an opportunity to map ground deformation over large areas of interest. This paper highlights the use of PS-InSAR (Permanent Scatterer Synthetic Aperture Radar Interferometry) in a region where an incomplete ground instability inventory exist

Improvement of the Accuracy of InSAR Image Co-Registration Based On Tie Points – A Review

Interferometric Synthetic Aperture Radar (InSAR) is a new measurement technology, making use of the phase information contained in the Synthetic Aperture Radar (SAR) images. InSAR has been recognized as a potential tool for the generation of digital elevation models (DEMs) and the measurement of ground surface deformations. However, many critical factors affect the quality of InSAR data and limit its applications. One of the factors is InSAR data processing, which consists of image co-registration, interferogram generation, phase unwrapping and geocoding. The co-registration of InSAR images is the first step and dramatically influences the accuracy of InSAR products. In this paper, the principle and processing procedures of InSAR techniques are reviewed. One of important factors, tie points, to be considered in the improvement of the accuracy of InSAR image co-registration are emphatically reviewed, such as interval of tie points, extraction of feature points, window size for tie point matching and the measurement for the quality of an interferogram

A Macroecological Analysis of SERA Derived Forest Heights and Implications for Forest Volume Remote Sensing

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H100, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H100 and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 102–106 plants/hectare and heights 6–49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H100