Conceptual Study and Performance Analysis of Tandem Dual-Antenna Spaceborne SAR Interferometry

Multi-baseline synthetic aperture radar interferometry (MB-InSAR), capable of mapping 3D surface model with high precision, is able to overcome the ill-posed problem in the single-baseline InSAR by use of the baseline diversity. Single pass MB acquisition with the advantages of high coherence and simple phase components has a more practical capability in 3D reconstruction than conventional repeat-pass MB acquisition. Using an asymptotic 3D phase unwrapping (PU), it is possible to get a reliable 3D reconstruction using very sparse acquisitions but the interferograms should follow the optimal baseline design. However, current spaceborne SAR system doesn't satisfy this principle, inducing more difficulties in practical application. In this article, a new concept of Tandem Dual-Antenna SAR Interferometry (TDA-InSAR) system for single-pass reliable 3D surface mapping using the asymptotic 3D PU is proposed. Its optimal MB acquisition is analyzed to achieve both good relative height precision and flexible baseline design. Two indicators, i.e., expected relative height precision and successful phase unwrapping rate, are selected to optimize the system parameters and evaluate the performance of various baseline configurations. Additionally, simulation-based demonstrations are conducted to evaluate the performance in typical scenarios and investigate the impact of various error sources. The results indicate that the proposed TDA-InSAR is able to get the specified MB acquisition for the asymptotic 3D PU, which offers a feasible solution for single-pass 3D SAR imaging.Comment: 16 pages, 20 figure

Unwrapped phase estimation via normalized probability density function for multibaseline InSAR

Interferometric synthetic aperture radar (InSAR) is a powerful technique for obtaining terrain information based on the interferometric phase. Multibaseline (MB) InSAR is an extension of the conventional InSAR and is used to improve the estimation accuracy and reliability of the unwrapped phase. Based on a newly defined normalized phase probability density function (pdf), a novel wrapped-to-unwrapped phase (W2UP) estimation method is proposed for MB-InSAR. First, the concept of the normalized pdf is introduced to overcome the limitation of the fixed 2π period for different baseline cases. Then, a new maximum likelihood estimation is established using the MB normalized pdfs, which has a much steeper peak around the true phase value than the single baseline case and leads to higher estimation accuracy. The proposed W2UP method estimates the unwrapped phase from multiple filtered interferograms, so it is less influenced by the phase noise. Both the theoretical analysis and results using the simulated and real MB data are provided to verify the effectiveness of the proposed method

A Method for Selecting SAR Interferometric Pairs Based on Coherence Spectral Clustering

To achieve accurate interferometric synthetic aperture radar (SAR) phase estimation, it is essential to select appropriate high-coherence interferometric pairs from massive SAR single-look complex (SLC) image data. The selection should include as many high-coherence interferometric pairs as possible while avoiding low-coherence pairs. By combining coherence and spectral clustering, a novel selection method for SAR interferometric pairs is proposed in this article. The proposed method can be adopted to classify SAR SLC images into different clusters, where the total coherence of interferometric pairs in the same cluster is maximized while that among different clusters is minimized. This is implemented by averaging the coherence matrices of representative pixels to construct an adjacency matrix and performing eigenvalue decomposition for estimating the number of clusters. The effectiveness of the proposed method is demonstrated using 33 TerraSAR-X and 38 dual-polarization Sentinel-1A data samples, yielding improved topography and deformation monitoring results

Passive Interfering Method for InSAR Based on Circularly Moving Strong Scatterers

A novel jamming method based on circularly moving strong scatterers is proposed. The jamming signal model is presented first, and the corresponding imaging results are derived through a range-Doppler algorithm. Detailed analysis shows that the proposed method can decrease the correlation, produce interferometric phase bias, result in failure of phase unwrapping, and reduce the accuracy of the digital elevation model. Simulation results are provided to verify the effectiveness of the proposed method

Variational Bayes Phase Tracking for Correlated Dual-Frequency Measurements with Slow Dynamics

We consider the problem of estimating the absolute phase of a noisy signal when this latter consists of correlated dual-frequency measurements. This scenario may arise in many application areas such as global navigation satellite system (GNSS). In this paper, we assume a slow varying phase and propose accordingly a Bayesian filtering technique that makes use of the frequency diversity. More specifically, the method results from a variational Bayes approximation and belongs to the class of nonlinear filters. Numerical simulations are performed to assess the performance of the tracking technique especially in terms of mean square error and cycle-slip rate. Comparison with a more conventional approach, namely a Gaussian sum estimator, shows substantial improvements when the signal-to-noise ratio and/or the correlation of the measurements are low

Efficient Solution of Minimum Cost Flow Problems for Large-scale Transportation Networks

With the rapid advance of information technology in the transportation industry, of which intermodal transportation is one of the most important subfields, the scale and dimension of problem sizes and datasets is rising significantly. This trend raises the need for study on improving the efficiency, profitability and level of competitiveness of intermodal transportation networks while exploiting the rich information of big data related to these networks. Therefore, this dissertation aims to investigate intermodal transportation network design problems, especially practical optimization problems, and to develop more realistic and effective models and solution approaches that will assist network operators and/or decision makers of the intermodal transportation system. This dissertation focuses on developing a novel strategy for solving the Minimum Cost Flow (MCF) problem for large-scale network design problems by adopting a divide-and-conquer policy during the optimization process. The main contribution is the development of an agglomerative clustering based tiling strategy to significantly reduce the computational and peak memory consumption of the MCF model for large-scale networks. The tiling strategy is supported by the regional-division theorem and -approximation regional-division theorem that are proposed and proved in this dissertation. The region-division theorem is a sufficient condition to exactly guarantee the consistency between the local MCF solution of each sub-network obtained by the aforementioned tiling strategy and the global MCF solution of the whole network. Furthermore, the -approximation region-division theorem provides worst-case bounds, so that the practical approximation MCF solution closely approximates the optimal solution in terms of its optimal value. A series of experiments are performed to evaluate the utility of the proposed approach of solving the large-scale MCF problem. The results indicate that the proposed approach is beneficial to save the execution time and peak memory consumption in large-scale MCF problems under different circumstances

Advanced pixel selection and optimization algorithms for Persistent Scatterer Interferometry (PSI)

Tesi amb diferents seccions retallades per dret de l'editorPremi Extraordinari de Doctorat, promoció 2018-2019. Àmbit de les TICGround deformation measurements can provide valuable information for minimization of associated loss and damage caused by natural and environmental hazards. As a kind of remote sensing technique, Persistent Scatterer Interferometry (PSI) SAR is able to measure ground deformation with high spatial resolution, efficiently. Moreover, the ground deformation monitoring accuracy of PSI techniques can reach up to millimeter level. However, low coherence could hinderthe exploitation of SAR data, and high-accuracy deformation monitoring can only be achieved by PSI for high quality pixels. Therefore, pixel optimization and identification of coherent pixels are crucial for PSI techniques. In this thesis, advanced pixel selection and optimization algorithms have been investigated. Firstly, a full-resolution pixel selection method based on the Temporal Phase Coherence (TPC) has been proposed. This method first estimates noise phase term of each pixel at interferogram level. Then, for each pixel, its noise phase terms of all interferograms are used to assess this pixel’s temporal phase quality (i.e., TPC). In the next, based on the relationship between TPC and phase Standard Deviation (STD), a threshold can be posed on TPC to identify high phase quality pixels. This pixel selection method can work with both Deterministic Scatterers (PSs) and Distributed Scatterers (DSs). To valid the effectiveness of the developed method, it has been used to monitor the Canillo (Andorra) landslide. The results show that the TPC method can obtained highest density of valid pixels among the employed three approaches in this challenging area with X-band SAR data. Second, to balance the polarimetric DInSAR phase optimization effect and the computation cost, a new PolPSI algorithm is developed. This proposed PolPSI algorithm is based on the Coherency Matrix Decomposition result to determine the optimal scattering mechanism of each pixel, thus it is named as CMD-PolPSI. CMDPolPSI need not to search for solution within the full space of solution, it is therefore much computationally faster than the classical Equal Scattering Mechanism (ESM) method, but with lower optimization performance. On the other hand, its optimization performance outperforms the less computational costly BEST method. Third, an adaptive algorithm SMF-POLOPT has been proposed to adaptive filtering and optimizing PolSAR pixels for PolPSI applications. This proposed algorithm is based on PolSAR classification results to firstly identify Polarimetric Homogeneous Pixels (PHPs) for each pixel, and at the same time classify PS and DS pixels. After that, DS pixels are filtered by their associated PHPs, and then optimized based on the coherence stability phase quality metric; PS pixels are unfiltered and directly optimized based on the DA phase quality metric. SMF-POLOPT can simultaneously reduce speckle noise and retain structures’ details. Meanwhile, SMF-POLOPT is able to obtain much higher density of valid pixels for deformation monitoring than the ESM method. To conclude, one pixel selection method has been developed and tested, two PolPSI algorithms have been proposed in this thesis. This work make contributions to the research of “Advanced Pixel Selection and Optimization Algorithms for Persistent Scatterer InterferometryLes mesures de deformació del sòl poden proporcionar informació valuosa per minimitzar les pèrdues i els danys associats causats pels riscos naturals i ambientals. Com a tècnica de teledetecció, la interferometria de dispersors persistents (Persistent Scatter Interferometry, PSI) SAR és capaç de mesurar de forma eficient la deformació del terreny amb una alta resolució espacial. A més, la precisió de monitorització de la deformació del sòl de les tècniques PSI pot arribar a arribar a nivells del mil·límetre. No obstant això, una baixa coherència pot dificultar l’explotació de dades SAR i el control de deformació d’alta precisió només es pot aconseguir mitjançant PSI per a píxels d’alta qualitat. Per tant, l’optimització de píxels i la identificació de píxels coherents són crucials en les tècniques PSI. En aquesta tesi s¿han investigat algorismes avançats de selecció i optimització de píxels. En primer lloc, s'ha proposat un mètode de selecció de píxels de resolució completa basat en la coherència temporal de fase (Temporal Phase Coherence, TPC). Aquest mètode estima per primera vegada el terme de fase de soroll de cada píxel a nivell d’interferograma. A continuació, per a cada píxel, s'utilitzen els termes de la fase de soroll de tots els interferogrames per avaluar la qualitat de fase temporal d'aquest píxel (és a dir, TPC). A la següent, basant-se en la relació entre el TPC i la desviació estàndard de fase (STD), es pot plantejar un llindar de TPC per identificar píxels de qualitat de fase alta. Aquest mètode de selecció de píxels es capaç de detectar tant els dispersors deterministes (PS) com els distribuïts (DS). Per validar l’eficàcia del mètode desenvolupat, s’ha utilitzat per controlar l’esllavissada de Canillo (Andorra). Els resultats mostren que el mètode TPC pot obtenir la major densitat de píxels vàlids, comparat amb els mètodes clàssics de selecció, en aquesta àrea difícil amb dades de SAR de banda X. En segon lloc, per equilibrar l’efecte d’optimització de fase DInSAR polarimètrica i el cost de càlcul, es desenvolupa un nou algorisme de PolPSI. Aquest algorisme proposat de PolPSI es basa en el resultat de la descomposició de la matriu de coherència per determinar el mecanisme de dispersió òptim de cada píxel, de manera que es denomina CMD-PolPSI. CMDPolPSI no necessita buscar solucions dins de l’espai complet de la solució, per tant, és molt més eficient computacionalment que el mètode clàssic de mecanismes d’igualtat de dispersió (Equal Scattering Mechanism, ESM), però amb un efecte d’optimització no tant òptim. D'altra banda, el seu efecte d'optimització supera el mètode BEST, el que te un menor cost computacional. En tercer lloc, s'ha proposat un algoritme adaptatiu SMF-POLOPT per al filtratge adaptatiu i l'optimització de píxels PolSAR per a aplicacions PolPSI. Aquest algorisme proposat es basa en els resultats de classificació PolSAR per identificar primer els píxels homogenis polarimètrics (PHP) per a cada píxel i, alhora, classificar els píxels PS i DS. Després d'això, els píxels DS es filtren pels seus PHP associats i, a continuació, s'optimitzen en funció de la mètrica de qualitat de la fase d'estabilitat de coherència; els píxels classificats com PS no es filtren i s'optimitzen directament en funció de la mètrica de qualitat de la fase DA. SMF-POLOPT pot reduir simultàniament el soroll de la fase interferomètrica i conservar els detalls de les estructures. Mentrestant, SMF-POLOPT aconsegueix obtenir una densitat molt més alta de píxels vàlids per al seguiment de la deformació que el mètode ESM. Per concloure, en aquesta tesi s’ha desenvolupat i provat un mètode de selecció de píxels, i s’han proposat dos algoritmes PolPSI. Aquest treball contribueix a la recerca en "Advanced Pixel Selection and Optimization Algorithms for Persistent Scatterer Interferometry"Postprint (published version

An insight in cloud computing solutions for intensive processing of remote sensing data

The investigation of Earth's surface deformation phenomena provides critical insights into several processes of great interest for science and society, especially from the perspective of further understanding the Earth System and the impact of the human activities. Indeed, the study of ground deformation phenomena can be helpful for the comprehension of the geophysical dynamics dominating natural hazards such as earthquakes, volcanoes and landslide. In this context, the microwave space-borne Earth Observation (EO) techniques represent very powerful instruments for the ground deformation estimation. In particular, Small BAseline Subset (SBAS) is regarded as one of the key techniques, for its ability to investigate surface deformation affecting large areas of the Earth with a centimeter to millimeter accuracy in different scenarios (volcanoes, tectonics, landslides, anthropogenic induced land motions). The current Remote Sensing scenario is characterized by the availability of huge archives of radar data that are going to increase with the advent of Sentinel-1 satellites. The effective exploitation of this large amount of data requires both adequate computing resources as well as advanced algorithms able to properly exploit such facilities. In this work we concentrated on the use of the P-SBAS algorithm (a parallel version of SBAS) within HPC infrastructure, to finally investigate the effectiveness of such technologies for EO applications. In particular we demonstrated that the cloud computing solutions represent a valid alternative for scientific application and a promising research scenario, indeed, from all the experiments that we have conducted and from the results obtained performing Parallel Small Baseline Subset (P-SBAS) processing, the cloud technologies and features result to be absolutely competitive in terms of performance with in-house HPC cluster solution

화산 활동 관측을 위한 SAR 간섭 기법에서의 대기 보정 기법

학위논문 (석사)-- 서울대학교 대학원 : 지구환경과학부, 2013. 2. 김덕진.Ground deformation in volcano is a consequence of changes in magma chambers volume. Magma storage, migration and volume change is closely associated phenomena with the ground deformation. Therefore, measuring ground deformation provides important information to understand the volcanic activities. For some specific volcanoes, such as Shinmoedake volcano, ground deformation of even a few centimeters can occur before eruption. Thus, measuring ground deformation needs to be fairly accurate. SAR interferometry is a potential technique to measure the ground deformation accurately. One of the limitations in SAR interferometry, however, is atmospheric phase delay effects, which are induced when microwave propagates into the atmosphere. In this aspect, various methods for mitigating atmospheric phase delay effects have been developed. This study aims to mitigate the atmospheric phase delay especially in volcano because the stratified and turbulent atmospheric phase delay effects could severely contaminate the deformation patterns. First method used in this study is the atmospheric correction technique using MODIS data. Multispectral observation can measure the integrated water vapor in the atmosphere by analyzing ratios of water vapor absorbing channel and atmospheric window channel. It can be directly used for calculating the tropospheric phase delay effect caused by water vapor. Recent researches using multispectral datasets are restricted to approach using ENVISAT. Therefore, new approach is necessary in application using ALOS PALSAR. This study evaluates the applicability and possibility. In adequate temporal difference and cloud coverage, available datasets of MODIS successfully converted to the atmospheric phase delay corresponding to SAR acquisition time. However, there are some limitations in application into all dataset because of the cloud cover and temporal difference between the SAR acquisition time and MODIS acquisition time. In spite of limitations, the use of MODIS data in atmospheric correction yield better results and minimize misinterpreted errors. The WRF model complements the limitations of MODIS data. In this respect, an application of the WRF model in atmospheric correction of differential interferogram was carried out in the second methods. The estimated APS from the WRF model can explain the stratified APS involved in differential interferograms. However, the accuracy of model prediction should be evaluated. The direct use of the WRF model predictions for atmospheric correction yield errors for mitigating the turbulent APS and the small-scaled APS. Final approach is a time-series analysis. In model experiments, several properties of atmospheric phase screen (APS) are found out. The first is that APS could remain in a time-series analysis and mainly comes from the stratified APS. The second is that it is possible to estimate and minimize the stratified APS by using sufficient WRF models. In the case of the turbulent APS, time-weighting low pass filtering is capable to reduce it. Therefore, the main idea of the atmosphere corrected time-series analysis adopt the stratified APS and turbulent APS correction method using WRF model and time-weighting methods. In comparison with observational dataset such as GPS and MODIS dataset, the estimated ground deformation and APS from the atmosphere corrected method have low rms errors, and high correlation. Therefore, this method can be believed as an accurate approach for measuring the ground deformation in volcanic region.1. INTRODUCTION 15 1.1. SAR INTERFEROMETRY AND VOLCANO MONITORING 15 1.2. ATMOSPHERIC PHASE DELAY IN INSAR 17 1.3. OBJECTIVES OF THIS RESEARCH 20 2. THE THEORETICAL BASIC OF SAR INTERFEROMETRY AND TIME-SERIES ANALYSIS 22 2.1. SAR INTERFEOMETRY 22 2.2. DIFFERENTIAL SAR INTERFEROMETRY 28 2.3. TIME-SERIES ANALYSIS 35 3. STUDY AREA AND DATASET 43 3.1. STUDY AREA 43 3.2. DATA 45 4. ATMOSPHERIC CORRECTION IN INDIVIDUAL DIFFERENTIAL INTERFEROGRAMS 50 4.1. DIFFERENTIAL SAR INTERFEROMETRY 50 4.2. ATMOSPHERIC PHASE DELAY EFFECTS SIMULATION 50 4.3. RESULTS 63 5. ATMOSPHERIC CORRECTION USING TIME-SERIES ANALYSIS 70 5.1. APS ESTIMATION ERRORS IN TIME-SERIES INSAR 71 5.2. PROPERTIES OF APS IN TIME AND SPACE 76 5.3. APPLICATION TO AVAILABLE DATASET AND DATA PROCESSING 88 5.4. COMPARISON BETWEEN CONVENTIONAL AND ATMOSPHERE CORRECTED TIME SERIES ANALYSIS 95 5.5. VALIDATION 101 6. CONCLUSION 108Maste