Jixian Zhang

Radar image and data fusion for natural hazards characterisation

Fusion of synthetic aperture radar (SAR) images through interferometric, polarimetric and tomographic processing provides an all-weather imaging capability to characterise and monitor various natural hazards. This article outlines interferometric synthetic aperture radar (InSAR) processing and products and their utility for natural hazards characterisation, provides an overview of the techniques and applications related to fusion of SAR/InSAR images with optical and other images and highlights the emerging SAR fusion technologies. In addition to providing precise land-surface digital elevation maps, SAR-derived imaging products can map millimetre-scale elevation changes driven by volcanic, seismic and hydrogeologic processes, by landslides and wildfires and other natural hazards. With products derived from the fusion of SAR and other images, scientists can monitor the progress of flooding, estimate water storage changes in wetlands for improved hydrological modelling predictions and assessments of future flood impacts and map vegetation structure on a global scale and monitor its changes due to such processes as fire, volcanic eruption and deforestation. With the availability of SAR images in near real-time from multiple satellites in the near future, the fusion of SAR images with other images and data is playing an increasingly important role in understanding and forecasting natural hazards.

Monitoring of urban subsidence with SAR interferometric point target analysis: A case study in Suzhou, China

Abstract Ground subsidence, mainly caused by over exploitation of groundwater and other underground resources, such as oil, gas and coal, occurs in many cities in China. The annual direct loss associated with subsidence across the country is estimated to exceed 100 million US dollar. Interferometric SAR (InSAR) is a powerful tool to map ground deformation at an unprecedented level of spatial detail. It has been widely used to investigate the deformation resulting from earthquakes, volcanoes and subsidence. Repeat-pass InSAR, however, may fail due to impacts of spatial decorrelation, temporal decorrelation and heterogeneous refractivity of atmosphere. In urban areas, a large amount of natural stable radar reflectors exists, such as buildings and engineering structures, at which radar signals can remain coherent during a long time interval. Interferometric point target analysis (IPTA) technique, also known as persistent scatterers (PS) InSAR is based on these reflectors. It overcomes the shortfalls in conventional InSAR. This paper presents a procedure for urban subsidence monitoring with IPTA. Calculation of linear deformation rate and height residual, and the non-linear deformation estimate, respectively, are discussed in detail. Especially, the former is highlighted by a novel and easily implemented 2-dimensional spatial search algorithm. Practically useful solutions that can significantly improve the robustness of IPTA, are recommended. Finally, the proposed procedure is applied to mapping the ground subsidence in Suzhou city, Jiangsu province, China. Thirty-four ERS-1/2 SAR scenes are analyzed, and the deformation information over 38,881 point targets between 1992 and 2000 are generated. The IPTA-derived deformation estimates correspond well with leveling measurements, demonstrating the potential of the proposed subsidence monitoring procedure based on IPTA technique. Two shortcomings of the IPTA-based procedure, e.g., the requirement of large number of SAR images and assumed linear plus non-linear deformation model, are discussed as the topics of further research.

A Uniform SIFT-Like Algorithm for SAR Image Registration

In this letter, a uniform scale-invariant feature transform (SIFT)-like algorithm is proposed for synthetic aperture radar (SAR) image registration, which can extract enough robust, reliable, and uniformly distributed features by the strategies of optimal feature selection based on a Voronoi diagram and feature scale-space proportional extraction. SAR images, taken from different viewpoints by an airborne sensor and at different times by spaceborne sensors, were used as test data to validate the effectiveness of the proposed algorithm. The indexes of local density and global coverage were used to assess the spatial distribution of matches. Compared with the traditional SIFT-like algorithm for SAR images (SAR-SIFT), the results show that the proposed algorithm can increase the number of matches and optimize their spatial distribution.

Land Cover Classification from Polarimetric SAR Data Based on Image Segmentation and Decision Trees

Abstract. The aim of this article is to present a new classification scheme for polarimetric synthetic aperture radar (POLSAR) data by integrating image segmentation and decision tree. In the first stage of the scheme, POLSAR data is segmented using the multiresolution algorithm embedded in the Definiens eCognition software into small segments consisting of homogeneous pixels. For each segment, an average coherency matrix is generated to represent the overall polarimetric characteristics. In the second stage, 5 elaborately chosen parameters, including the H, A, α parameters from Cloude and Pottier decomposition and 2 parameters associated with the depolarization effect of radar targets, form the decision attributes to classify the segments. An L-band POLSAR image over Dutch Flevoland acquired by the airborne synthetic aperture radar (AIRSAR) system is used as test data. The proposed scheme achieves 13% higher overall accuracy when compared with the widely adopted Wishart classifier, and comparable or higher accuracy when compared with other segment-based algorithms. The 2 depolarization-related parameters, which have never been used for classification before, are found to have powerful discriminability in classifying different vegetation types. This study also demonstrates that the 5 parameters we have chosen are an ideal combination of POLSAR feature parameters and might be applicable for general classification purposes. Résumé. Le but de cet article est de présenter une nouvelle méthode de classification de données provenant d’un radar polarimétrique à synthèse d’ouverture (POLSAR) en intégrant la segmentation d’image et l’arbre de décision. Dans la première étape du procédé, les données POLSAR sont segmentées en petits segments composés de pixels homogènes en utilisant l’algorithme multirésolution incorporé dans le logiciel eCognition de Definiens. Pour chaque segment, une matrice de cohérence moyenne est générée pour représenter les caractéristiques polarimétriques globales. Dans la deuxième étape, cing paramètres minutieusement choisis, y compris les paramètres H, A, α de la décomposition de Cloude-Pottier et deux paramètres associés à l’effet de dépolarisation des cibles radar, forment les attributs de décision pour classer les segments. Une image POLSAR en bande L du Flevoland néerlandais acquise par le système AIRSAR est utilisée comme données d’évaluation. La méthode proposée atteint une précision globale 13 % plus élevée lorsque comparée au classificateur Wishart largement utilisé et une précision comparable ou plus élevée en comparaison avec d’autres algorithmes basés sur des segments. Les deux paramètres liés à la dépolarisation, qui n’ont jamais été utilisés pour la classification, s’avèrent avoir une haute capacité de discrimination pour classer divers types de végétation. Cette étude démontre également que les cing paramètres que nous avons choisis forment une combinaison idéale de paramètres POLSAR qui peut être applicable à des fins générales de classification.

Monitoring and characterizing natural hazards with satellite InSAR imagery

Interferometric synthetic aperture radar (InSAR) provides an all-weather imaging capability for measuring ground-surface deformation and inferring changes in land surface characteristics. InSAR enables scientists to monitor and characterize hazards posed by volcanic, seismic, and hydrogeologic processes, by landslides and wildfires, and by human activities such as mining and fluid extraction or injection. Measuring how a volcanos surface deforms before, during, and after eruptions provides essential information about magma dynamics and a basis for mitigating volcanic hazards. Measuring spatial and temporal patterns of surface deformation in seismically active regions is extraordinarily useful for understanding rupture dynamics and estimating seismic risks. Measuring how landslides develop and activate is a prerequisite to minimizing associated hazards. Mapping surface subsidence or uplift related to extraction or injection of fluids during exploitation of groundwater aquifers or petroleum reservoirs provides fundamental data on aquifer or reservoir properties and improves our ability to mitigate undesired consequences. Monitoring dynamic water-level changes in wetlands improves hydrological modeling predictions and the assessment of future flood impacts. In addition, InSAR imagery can provide near-real-time estimates of fire scar extents and fire severity for wildfire management and control. All-weather satellite radar imagery is critical for studying various natural processes and is playing an increasingly important role in understanding and forecasting natural hazards.

Detecting and assessing the land subsidence in coal mining area using PALSAR data based on D-InSAR technique

Land subsidence induced by mining coal is one major issue in terms of damage to infrastructures. In this paper, the potential of L-band repeat-pass differential SAR interferometry for land subsidence in coal mining area is evaluated using ALOS PALSAR data. The focus is to detect and assess the land subsidence using PALSAR data based on D-InSAR technique. Firstly it introduced the basic principle and some applications of D-InSAR. Then it introduced the information of PALSAR and test data of coal mining area. It described the procedure of data processing of two-pass D-InSAR. And it got the deformation of coal mining area. It carried out interpretation and analysis on the D-InSAR results in details. The maximum settlement of land subsidence in this coal mining area was up to 42.4cm in vertical direction in 46 days. At last, it got some valuable conclusions in monitoring the land subsidence in coal mining area using two-pass D-InSAR technique. The test proves that PALSAR data is suitable for monitoring the land subsidence in areas with vegetated land cover, such as coal mining area.

Recent subsidence in Tianjin, China: observations from multi-looking TerraSAR-X InSAR from 2009 to 2013

Tianjin, China, has been suggested to have serious ground subsidence due to excessive extraction of groundwater. It is essential to monitor this subsidence, which has potential hazards and risks. Time series InSAR (TS-InSAR), such as small baselines subset (SBAS), is a powerful tool that can monitor ground deformation with high accuracy and at high spatial resolution over a long time interval. However, the high computational complexity may exceed computer memory limit when high-spatial resolution SAR (such as TerraSAR-X, TSX) images are used. In this article, the multi-look approach is introduced to the SBAS tool from StaMPS/MTI (Stanford method for persistent scatter/multi-temporal InSAR) in order to balance the spatial resolution and subsidence information in detection. The looks used for multi-looking are first fixed in terms of the accuracy of deformation and the density of coherent points. Then, the recent subsidence in Tianjin is extracted using multi-looking SBAS based on 48 TSX images acquired from 2009 to 2013. The results are validated by levelling measurements with a root mean square error (RMSE) of 4.7 mm year–1, which demonstrates that SBAS analysis can effectively monitor deformation based on multi-looking TSX acquisitions in the area under investigation. Besides, the results also show that Tianjin has been suffering from subsidence during this period, and there were two separate large subsidence basins located in this study area with more than 500 mm cumulative subsidence. Moreover, the subsidence rate increased after December 2010 in Tianjin.

Monitoring Land Subsidence in Suzhou City Using D-InSAR Technique

This paper mainly discusses how to monitor the land subsidence in Suzhou city using differential Interferometric Synthetic Aperture SAR (D-InSAR) technique. It firstly introduces the principle and data processing flowchart of D- InSAR. Then it carries out test and gets the land subsidence in Suzhou city using two pass D-InSAR technique. At last, it analyzes some problems which influence the application of D- InSAR in monitoring the urban land subsidence in detail. Keywords-D-InSAR;land subsidenc;2-pass;ERS

A New Numerical Method for Calculating Extrema of Received Power for Polarimetric SAR

A numerical method called cross-step iteration is proposed to calculate the maximal/minimal received power for polarized imagery based on a targets Kennaugh matrix. This method is much more efficient than the systematic method, which searches for the extrema of received power by varying the polarization ellipse angles of receiving and transmitting polarizations. It is also more advantageous than the Schuler method, which has been adopted by the PolSARPro package, because the cross-step iteration method requires less computation time and can derive both the maximal and minimal received powers, whereas the Schuler method is designed to work out only the maximal received power. The analytical model of received-power optimization indicates that the first eigenvalue of the Kennaugh matrix is the supremum of the maximal received power. The difference between these two parameters reflects the depolarization effect of the targets backscattering, which might be useful for target discrimination.

Measuring Co-seismic Deformation of the Sichuan Earthquake by Satellite Differential INSAR

The Sichuan Earthquake, occurred on May 12, 2008, is the strongest earthquake to hit China since the 1976 Tangshan earthquake. The earthquake had a magnitude of M 8.0, and caused surface deformation greater than 3 meters. This paper presents the research work of measuring the co-seismic deformations of the earthquake with satellite differential interferometric SAR technique. Four L-band SAR images were used to form the interferogram with 2 pre- scenes imaged on Feb 17, 2008 and 2 post- scenes on May 19, 2008. The Digital Elevation Models extracted from 1:50,000-scale national geo-spatial database were used to remove the topographic contribution and form a differential interferogram. The interferogram presents very high coherence in most areas, although the pre- and post- images were acquired with time interval of 92 days. This indicates that the L-band PALSAR sensor is very powerful for interferometry applications. The baseline error is regarded as the main phase error source in the differential interferogram. Due to the difficulties of doing field works immediately after the earthquake, only one deformation measurement recorded by a permanent GPS station is obtained for this research. An approximation method is proposed to eliminate the orbital phase error with one control point. The derived deformation map shows similar spatial pattern and deformation magnitude compared with deformation field generated by seismic inversion method.