Single Look Complex Research Articles

BSF: Block Subspace Filter for Removing Narrowband and Wideband Radio Interference Artifacts in Single-Look Complex SAR Images

Radio signals emitted by various sources, such as ground radars and broadcast/communication devices, can unintentionally cause radio frequency interference (RFI) to spaceborne synthetic aperture radar (SAR), degrading SAR image qualities to various degrees. Most existing methods tackle this problem by applying specially designed preprocessing steps to RFI-polluted level-0 SAR data before SAR focusing. However, such preprocessing is not widely used in spaceborne SAR, as there exist radiometric artifacts due to various RFI sources in the level-1 single-look complex (SLC) image products in many spaceborne SAR data, e.g., Sentinel-1 open data archives. To address this problem, in this article, we first propose a generic subspace model for characterizing a variety of RFI types, which reveals a low-dimensional structure of RFI subspace. Based on the proposed model, we next design a block subspace filter (BSF) for removing RFI artifacts in SLC SAR images directly. Experiments with ERS-2, ENVISAT/ASAR, Sentinel-1, and Gaofen-3 data are presented, and quantitative assessments based on numerical simulations are provided, which demonstrates the promising performance and application potentials of the proposed method. BSF is simple yet efficient and does not require performing preprocessing on level-0 raw data, which is helpful for users to obtain clean SAR images. MATLAB/Octave code implementation of BSF is available at <uri>https://github.com/huizhangyang/BSF</uri>.

On the Use of Ocean Surface Doppler Velocity for Oceanic Front Extraction From Chinese Gaofen-3 SAR Data

Oceanic front extraction from synthetic aperture radar (SAR) images is of great significance to the study of marine ecosystems. At present, the methods for oceanic front extraction are usually based on SAR intensity images, which treat oceanic fronts as edge-like features in the SAR images. However, affected by radar parameters and sea state, sometimes, oceanic front signatures may not be clearly visible in the SAR intensity images. Therefore, existing methods are limited. In order to solve this problem, a method combining intensity and Doppler information for oceanic front extraction is proposed. Using Chinese Gaofen-3 single-look complex (SLC) data, three cases where oceanic front signatures are clearly visible, partially visible, and extremely weak in the SAR intensity images are investigated, which demonstrate how the Doppler velocity gradient across a front can be leveraged to enhance the extraction of oceanic fronts from SAR data. The results show that the Doppler data, as supplementary information, not only can complement the oceanic fronts extracted from SAR intensity images, but also can be used as a reference for the oceanic fronts extracted from SAR intensity images.

As If by Magic: Self-Supervised Training of Deep Despeckling Networks With MERLIN

Speckle fluctuations seriously limit the interpretability of synthetic aperture radar (SAR) images. Speckle reduction has thus been the subject of numerous works spanning at least four decades. Techniques based on deep neural networks have recently achieved a new level of performance in terms of SAR image restoration quality. Beyond the design of suitable network architectures or the selection of adequate loss functions, the construction of training sets is of uttermost importance. So far, most approaches have considered a supervised training strategy: the networks are trained to produce outputs as close as possible to speckle-free reference images. Speckle-free images are generally not available, which requires resorting to natural or optical images or the selection of stable areas in long time series to circumvent the lack of ground truth. Self-supervision, on the other hand, avoids the use of speckle-free images. We introduce a self-supervised strategy based on the separation of the real and imaginary parts of single-look complex SAR images, called MERLIN (coMplex sElf-supeRvised despeckLINg), and show that it offers a straightforward way to train all kinds of deep despeckling networks. Networks trained with MERLIN take into account the spatial correlations due to the SAR transfer function specific to a given sensor and imaging mode. By requiring only a single image, and possibly exploiting large archives, MERLIN opens the door to hassle-free as well as large-scale training of despeckling networks. The code of the trained models is made freely available at https://gitlab.telecom-paris.fr/RING/MERLIN.

Kotrupi landslide deformation study in non-urban area using DInSAR and MTInSAR techniques on Sentinel-1 SAR data

The DInSAR (Differential Interferometric SAR) and MTInSAR (Multi-Temporal Interferometric SAR) techniques are used to examine a surface displacement in the Kotrupi Region in this paper. DInSAR and MTInSAR are capable of determining the surface deformation of a rapidly moving landslide. The Vertical Line of Sight (LOS) displacements were estimated with millimeter accuracy using a DInSAR technique followed by phase unwrapping. MTInSAR approaches using APS (Atmospheric Phase Screen) and sparse point processing, on the other hand, measure earth surface displacements and track their trends over time. DInSAR is limited by several difficulties, including phase decorrelation errors, phase unwrapping errors, and atmospheric phase disturbances, all of which reduce the quality of deformation effects. MTInSAR approaches, on the other hand, use data from several different periods within the same temporal baseline. As a result, the effect atmospheric phase disturbances and unwrapping error is greatly decreased when compared to classic DInSAR approaches. After the Kotrupi landslide of August 13, 2017, conventional DInSAR with a single pair of SLC (Single Look Complex) Sentinel-1 SAR images showed a maximum displacement value of about −60 mm to 60 mm in the Kotrupi landslide runoff region. While the advanced MTInSAR results indicate a maximum rate of velocity of about −45 mm/year for the continuous deformation happening in the Kotrupi landslide area. The coherence values along Pathankot National Highway (NH) −154 before and after the landslide were 0.857 and 0.019, respectively. Deformation studies before and after the landslide occurrence are also conducted using a DEM (Digital Elevation Model). Based on the DEM obtained, erosional and depositional deformations of −3.5 m and +3.0 m have been recorded in the Kotrupi landslide runoff area, resulting in 6–7 m change and material loss. The Root Mean Square Error (RMSE) for MTInSAR DEM 0.787 m and DInSAR DEM 1.026 m was estimated after the DEMs produced from DInSAR and MTInSAR processing were compared with TanDEM-X. MTInSAR produces much better results than DInSAR in non-urban areas.

Efficient SAR Azimuth Ambiguity Reduction in Coastal Waters Using a Simple Rotation Matrix: The Case Study of the Northern Coast of Jeju Island

Azimuth ambiguities, or ghosts on SAR images, represent one of the main obstacles for SAR applications involving coastal monitoring activities such as ship detection. While most previous methods based on azimuth antenna pattern and direct filtering are effective for azimuth ambiguity suppression, they may not be effective for fast cruising small ships. This paper proposes a unique approach for the reduction of azimuth ambiguities or ghosts in SAR single-look complex (SLC) images using a simple rotation matrix. It exploits the fact that the signal powers of azimuth ambiguities are concentrated on narrow bands, while those of vessels or other true ground targets are dispersed over broad bands. Through sub-aperture processing and simple axis rotation, it is possible to concentrate the dispersed energy of vessels onto a single axis while the ghost signal powers are dispersed onto three different axes. Then, the azimuth ambiguities can be easily suppressed by a simple calculation of weighted sum and difference, while preserving vessels. Applied results achieved by processing TerrSAR-X SLC images are provided and discussed. An optimum weight of 0.5 was determined by Receiver Operating Characteristic (ROC) analysis. Capabilities of ship detection from the test image were significantly improved by removing 93% of false alarms. Application results demonstrate its high performance of ghost suppression. This method can be employed as a pre-processing tool of SAR images for ship detection in coastal waters.

Persistent Scatterer Interferometry for Pettimudi (India) Landslide Monitoring using Sentinel-1A Images

The continuous monitoring of land surface movement over time is of paramount importance for assessing landslide triggering factors and mitigating landslide hazards. This research focuses on measuring horizontal and vertical surface displacement due to a devastating landslide event in the west-facing slope of the Rajamala Hills, induced by intense rainfall. The landslide occurred in Pettimudi, a tea-plantation village of the Idukki district in Kerala, India, on August 6–7, 2020. The persistent-scatterer synthetic aperture radar interferometry (PSInSAR ) technique, along with the Stanford Method for Persistent Scatterers (StaMPS), was applied to investigate the land surface movement over time. A stack of 20 Sentinel-1A single-look complex images (19 interferograms) acquired in descending passes was used for PSInSAR processing. The line-of-sight (LOS ) displacement in long time series, and hence the average LOS velocity, was measured at each measurement-point location. The mean LOS velocity was decomposed into horizontal east–west (EW ) and vertical up–down velocity components. The results show that the mean LOS, EW, and up–down velocities in the study area, respectively, range from –18.76 to +11.88, –10.95 to +6.93, and –15.05 to +9.53 mm/y, and the LOS displacement ranges from –19.60 to +19.59 mm. The displacement values clearly indicate the instability of the terrain. The time-series LOS displacement trends derived from the applied PSInSAR technique are very useful for providing valuable inputs for disaster management and the development of disaster early-warning systems for the benefit of local residents.

C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)

Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in semi-arid areas where water resources are limited. Radar observations, available at high resolution and with a high revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared with the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gather soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/2 weeks including aboveground fresh and dry biomasses, vegetation water content based on destructive measurements, the cover fraction, the leaf area index, and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High Resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric-effects-corrected products is also provided. This database, which is the first of its kind made available open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation variable retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely accessible via the following DOI: https://doi.org/10.23708/8D6WQC (Ouaadi et al., 2020a).

Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters (θNovSAR) or the combination of 43 SAR and 10 terrain parameters (θNovSAR+Terrain). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model (θNovSAR+Terrain with 0.025 and 0.020 m3 m−3, and 89% compared to θNovSAR with 0.028 and 0.022 m3 m−3, and 86% in terms of RMSE, MAE, and R2). The higher explanatory power for θNovSAR+Terrain is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon.

Comparison of Target Detectors to Identify Icebergs in Quad-Polarimetric L-Band Synthetic Aperture Radar Data

Icebergs represent hazards to ships and maritime activities and therefore their detection is essential. Synthetic Aperture Radar (SAR) satellites are very useful for this, due to their capability to acquire data under cloud cover and during day and night passes. In this work, we compared six state-of-the-art polarimetric target detectors to test their performance and ability to detect small-sized icebergs &lt;120 m in four locations in Greenland. We used four single-look complex (SLC) ALOS-2 quad-polarimetric images from JAXA for quad-polarimetric detection and we compared with dual-polarimetric detectors using only the channels HH and HV. We also compared these detectors with single-polarimetric intensity channels and we tested using two scenarios: open ocean and sea ice. Our results show that the multi-look polarimetric whitening filter (MPWF) and the optimal polarimetric detector (OPD) provide the most optimal performance in quad- and dual-polarimetric mode detection. The analysis shows that, overall, quad-polarimetric detectors provide the best detection performance. When the false alarm rate (PF) is fixed to 10−5, the probabilities of detection (PD) are 0.99 in open ocean and 0.90 in sea ice. Dual-polarimetric or single-polarimetric detectors show an overall reduction in performance (the ROC curves show a decrease), but this degradation is not very large (&lt;0.1) when the value of false alarms is relatively high (i.e., we are interested in bigger icebergs with a brighter backscattering &gt;120 m, as they are easier to detect). However, the differences between quad- and dual- or single-polarimetric detectors became much more evident when the PF value was fixed to low detection probabilities 10−6 (i.e., smaller icebergs). In the single-polarimetric mode, the HV channel showed PD values of 0.62 for open ocean and 0.26 for sea ice, compared to values of 0.81 (open ocean) and 0.77 (sea ice) obtained with quad-polarimetric detectors.

InSAR constraints on the active deformation of salt diapirs in the Kalut basin, Central Iran

In recent years, salt diapirs have been widely used for gas storing, hazardous waste disposal, mining, and petroleum production. The rate and pattern of active movement in salt structures are crucial to be obtained before planning these industrial utilizations. The central basin of the Iranian plateau represents well-exposed and -preserved diapirs that can be considered as an analog for other salt basins around the world where the exposures are not as good. Integration of the Interferometric Synthetic Aperture Radar (InSAR) technique with remote-sensing structural mapping permits us to study the active surficial deformation of eight salt diapirs in the Kalut basin in central Iran. We classify the Kalut diapiric structures into salt walls, massifs, stocks, and welds. We then use 75 Single Look Complex (SLC) IW scenes acquired on the ascending collected from November 2014 to November 2018 and 62 SLC images on descending geometries collected from October 2014 to January 2018, both covering the same period. The Sentinel-1 images are processed by the Small BAseline Subset (SBAS) approaches to define vertical and horizontal displacement maps revealing that the Kalut basin is one of the most-active present-day diapiric systems in the world, its eight salt diapirs are rising, and none of them subsides. Maximum calculated rates of growth reach 8 mm/yr in vertical and ~ 2 mm/yr in horizontal directions, suggesting that in general vertical displacements play a significant role in the deformation of this salt-controlled basin. Salt stocks are uniformly rising in their crestal domain (up to 8 mm/yr) with lateral spreading along their flanks (1–2 mm/yr). The larger salt walls are more active and rising faster (8 mm/yr) than smaller ones (3 mm/yr), while welded areas are inactive. These rising salt diapirs are flanked by seasonal depocenters accumulating up to 10 mm/yr of fluvial, evaporitic, and aeolian deposits.

SAR Interference Suppression Algorithm Based on Low-Rank and Sparse Matrix Decomposition in Time–Frequency Domain

Radio frequency electromagnetic interference is a relatively common phenomenon, especially for synthetic aperture radar (SAR) systems working in P- or L-band. Compared with the suppression of narrowband interference, that of wideband interference, particularly of those whose signal parameters have frequently changing property, is still a sophisticated problem. In this letter, a suppression algorithm for interference with wideband and complicated parameters is proposed, based on low-rank and sparse matrix decomposition (LRSMD) in time–frequency domain (TFD) of the signal. The proposed algorithm begins with transforming the SAR signal into TFD. After that, LRSMD based on bilateral random projection (BRP) is applied to decompose the time–frequency spectrum matrix into three parts, a low-rank matrix standing for interference, a sparse matrix standing for SAR signal, and a noise matrix. Finally, inversely transform the sparse matrix into the time domain to obtain SAR signal without interference. The proposed algorithm is applied to a single look complex (SLC) SAR data of Sentinel-1 to validate its effect and efficiency.

Thermal Noise Removal From Polarimetric Sentinel-1 Data

This study proposes, for the first time, an approach to remove thermal noise from the wave coherency matrix, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {C_{2}}$ </tex-math></inline-formula> , estimated from single-look complex dual-polarization Interferometric Wide Swath mode Sentinel-1 synthetic aperture radar data. The approach is straightforward; it exploits the ThermalNoiseRemoval module, provided by the European Space Agency (ESA) in its Sentinel Application Platform (SNAP) software, to remove thermal noise from the channel intensities. Then, noise correction on the complex data is applied, in order to estimate the noise-free <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {C_{2}}$ </tex-math></inline-formula> matrix. As a further novelty, the proposed approach can be implemented in SNAP, through the use of a processing graph that is here provided. The method is applied on a dense time series of Sentinel-1 data, collected on an agricultural area located near Seville, Spain. The impact of thermal noise on the estimation of the eigendecomposition parameters of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {C_{2}}$ </tex-math></inline-formula> , i.e., entropy ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{2}$ </tex-math></inline-formula> ), average alpha angle ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\overline {\alpha _{2}}$ </tex-math></inline-formula> ), and anisotropy ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A_{2}$ </tex-math></inline-formula> ), is assessed for different land-cover types, namely river, rice, forest, and urban areas. Monte Carlo simulations are implemented to assess the performance of the proposed approach in estimating <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{2}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\overline {\alpha _{2}}$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A_{2}$ </tex-math></inline-formula> . Results show that the proposed noise removal method improves the estimation of these parameters for the considered land-cover classes.

Φ-Net: Deep Residual Learning for InSAR Parameters Estimation

Nowadays, deep learning (DL) finds application in a large number of scientific fields, among which the estimation and the enhancement of signals disrupted by the noise of different natures. In this article, we address the problem of the estimation of the interferometric parameters from synthetic aperture radar (SAR) data. In particular, we combine convolutional neural networks together with the concept of residual learning to define a novel architecture, named Φ-Net, for the joint estimation of the interferometric phase and coherence. Φ-Net is trained using synthetic data obtained by an innovative strategy based on the theoretical modeling of the physics behind the SAR acquisition principle. This strategy allows the network to generalize the estimation problem with respect to: 1) different noise levels; 2) the nature of the imaged target on the ground; and 3) the acquisition geometry. We then analyze the Φ-Net performance on an independent data set of synthesized interferometric data, as well as on real InSAR data from the TanDEM-X and Sentinel-1 missions. The proposed architecture provides better results with respect to state-of-the-art InSAR algorithms on both synthetic and real test data. Finally, we perform an application-oriented study on the retrieval of the topographic information, which shows that Φ-Net is a strong candidate for the generation of high-quality digital elevation models at a resolution close to the one of the original single-look complex data.

Deformation Monitoring of Slopes With a Shipborne InSAR System: A Case Study of the Lancang River Gorge

Differential synthetic aperture radar interferometry (DInSAR) is a remote sensing technique to monitor ground deformation using repeat-pass data and suitable processing methods. Different platforms have been used to acquire DInSAR data such as space-borne, air-borne, car-mounted, and ground-based SAR systems. Space-borne SAR systems are relatively mature, but air-borne and other systems have attracted increasing interest of the remote sensing community due to their operational flexibility and observation capabilities. In this paper, a new InSAR platform is proposed for the deformation monitoring of reservoir slopes. The shipborne InSAR system uses frequency modulated continuous wave (FMCW) technology with one-transmit-dual-receive configuration. First, we describe the data acquisition process at the Lancang River Bank in October 2019. Subsequently, we present the imaging and interferometric capabilities of the new shipborne InSAR system. Moreover, we analyze the acquired radar data and present an assessment of the quality of the single look complex (SLC) SAR imagery and interferometric products. We focus on the influence of platform stability and baseline bias. Finally, we compare the calculated deformation with the measured deformation obtained from corner reflectors deployed in the monitored area. A line-of-sight error within 2cm is obtained. This paper proposes a novel solution for deformation monitoring of reservoir slopes and a new approach for InSAR applications.

Geomorphologic Domination on Urban Sprawl of Southern Riyadh, Saudi Arabia Using Differential Interferometric Synthetic Aperture Radar (DInSAR)

This study tests the southern part of the Riyadh City growth domination by the Early Quaternary-Holocene trans-tensional Central Arabian graben system reactivation and the subsequent dissolution-induced collapses and karstification. This study utilizes Synthetic Aperture Radar (SAR) and Differential Interferometric Synthetic Aperture Radar (DinSAR) to examine the morphology of arid landscape, south of Riyadh. Eight Single Look Complex (SLC) amplitude images are calibrated, filtered, georeferenced and orthorectified at a resolution of 20 meters, and compared with one another by producing 17 diachronic images of the pairs at different intervals (1996, 2003-2005, 2008). The diachronic SAR intensity imageries suggest a downthrown displacement reaching 600 m and eastward tilting at the bottoms of the grabens. Also, the structurally-controlled valleys are developing an eastward-running drainage system towards the oasis of Al-Kharj and capturing an older hydrologic system. Moreover, a 12-year period (1996-2008) of the SAR data was obtained to examine the average annual rate of southern Riyadh’s urban sprawl, which is estimated at approximately 390 metres/year over the 12 years and constrained by geomorphological features towards the deformed area. DInSAR imageries show the primary results obtained from the 26 May 2004 and 31 Jan. 2005 pair of images, merged with 30 m resolution DEM-SRTM data for the arid region south of Riyadh to eliminate the influence of topography. DInSAR is applied in this study for its ability to detect small displacements at the centimetre scale (1/2 wavelength). Although the DInSAR’s coherence and phase imageries suggest a fairly stable region since the last tectonic and subsequent geomorphic events, erosional and artificial changes are observed, bounded within the valleys and depressions, primarily due to aeolian and fluvial processes and agriculture. It is highly recommended to preserve the area for sustainability and economy.

Deriving Digital Surface Models from Geocoded SAR Images and Back-Projection Tomography

Digital surface models (DSMs) are sets of elevation data of the Earth's surface, useful for applications such as urban studies and height estimation of buildings. They can be derived from a set of synthetic aperture radar (SAR) images acquired in an interferometric or tomographic configuration. Each image acquisition is usually focused in radar geometry. In this work, we present steps required to derive DSMs from SAR single-look complex (SLC) products focused in map geometry (geocoded). We modified existing tomographic reconstruction techniques to be able to operate with geocoded SLCs and extended methods to operate with 3-D geocoded SLCs. The performance analysis showed that methods using 3-D geocoded SLC products yielded DSMs with fewer outliers and retained more information of the illuminated area, with a cost of higher computational complexity. Compressive sensing methods using 2-D geocoded SLCs can be a good alternative due to their comparatively moderate computational complexity.

CP-IRGS: A Region-Based Segmentation of Multilook Complex Compact Polarimetric SAR Data

The Canadian RADARSAT constellation mission (RCM) is represented by three synthetic aperture radar (SAR) satellites, each of which includes a compact polarimetry (CP) mode. CP is advantageous because it provides increased backscatter information relative to single and conventional dual-polarized modes and has larger swath widths relative to a quad polarization mode. CP captures single-look complex data which can be used to derive the multilook complex (MLC) coherence matrix, or, equivalently, the Stokes vector data of the backscattered field. The challenge is to develop computer vision algorithms that can be used to effectively segment the scene using this new data source. An unsupervised region-based segmentation approach has been designed and implemented that utilizes the complex Wishart distribution characteristic of the MLC CP data. The segmentation method is based on the iterative region growing with semantics algorithm originally designed for single and dual pol intensity SAR data. The algorithm has been tested using both simulated CP SAR images and a pair of available quad polarization SAR images. The results demonstrate that the CP-IRGS algorithm provides more accurate segmentation images than those using only the RH and RV channel intensity images.

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

Clear-Cut Detection and Mapping Using Sentinel-1 Backscatter Coefficient and Short-Term Interferometric Coherence Time Series

Forest clear-cut detection would be valuable for forest management, if it could be done routinely in near-real-time with a spaceborne synthetic aperture radar (SAR) system, which provides data all year and all-weather. In Sentinel-1 (S-1) time-series data, a forest clearing will lead to reduced backscatter intensity and increased interferometric SAR (InSAR) coherence magnitude. A time-series of 108 interferomtric wide (IW) Single look complex (SLC) S-1 images collected in 2016, 2017, and 2018 are used to study the potential for mapping clear-cut areas in eastern Ireland. We combined multitemporal InSAR coherence and backscatter intensity for the detection. This is an extension of previous studies that used either backscatter intensity or InSAR coherence magnitude, while we show the added value of both together. Coherence magnitude was the strongest predictor of the two.

Extraction of Land Information, Future Landscape Changes and Seismic Hazard Assessment: A Case Study of Tabriz, Iran.

Exact land cover inventory data should be extracted for future landscape prediction and seismic hazard assessment. This paper presents a comprehensive study towards the sustainable development of Tabriz City (NW Iran) including land cover change detection, future potential landscape, seismic hazard assessment and municipal performance evaluation. Landsat data using maximum likelihood (ML) and Markov chain algorithms were used to evaluate changes in land cover in the study area. The urbanization pattern taking place in the city was also studied via synthetic aperture radar (SAR) data of Sentinel-1 ground range detected (GRD) and single look complex (SLC). The age of buildings was extracted by using built-up areas of all classified maps. The logistic regression (LR) model was used for creating a seismic hazard assessment map. From the results, it can be concluded that the land cover (especially built-up areas) has seen considerable changes from 1989 to 2020. The overall accuracy (OA) values of the produced maps for the years 1989, 2005, 2011 and 2020 are 96%, 96%, 93% and 94%, respectively. The future potential landscape of the city showed that the land cover prediction by using the Markov chain model provided a promising finding. Four images of 1989, 2005, 2011 and 2020, were employed for built-up areas’ land information trends, from which it was indicated that most of the built-up areas had been constructed before 2011. The seismic hazard assessment map indicated that municipal zones of 1 and 9 were the least susceptible areas to an earthquake; conversely, municipal zones of 4, 6, 7 and 8 were located in the most susceptible regions to an earthquake in the future. More findings showed that municipal zones 1 and 4 demonstrated the best and worst performance among all zones, respectively.