Inferring a globally consistent normal orientation for point clouds remains challenging, especially for noisy and non-watertight point clouds. To improve accuracy and robustness in orientation inference, a Boundary-Aware Consistent Normal Orientation (BACNO) method is proposed. Its main idea is to transform the normal orientation problem into a boundary-aware narrow band grid partitioning problem. This processing process is as follows: First, an unsigned distance field for an input point cloud is computed, which is defined on a regular grid. The field is then trimmed as a boundary-aware narrow band grid around the point cloud. Next, the narrow band grid is segmented into two parts, with each part located on one side of the input point cloud. Finally, a coarse-to-fine normal orientation strategy is presented to achieve the globally consistent orientation. Extensive experimental results demonstrate that the proposed method outperforms state-of-the-art methods, particularly for noisy and non-watertight point clouds.

ABSTRACT Sampled point clouds, particularly with prelabeled annotations and ground truth metrics, are frequently used in computer graphics and machine learning. In this work, we focus on a fuzzy sampling approach for such point clouds with qualified uniformity properties. After abstracting the uniformity requirements, a novel approach to sampling point clouds from implicitly defined curves/surfaces is proposed. The approach deliberately combines techniques including isodeviation dispatch, curvature compensation, and normalized distance blue noise. The experimental results show various sampled point clouds with uniform visual effects and statistical metrics. Moreover, the comparisons in terms of distance, density, and thickness uniformity with state‐of‐the‐art methods exhibit the approach's advantages. Due to its low cost, ground truth, and annotation easiness features, the method will be smoothly applied in deep learning and computer animation.

Photon-counting laser altimeter is an advanced remote sensing observation equipment, which provides detailed surface profile information, exemplified by the advanced topographic laser altimeter system (ATLAS) on Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). However, the high sensitivity of a photon-counting laser altimeter introduces noisy geolocated photons, posing a tremendous challenge in signal extraction from noise photons with low signal-to-noise ratios (SNRs). An efficient signal extraction algorithm is critical for further applications of ICESat-2 data, and the spatial density-based algorithms perform well and have been verified in various scenarios, e.g., canopy and ground detection, sea-ice freeboard detection, and bathymetry. Currently, the geometric parameters in density-based algorithms are usually empirically determined, and the main challenge is to adaptively set the optimal geometric parameters in variable scenarios. In this study, a theoretical mapping model that correlates the performance metrics (e.g., number of true positive, false positive, and false negative photons) with the algorithm parameters and the lidar system parameters is derived. The performance of this model is verified using Monte Carlo simulated data of bare lands and vegetated areas with$R^{2}$exceeding 0.99, and also verified using ICESat-2 data over land, ocean, vegetation, and ice areas with$R^{2}$exceeding 0.94. Based on the model, the signal extraction capacity in different SNRs, channel numbers, and signal durations are discussed, offering a theoretical foundation for determining the optimal parameters to extract ICESat-2 signal photons and also for better designing hardware parameters of photon-counting laser altimeters.

Current multi-polarization Time-Series Interferometric Synthetic Aperture Radar (TS-InSAR) approaches conduct polarimetric optimization during the coherent scatterer selection and interferogram generation stages. However, these techniques often encounter limitations when dealing with certain types of targets, particularly when the target scattering phase center positions differ across polarizations. Consequently, the temporal coherence of the optimized interferometric phase can sometimes be worse than that of the classical single-polarization technique. This work aims to enhance these techniques by fine-tuning the TS-InSAR workflow and selecting the most suitable observation from various polarization channels based on temporal coherence. Experimental results show that the proposed method increases the number of persistent scatterers and offers more reliable deformation monitoring than both single-polarization and existing multi-polarized methods.

In this paper, the Huangtupo landslide in the Three Gorges Reservoir (TGR) was explored by the multi-source satellite SAR data from ALOS-2 and Sentinel-1A/B based on time-series InSAR analysis. One track of ALOS-2 and two tracks of Sentinel-1 SAR datasets acquired from August 2016 to October 2017 were used to investigate the surface deformations. The maximum line-of-sight (LOS) deformation rate measured over the slope surface was up to -50 mm/year from the ALOS-2 measurement, while it was as high as -40 and -30 mm/year for Sentinel-1 Track 11 and Track 84 measurements, respectively. Cross-validation between the multi-source InSAR results showed that the ALOS-2 and Sentinel-1A/B measurements are highly correlated. Time series displacement analysis of selected point clusters based on InSAR measurements from ALOS-2 and Sentinel-1 data stacks was also investigated along with the TGR water level and daily rainfall over this area. Seasonal fluctuations caused by changes in rainfall and TGR water level can be clearly observed from the time series evolutions of deformation. It is clearly found that the lower parts of the Huangtupo landslide are more likely to influenced by the TGR water level, while the upper parts mainly responses to the seasonal changes of rainfall.

Abstract B‐spline curve interpolation is a fundamental algorithm in computer‐aided geometric design. Determining suitable parameters based on data points distribution has always been an important issue for high‐quality interpolation curves generation. Various parameterization methods have been proposed. However, there is no universally satisfactory method that is applicable to data points with diverse distributions. In this work, a hybrid parametrization method is proposed to overcome the problem. For a given set of data points, a classifier via supervised learning identifies an optimal local parameterization method based on the local geometric distribution of four adjacent data points, and the optimal local parameters are computed using the selected optimal local parameterization method for the four adjacent data points. Then a merging method is employed to calculate global parameters which align closely with the local parameters. Experiments demonstrate that the proposed hybrid parameterization method well adapts the different distributions of data points statistically. The proposed method has a flexible and scalable framework, which can includes current and potential new parameterization methods as its components.

Abstract Restoring the appearance of the model is a crucial step for achieving realistic 3D reconstruction. High‐fidelity textures can also conceal some geometric defects. Since the estimated camera parameters and reconstructed geometry usually contain errors, subsequent texture mapping often suffers from undesirable visual artifacts such as blurring, ghosting, and visual seams. In particular, significant misalignment between the reconstructed model and the registered images will lead to texturing the mesh with inconsistent image regions. However, eliminating various artifacts to generate high‐quality textures remains a challenge. In this paper, we address this issue by designing a texture optimization method to generate seamless and aligned textures for 3D reconstruction. The main idea is to detect misalignment regions between images and geometry and exclude them from texture mapping. To handle the texture holes caused by these excluded regions, a cross‐patch texture hole‐filling method is proposed, which can also synthesize plausible textures for invisible faces. Moreover, for better stitching of the textures from different views, an improved camera pose optimization is present by introducing color adjustment and boundary point sampling. Experimental results show that the proposed method can eliminate the artifacts caused by inaccurate input data robustly and produce high‐quality texture results compared with state‐of‐the‐art methods.

Pre-disaster information storage is crucial for effective disaster response. The discussion regarding deep learning-based Light Detection and Ranging (Lidar) semantic segmentation technology for indoor small items has been ongoing in recent years. However, the methods applicable to large-scale outdoor Lidar datasets for pre-disaster information storage remain limited. This study aims to propose a novel deep learning-based network for city-scale Lidar semantic segmentation to support pre-disaster information storage, called channel attention and normal-based local feature aggregation network (CNLNet). This network is designed to segment common urban land cover objects, including buildings and vegetation. This network incorporates surface normal information and the channel attention mechanism into the RandLA-Net backbone. Ablation studies have been devised to assess the performance of these two features. During the pre-processing step, color information from optical images is fused with Lidar data. The findings demonstrate that CNLNet can enhance the accuracy of the RandLA-Net backbone by improving mIoU at least 1-2%. Including one of these two features also contributes to the backbone’s improved accuracy. Notably, CNLNet outperforms other well-known networks in terms of accuracy with the test of the public Sementic3D dataset. The study further reveals that the proposed network excels in building segmentation, a crucial facet of pre-disaster information storage. Moreover, the results show that spatial resolution, whether at 0.5m or 10m per pixel for optical images, has limited influence on outcomes. One theoretical contribution of this study is the demonstration of the advantages of integrating either surface normal information or a channel attention mechanism to enhance large-scale outdoor Lidar semantic segmentation. Labeled Lidar datasets have been created for training. The practical contribution is that it can optimize disaster response by efficiently facilitating pre-disaster information storage.

Graph convolutional network (GCN) has attracted increasing attention in hyperspectral image (HSI) classification due to its capability to capture the long-range correlations between adjacent land covers. Most GCN-based HSI classification methods have been proposed to address the four limitations (shape-fixed kernel, massive calculations and parameters, limited classification ability with limited labeled samples, and difficulty to capture the long-term relationships of land covers) of convolutional neural networks (CNNs) by operating on superpixel-based nodes. However, the pixels in each superpixel share the spectral-spatial features, overlooking the unique characteristics of individual pixels. To address these limitations of GCN and CNN and fully exploit their advantages, we propose a novel multibranch attention framework (MFAF), in which the specially designed GCN and CNN branches learn the complementary spectral-spatial features. Specifically, we develop a multiscale attentional GCN to enhance the ability to understand the long-range correlations between land covers, accomplished by constructing the multiscale attentional adjacency matrix. Then, based on the two designs of the dual-branch depthwise separable convolution (DSC) and the attention-based residual block, we present a new complementary dual convolutional attention network that extracts more discriminative spectral-spatial features of pixels. Finally, we introduce an attention-based fusion pooling (AFP) mechanism to combine the features generated by different network branches. Extensive experimental evaluations on four public HSI datasets demonstrate that the proposed MFAF achieves better performance than several state-of-the-art methods, delivering superior and consistent results in terms of overall accuracy (OA), average accuracy (AA), and kappa coefficient (KAPPA).

Structure-from-Motion (SfM) aims to recover 3D scene structures and camera poses based on the correspondences between input images, and thus the ambiguity caused by duplicate structures (i.e., different structures with strong visual resemblance) always results in incorrect camera poses and 3D structures. To deal with the ambiguity, most existing studies resort to additional constraint information or implicit inference by analyzing two-view geometries or feature points. In this paper, we propose to exploit high-level information in the scene, i.e., the spatial contextual information of local regions, to guide the reconstruction. Specifically, a novel structure is proposed, namely, track-community, in which each community consists of a group of tracks and represents a local segment in the scene. A community detection algorithm is performed on the track-graph to partition the scene into segments. Then, the potential ambiguous segments are detected by analyzing the neighborhood of tracks and corrected by checking the pose consistency. Finally, we perform partial reconstruction on each segment and align them with a novel bidirectional consistency cost function which considers both 3D-3D correspondences and pairwise relative camera poses. Experimental results demonstrate that our approach can robustly alleviate reconstruction failure resulting from visually indistinguishable structures and accurately merge the partial reconstructions.

Wildfires have attracted considerable attention because of their increasing frequency and severity around the globe. Satellite remote sensing data is a valuable asset for monitoring, and mapping burned areas (BA). However, most global BA products based on optical imagery are limited by cloud coverage and not usable for cloud-prone regions. All-weather Synthetic Aperture Radar (SAR) imagery can be a complement to an optical-based counterpart. In order to exploit the value of phase information of SAR data, this paper aims to propose a framework by developing a visual saliency detection algorithm for BA mapping using Sentinel-1 Interferometric SAR (InSAR) coherence difference image. The results show that the proposed method can effectively improve the coherence difference's accuracy performance. Additionally, we also demonstrate that for C-band Sentinel-1 SAR data, both VV and VH polarized images can be used in BA mapping, but the former would provide slightly better results.

The ancient Aniangzhai (ANZ) landslide in Danba County, Sichuan Province of southwest China was reactivated after a series of complex hazard events that occurred in June 2020. Since then, emergency engineering work was carried out to prevent further failure of the reactivated landslide. This study investigates the multi-temporal optical images (3 m spatial resolution) acquired from the PlanetScope satellite with pixel offset tracking (POT) technique to assess deformation characteristic and spatial-temporal evolution of the reactivated ANZ landslide during the post-failure stage. The relationships between sun illumination differences, temporal baseline of correlation pairs and the uncertainties were explored. The large horizontal displacements over the reactivate ANZ slope were detected from the time-series POT results, showing a significant increase of about 24 m between 24 June 2020 and 11 June 2021. The time series optical POT results revealed that the reactivated ANZ landslide body is gradually slowing down to a steady deformation status since its occurrence in August 2020, indicating the effectiveness of engineering work on the prevention of further landslide.

Most deep learning networks for Lidar semantic segmentation have been devoted to small-scale indoor data and only few of them have focused on large-scale outdoor data. To bridge this gap, this research explores the influences of changing features of deep learning networks on the accuracy of large-scale outdoor Lidar semantic segmentation. Surface normal information and random downsampling layers are the two features considered. Eight scenarios are designed to test them. Point clouds acquired from Kapiti Coast, New Zealand in 2021 with five labeled classes are used for training, validation, and testing stages. Mean intersection over union (mIOU) is the main metric in the validation and test. The findings show that the network adding surface normals with four random downsampling layers whose sampling ratios are 4, 4, 4, and 4 of those layers performs best because of its high mIOU. Moreover, IOU results reflect that the segmentation of buildings performs best between all tested classes.

In 2022, flooding severely violated Australia, resulting in the displacement of residents and damage to property and public facilities. With the rapid development of information technology, it is possible to use Synthetic Aperture Radar (SAR) satellite remote sensing technology and the Quadrotor Unmanned Aerial Vehicle (QUAV) to detect and assess flooding environments. SAR can penetrate the cloud to operate at all times and in all weather, which is ideal for flooding area mapping. However, most SAR-based products are constrained by the flood’s dynamically shifting boundary and spatial and temporal resolution. QUAV is portable and capable of precise positioning despite being ineffective in covering large areas, such as flood-affected areas. Thus, it can complement the SAR counterpart for flood mapping in boundary extraction. This paper aims to propose a framework that mainly fuses satellite SAR and QUAV technology by aggregating the multiple-scale data for double validation and detailing, with enhancement by deep learning-based prediction models and a closed-loop feedback mechanism, to form a novel space-air vertical remote sensing framework. Finally, the selected flood-affected areas in Moama, NSW, Australia, were conducted as a case study. The results show that the proposed method can effectively enhance flood area assessment and mapping.

Early detection of bushfire plays a crucial role in firefighting, fire modelling, and minimising losses of human lives and properties. However, current bushfire monitoring systems have an intrinsic shortcoming because only temperature difference between neighboring pixels is exploited. This paper proposes to also examine a range of other changes occur when a bushfire is ignited, for example, a reduction of vegetation cover, volume scattering of bush and trees, as well as height of vegetation. All of these can be readily measured by optical and radar satellites already in orbits in near real-time, that is, less than two hours after a satellite overpass. Cross-correlation of these measurements has the potential to significantly reduce false alarm of a bushfire, while improving the early detection and measurement of fire spots, and hence make the system much more robust. A case study near Sydney is included here based on Sentinel-1 SAR and Sentinel-2 optical satellite data collected on 10 and 11 October 2020, respectively. This research is a major step forward towards the operational and synergetic use of optical and SAR satellites in bushfire monitoring.

In this paper, the 2020 Aniangzhai Landslide in Danba County in Sichuan province, China was investigated by using multi-temporal SAR and optical datasets. The pre- and post- failure scars of the landslide and debris flow were depicted using high-resolution optical images from the Planetscope satellites. The descending Sentinel-1A/B C-band SAR images were applied to explore the deformation characterization of this event. Advanced time-series InSAR analysis was processed to detect the sliding motion, with identifying the spatial-temporal pattern and evolution of the failure area of the Aniangzhai landslide. The maximum line-of-sight (LOS) deformation rate measured over the slope surface was up to -80 mm/year. Time series analysis of selected measurement points indicates that the cumulative deformation peaked at -113 mm. Most importantly, two significant accelerations were detected at the upper area of the Aningzhai slope before the occurrence of failure. By comparing the time series analysis of points in different sections, it is evidenced that the initial failure of lower part triggered the sliding motion of upper part at the slope.

Building damage assessment after natural disasters is an important task for disaster managers and practitioners. In order to provide detailed levels of post-event building damage, this paper applies deep learning models for building localization and damage classification using transfer learning with an online free xBD dataset. The model is pretrained with ImageNet dataset. SE-ResNeXt-50-32x4d is applied for building localization, and HRNet is applied for damage classification. The building damage is divided into four levels, including no damage, minor damage, major damage, and total damage. The results show that the method can be applied for classifying building damage in an acceptable manner. This can help the government and rescue teams make disaster response quickly and support disaster management.

Abstract This paper proposes a passive multi‐view system for human body shape reconstruction, namely RHF‐Human, to overcome several challenges including accurate calibration and stereo matching in self‐occluded and low‐texture skin regions. The reconstruction process includes four steps: capture, multi‐view camera calibration, dense reconstruction, and meshing. The capture system, which consists of 90 digital single‐lens reflex cameras, is single‐shot to avoid nonrigid deformation of the human body. Two technical contributions are made: (1) a two‐step robust multi‐view calibration approach that improves calibration accuracy and saves calibration time for each new human body acquired and (2) an accurate PatchMatch multi‐view stereo method for dense reconstruction to perform correct matching in self‐occluded and low‐texture skin regions and to reduce the noise caused by body hair. Experiments on models of various genders, poses, and skin with different amounts of body hair show the robustness of the proposed system. A high‐fidelity human body shape dataset with 227 models is constructed, and the average accuracy is within 1.5 mm. The system provides a new scheme for the accurate reconstruction of nonrigid human models based on passive vision and has good potential in fashion design and health care.

Compared with traditional measurement technologies, synthetic aperture radar interferometry (InSAR) has unique advantages in monitoring ground subsidence due to underground mining. However, when the subsidence gradient of the subsidence trough exceeds the maximum measurable gradient of InSAR technology, the interference fringes will be too dense, causing phase aliasing. As a result, it is impossible to obtain correct phase-unwrapping result. The main objectives of this letter are two folded. First is to develop an unwrapping strategy to deal with the unwrapping problem caused by large subsidence gradient at the mine subsidence trough. The main idea of this strategy is to estimate most of the subsidence phase by multiple model inversions based on iterative approach. Then, the model phases from multiple models are combined with the final unwrapped residual phase. Another objective of this letter is to evaluate the feasibility of the three common deformation models, i.e., Mogi, probability integral method (PIM), and Okada, in solving the phase-unwrapping problem. Their advantages and disadvantages are outlined. Both the simulated data and real data are used for this experiment. The result shows that the problem of large subsidence gradient in the differential interferometric synthetic aperture radar (DInSAR) results can be solved by multiple model inversion. Among the three models, the use of Okada model seems to provide slightly more accurate result for solving the large-scale subsidence in the mining area than the other two models with the proposed strategy.

Optical remote sensing satellite usually adopts the method of photographing calibration field to achieve calibration of internal and external orientation elements of optical camera. However, as time goes on, or as the latitude changes, the accuracy of satellite positioning after calibration will decline. The traditional calibration method requires satellite passes calibration field and is difficult to achieve globally, so it's hard to be realized that keeping the global positioning accuracy high. The purpose of this study is to explore a more feasibility calibration method of interior orientation elements in optical satellites. That is, by photographing stars, the stars are used as control points to calibrate the camera's internal orientation elements. In this study, a camera calibration method based on multi-frame star map was proposed. The star maps were captured by the star camera of Ziyuan-3 02 (ZY-3 02). Final result proved to be efficient to offer sufficient information for interior orientation elements calibration purpose.

In this paper, the 2017 Maoxian landslide in Sichuan province, China was investigated by using multi-temporal SAR and optical datasets. The pre- and post-failure scars of the landslide were depicted by using the K-means classification of the Normalized Difference Vegetation Index (NDVI) maps. Two stacks of ascending and descending Sentinel-1A/B C-band SAR images were applied to explore the pre-failure characteristics of this event. Advanced time-series InSAR analysis was processed to detect the pre-failure movements of this event, with identifying the spatial-temporal pattern and evolution of the source area of the Maoxian landslide. The maximum line-of-sight (LOS) deformation rate measured over the slope surface was up to -30 mm/year in the descending track, with only -18 mm/year for the ascending track. Most importantly, an obvious acceleration was detected from the time series analysis of selected measurement points at the source area before the occurrence of failure. By comparing the TS-InSAR result with the precipitation record over this region, it is evidenced that heavy rainfall might be the major triggering factor of the Maoxian Landslide.

In this study, the long term land subsidence (~ 15 years) in the Mexico City, Mexico mapped using two in-house InSAR methods GEOS (Geoscience and Earth Observing Systems Group)-ATSA (Advance Time-Series Analysis) and GEOS-SBAS (Small Baseline Subset) has been presented. An IDW (Inverse Distance Weighted)-based integration module and MLR (maximum likelihood regression)-based M-estimator are introduced to further enhance these two methods. The land subsidence was continuously mapped using Envisat (2004 - 2007), ALOS-1 (2007 - 2011), CSK (2011 - 2014), ALOS-2 (2014 - 2018), and Sentinel-1 (2015 - 2017) datasets. A comparison between InSAR time series and GPS measurement shows that the subsidence rates were consistent over 2004 - 2018, and five evidences were given to support this argument. The 15-year accumulated subsidence map was generated and the maximum subsidence over 4.5 m was found. By comparing the InSAR result with land use map, it has been found that there are some relations between the local subsidence rate and land use type while residential usage and consumption of the groundwater has quite significant contribution to the local subsidence rate.

In this paper, the 2015 Nepal Earthquake sequence, both the main shock (MW7.8) on 25 April 2015 and the major aftershock (MW7.2) on 12 May 2015 are investigated by using co-seismic DInSAR and GPS measurements. Source model and slip distribution of both events are determined using geodetic inversion based on an elastic dislocation model. The optimised source model for the main shock shows a thrust fault striking 285.9° NW-SE and dipping 7.7° NE with a slight right-lateral component. The maximum slip of this event is up to 5.1m. The peak slip of distributed slip model for the major aftershock was found at the similar depth of main shock. The triggering relationship between main shock and major aftershocks is demonstrated based on the calculation of Coulomb stress change.

This paper addresses the forest height inversion based on the modified RMoG model using repeat-pass Pol-InSAR data. The linear variance of the Gaussian motion distribution in the RMoG model is replaced by a linear standard deviation to describe the volumetric motion heterogeneity and related coherence function is deduced based on that. Furthermore, a forest parameter inversion algorithm is proposed based on this modified model and its performance is investigated with L-band repeat-pass ALOS-1 quad-polarization data acquired over German forest site with temporal baselines of 46 days. Inversion results indicates that in comparison with the traditional RVoG and RMoG methods, the modified method can reduce 27.73% and 8.57% of the overestimation errors caused by the temporal decorrelation.

Continuous research has been conducted in Bandung City, West Java province, Indonesia over the past two decades. Previous studies carried out in a regional-scale might be useful for estimating the correlation between land subsidence and groundwater extraction, but inadequate for local safety management as subsidence may vary over different areas with detailed characters. This study is focused primarily on subsidence phenomenon in local, and patchy scales, respectively, with Sentinel-1 and ALOS-2 dataset acquired from September 2014 to July 2017. In order to understand the subsidence in a more systematic way, six 10-cm subsidence zones have been selected known as Zone A to F. Further analyses conducted over multiple scales show that industrial usage of groundwater is not always the dominant factor that causes the land subsidence and indeed it does not always create large land subsidence either. Regions experiencing subsidence is due to a combined impact of a number of factors, e.g., residential, industrial or agricultural activities.

In this paper, ground deformation and source model of the 12 November 2017 Kermanshah Earthquake (border region between Iran and Iraq) are investigated by using ALOS-2 ScanSAR and Sentinel-lAiB TOPSAR co-seismic Differential Interferometric Synthetic Aperture Radar (DInSAR) measurements. Geodetic inversion has been performed to constrain source parameters and invert slip distribution on the fault plane. Stress changes on the source fault and neighboring active faults around this area are estimated based on co-seismic deformation. The best-fit source model shows a reverse fault with a relative large right-lateral component, striking 353.5° NNW-SSE and dipping 16.3° NE. The maximum slip is up to 3.8m at 12-14 km depth and the inferred seismic moment is 1.01×1020Nm, corresponding to Mw 7.3. The positive stress changes on the neighboring active faults indicate that this event may trigger other earthquakes on the Zagros Mountain Front Fault (MFF).

SqueeSAR, also known as advanced time-series interferometric synthetic aperture radar (ATS-InSAR) method, is a significant improvement of conventional persistent scatterer InSAR (PSInSAR), whereby the concepts of distributed scatterer (DS) and persistent scatterer (PS) are first been introduced, respectively. It is worth noting that during the measurement pixel selection, it is inevitable that a number of PS can be categorized as DS as well, hence resulting in common PS-DS pixels. In order to understand the consistency among these common PS-DS pixels with PSInSAR and ATS-InSAR methods, statistical analyses are conducted with 10 real InSAR image stacks in this letter. The relationship between the goodness-of-fit value and four main factors, including root-mean-square difference, DS percentage, PS-DS/PS ratio, and PS-DS/DS ratio, is studied. It is concluded that Sentinel-1-based TS-InSAR can be less influenced by the goodness-of-fit threshold in comparison with the counterpart result of ALOS-1 under the same parameter setting; finally, conclusions for the threshold settings are given.

Advanced Time series InSAR (ATS-InSAR) is generally refer to those TS-InSAR methods with an external Distributed Scatterer (DS) selection module, e.g. SqueeSAR and GEOS-ATSA. It is being known as a very efficient tool for monitoring the ground deformation over suburban or even non-urban regions with great success. However, within Appin Colliery, which is located in the southeastern corner of the Southern Coalfield, New South Wales (NSW), Australia. C-band ASAR based ATS-InSAR failed to produce reasonable outcome due to the underground mining effect. This paper presents a modified ATS-InSAR method for mapping the ground deformation over underground mining region. Firstly, traditional reliable DS pixels and Persistent Scatterer (PS) pixels are selected to form the initial triangular irregular network (TIN) reference network. Then the ground deformation and DEM error with respect to these Measurement Scatterer (MS) pixels are solved through a robust regression estimator. Due to the losses of coherence, the general underground mining pattern cannot be formed when using C-band image stacks. Therefore, in order to achieve the best detail, modified MS pixel selection method is conducted by including less reliable MS pixels based on a weighted least square method. Moreover, final result proved to be efficient to offer sufficient information to associated councils and department for risk management purpose.

Beijing metropolitan, the capital city of China, has suffered from the groundwater-induced subsidence since the late 1950s. The previous researches with respect to the ground deformation in Beijing City mainly focused on the time period before the year of 2014. To study the recent evolution of ground deformation, twenty-four C-band Sentinel-1A/B images (June 2015-November 2016) along with nineteen L-band ALOS-1 PALSAR images (June 2007-January 2011) are analysed in this research. As the optical-based classification result indicates that approximate 42% of the processed area is covered by rural land-use type, of which the main uses are farmland and grasslands, therefore, to achieve the best detail over both urban/non-urban regions, a Distributed Scatterer based TS-InSAR is implemented to provide the timely information for ground deformation assessment. It is worth noting that even though both descending and ascending time series InSAR (TS-InSAR) deformation products are available, the vertical deformation is not estimated due to that the temporal period between these two products are not the same. Moreover, the general subsidence patterns from -20 mm·yr-1to -120 mm·yr-1section over these two datasets are correlated to some extent, which suggests that the subsiding trend is still continuing. However, there is a vast difference between - 120 mm·yr-1to -150 mm·yr-1section, which reveals the fact that the maximum subsidence rates over some regions are decelerating.

This paper reports the findings based on ALOS-1 and GRACE satellite data for the purpose of monitoring land surface subsidence due to groundwater extraction in the Ordos Basin, China. Twenty ALOS-1 PALSAR data acquired between 8 January 2007 and 19 January 2011 are utilized in the time-series InSAR interferometry (TS-InSAR) analysis while the total water storage observations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite data are integrated with hydrological modeling results (for soil moisture modelling) to estimate the groundwater depletion rate. The outcome shows that the total mean subsidence measured from TS-InSAR is about -6.8 mm yr-1in vertical direction while the groundwater depletion rate is about -4.2 mm yr-1between 17 December 2006 and 15 December 2010. Since in general every 1 m drop in groundwater level could lead to land subsidence of about 5 to 50 mm, and the total subsidence in Ordos Basin is mainly due to groundwater extraction and underground mining activities. The experiment result shows that the total land subsidence is mainly induced by underground mining.

This paper reports the findings on monitoring land subsidence in the south of Sydney, Australia, using data from ALOS-1 and ENVISAT satellites. Twenty-three L-band ALOS-1 PALSAR scenes acquired between 29 June 2007 and 07 January 2011 and twenty-six C-band ENVISAT ASAR image acquired between 09 July 2007 and 06 September 2010 are analysed in this research. Since the city of Wollongong and the town of Appin underground mining site are two most interested regions in the south of Sydney for this study, a new strategy is proposed to select measurement points according to different geophysical information in order to achieve the best detailed deformation mapping. In this paper, both descending and ascending time series InSAR (TS-InSAR) deformation products are utilised to estimate the vertical deformation in Wollongong city area. The highest vertical deformation less than -0.8 cm yr-1is detected. It is worth noting that TS-InSAR analysis to C-band satellite dataset cannot generate reasonable result in Appin underground mining site due to its limited dynamic range of detectable subsidence.

This paper reports the use of UAV for mining-related applications at the Ulan open-cut mine and Tahmoor underground mine, in New South Wales (NSW), Australia. The preliminary results showed that UAV is capable for estimating the stockpile volume, monitoring the highwall slope stability, and mapping the underground mine subsidence.

This paper reports the characteristics of land deformation in Taipei City derived from the ALOS PALSAR data, acquired between January 2007 and March 2011. InSAR time-series analysis has been performed to map the land surface movement. Several local deformation zones have been identified in Taipei City. Deformation measured from the CORS GPS networks were used for validation. The InSAR-derived preliminary land deformation results have been compared with the GPS-derived deformation at 21 GPS stations. The standard deviation and absolute mean of the difference in displacement between InSAR and GPS measurements was 3 mm/year and 2 mm/year, respectively.

Land displacement in Ordos, China, between 8 January 2007 and 19 January 2011 was mapped using L-band ALOS PALSAR data. Twenty ALOS PALSAR scenes acquired were utilised to generate both PSI and DInSAR results. Several locations in the eastern Ordos experiencing rapid land subsidence were identified including Huo Luo Wan coalmine and Qu Jia Liang coalmine. The subsidence rates ranging from -35mm/year to 35 mm/year were detected. The comparison between PSI and DInSAR outcomes, although showing good agreement in general, reveals some gaps in PSI map near Qu Jia Liang coalmine mainly due to rapid changes within the four-year period. Six successive DInSAR results were exploited to generate time series deformation map, with selected points being analysed and the reason being given for the formation of gaps as well. The DInSAR deformation measurements were then converted into time series velocity maps and integrated with PSI outcome to generate a final product.

In this study, a model is developed to quantify the land use contribution to nighttime light using coarse-resolution nighttime light imagery and fine-resolution land use data. We assumed that the nighttime light of a region can be represented by a linear combination of land use areas, with its nighttime light intensity (NLI) as coefficient. Based on an unmixing strategy, the NLI of each land use type was estimated. The Berlin City and MA State were used as study areas. For the Berlin City, we made use of nighttime light imagery from the Suomi National Polar-orbiting Partnership and the land use maps with 52 classes as data sets for analysis, and we used a nighttime aerial photograph to derive reference data. For the MA State, we made use of nighttime light imagery from the Defense Meteorological Satellite Program's Operational Linescan System and the land use maps with 33 classes as data sets for analysis, and we used a nighttime photograph from the International Space Station to derive reference data. The reference NLI data were correlated with the estimated NLI data, and the R2values of Berlin and Massachusetts were 0.7277 and 0.7982, respectively, proving that the proposed model is effective.

The digital elevation model (DEM) is one of the most important sources used for Earth surface analysis because of its various applications and its usability in subsequent studies. A DEM can be generated with interferometric synthetic aperture radar (InSAR) and radargrammetry techniques from synthetic aperture radar (SAR) images with orbital separation between them as the baseline. The on-board radar imaging system records both phase and intensity information of the backscattered signals. Radargrammetry is based on the disparity between two intensity images which is less affected by temporal and atmospheric decorrelation while InSAR uses the phase differences between two images. However, SAR intensity images may have low spatial resolution compared to optical images that are used in the photogrammetric DEM generation and the original SAR image is degraded by speckle noise. This leads to low accuracy of radargrammetric DEMs. It is necessary to develop methods focusing on original SAR images to improve the accuracy of radargrammetric DEM generation. In this letter, we propose that the single look complex (SLC) SAR image reprocessing method can be utilized to improve the image quality of radargrammetric DEMs. Simulated noise-free SAR images are used to generate radargrammetric DEMs for the purpose of comparison. Three SLC SAR images are selected for image quality improvement in each track. Other images in the same track are matched and processed to one reference image. The SAR simulation images are generated based on the reference image in each track. Therefore, the products in the same track have the same meta-information. Each DEM product is compared with conventional radargrammetric DEMs. This study shows that DEMs generated from re-processed SAR images are more accurate than radargrammetric DEMs from single-pair SAR images. Also, the accuracy of radargrammetric DEM produced with the proposed method is close to DEMs generated with noise-free simulated SAR images.

It is important to monitor long-term coastal erosion in countries such as Australia given the majority of our population live in coastal regions. However, ground-based surveys are labour-intensive and involve significant cost when large spatial areas need to monitored. This paper presents a complementary, cost-effective approach for mapping the eroded shoreline, through integrating Landsat multispectral (MS) imagery data and synthetic aperture radar (SAR) imagery data. This integration method overcomes the problem of data shortage associated with using a single data source. Moreover, it can extract the instant land-water interface at sub-pixel resolution. Because the extracted land-water boundaries are dynamic, a tidal model has to be applied to define the high water line. Wavelet transform and linear spectral unmixing are the two major algorithms used for extracting the land-water interface. Several inundated areas are identified at the selected study area along the coast of East Gippsland Basin, Victoria, Australia. Naturally occurring erosion is believed to be the major factor for these inundated areas. Theoretically the extracted shorelines for defining erosion can reach 1m - 2m resolution approximately.

In this study, characteristics of land subsidence in Bandung Basin, Indonesia was estimated using the C-band ENVISAT ASAR and the L-band ALOS PALSAR data, acquired between 2002 to 2008 and 2007 to 2011, respectively. The software, GEOS-APSI (Advanced Persistent Scatterer Interferometry), was used to map the long term land displacement. GEOS-APSI is an in-house developed software by GEOS at UNSW for Interferometric SAR Persistent Scatterer Interferometry. Several subsidence zones were identified in the basin including areas in Cimahi, Dayeuh Kolot, Rancaekek, and Solokan Jeruk. Subsidence with a maximum of 250 mm/yr was observed in some of the zones. The results were validated with six epochs of GPS survey between 2002 and 2010. The standard deviation of difference in subsidence between InSAR and GPS measurements for ENVISAT ASAR and ALOS PALSAR was 13 mm/yr and 22 mm/yr, respectively.

A natural CO2 leakage site is studied in order to understand the impact of top soil CO2 contamination on vegetation cover. The site is relatively small and with a very high elevation. The authors are motivated to develop a new algorithm for locating land surface change with vegetation index NDVI. One of the challenges in this study is that it is impossible to develop a vegetation growth model because of large data gaps. There were only 27 Landsat 5 TM images available for time series analysis. Moreover, the selected AOI covered just 4 pixels of MODIS image in computation for NDVI difference. However, the results are very promising in locating these NDVI changed pixels. The clear trend of NDVI change of these pixels on the vegetation cover that suffered CO2 damage has proven this proposed algorithm's capability in monitoring potential CO2 leakage at CCS site even with a small scale.

In this paper, the authors have analysed the signature of extracting water body from SAR intensity and coherence images with morphological segmentation, and presented a solution to water body mapping in the absence of ancillary information from air or ground. The use of morphological image reconstruction algorithms is a great improvement for mapping water with SAR imagery. Especially, the innovative decision fusion method on integrating intermediate extracted data from intensity and coherence images is the first of its kind in water body mapping. The image reconstruction from fusion marker and mask has shown its promise in removing non-water related features/patterns. A case study is given to demonstrate the efficiency and accuracy of this proposed methodology in removing vegetation cover and man-made features. Thus, the advantage of SAR imagery available unlimited by weather condition can be fully exploited. Furthermore, due to the reduced computing time, this approach for water body mapping can assist authorities to respond promptly to emergencies such as major floods.

Roads are traffic lifelines for emergency rescue and disaster relief. After major earthquakes, it is very significant to extract road damage rapidly and accurately in disaster areas by remote sensing for emergency rescue. Because road damage caused by earthquake is ever-changing,there is no common spectral characteristic of it in remote sensing images. Meanwhile, there are many phenomena of “synonyms spectrums” and “different spectrum characteristics with the same object” in remote sensing images. Thus, traditional methods by spectrum characteristics are usually with low accuracy and not universal. This paper proposes an automatic approach to extract road damage rapidly based on sidelines using high resolution satellites images and road maps. Road sideline is one of stable geometric features in both pre-earthquake and post-earthquake images, and the change of road sideline is a remarkable evidence of road damage exists. The approach firstly extracts sidelines of undamaged road from images acquired after earthquakes, and then these road sidelines are compared with the road lines before earthquakes supplied by road maps. The damaged segments can be extracted through comparison. The performance of the method is evaluated by an experiment with QuickBird images in the WenChuan earthquake disaster area.

Tropical cyclones (TC) are the most damaging natural hazards for coastal residents because of the associated casualties and property losses. The difficulties of recognizing and locating the eyes of TCs in previous research are because of cloud obstructions in optical images, especially during the formation stage of a TC. The distinct advantage of synthetic aperture radar (SAR) lies in its capability of cloud penetration and hence detection of signal response from sea surface backscatters, which can be used for better detection the eyes of TCs. The aim of this project is to pinpoint the track of a TC by studying the characteristic pattern of its eye, in order to predict its behavior by applying remotely sensed MODIS and ASAR data. This paper uses morphological operators to analyze and extract the region covered by the eye of a TC for the purpose of determining the relative centre of a TC's eye, and then derive the most probable path.

SAR has advantage over optical due to its all-weather working ability and penetration capability. The sensitivity of ENVISAT ASAR HH backscatter to multiple pasture properties (particularly biomass) has been examined over pasture area in Otway, Australia. First of all, decision-tree classification was performed using two MODIS NDVI images to extract grass areas from the study area. Then, over the classified grass area, by relating ENVISAT ASAR HH dB to MODIS NDVI and M.I (soil moisture index, calculated from climate data), it has been proved that the incidence angle of 17° is better than 33° for ASAR HH to detect temporal changes of grass properties and soil moisture, with slightly higher sensitivity to grass biomass than soil moisture. With MODIS NDVI describing the whole study area as reference for specific paddock, Landsat TM was used to assist understanding SAR signal at paddock scale. It was found that HH dB is moderately correlated to the TM NDVI, NDWI and EVI, with priority over NDWI (plant water content).

Underground mining easily causes casualties, and illegal mining is a major contributor to this issue. It is a challenge to locate and differentiate illegal mines from approved mines over a vast area. Over the past few years, our research in satellite differential radar interferometry (DInSAR) to monitor underground-mine-induced surface subsidence has demonstrated the reliability of DInSAR. In this paper, a DInSAR-based illegal-mining detection system (DIMDS) is proposed to exploit the geometric, spatial, and temporal characteristics of those subsidence patterns. Testing results over a coalfield in Asia have proven the efficiency, reliability, and cost effectiveness of DIMDS in finding out illegal mines.

Floods cause enormous economic losses and many casualties around the world each year. Decisions in relation to flood emergencies must be taken efficiently and accurately. Satellite InSAR coherence is a potential technique that is capable to meet such requirements because it is sensitive to water bodies and can cover a vast region. However, the accuracy may be still not good enough due to existence of many other InSAR decorrelation factors if flood map is only based on coherence data. Therefore, this paper proposed a method to improve the flood water detection performance by integrating multi-source datasets with coherence. A case study is also presented to demonstrate the improvement.

CO2 Enhanced Coal Bed Methane (ECBM) has been considered as one of the promising clean coal technologies to reduce green house gas emissions. Australia and China have begun a demonstrational project to explore the potential of ECBM in the gas/coal fields in Shanxi Province, China. A total of 12 ALOS PALSAR images and 354 SPOT VGT-S10 images have been successfully used to establish baseline information of ground deformation and vegetation dynamics on the site based on DInSAR and NDVI time series analysis respectively. Cross analysis between SOPT and MODIS has also been given. Such baseline information will play an important role in the future for monitoring environmental safety at the site.

Regular estimates of pasture biomass are invaluable for managing the supply of annual pasture in Western Australia (WA). NDVI has been used for estimation of biomass in Australia, with some shortcomings which could be overcome by SAR. The regression model between pasture biomass and SAR backscatter could be obtained if sufficient ground measurements of biomass are available. However, biomass measurements over a large area are not easy to be obtained. Therefore, we are trying to relate ENVISAT ASAR/ALOS PALSAR backscatter to NDVI, and then to biomass. It was found that time series of NDVI is significantly correlated to ASAR HH, HV+HH, VV+VH (dB) with R2of 0.71, 0.67, and 0.63, respectively. NDVI is also significantly related to ALOS PALSAR HH (R2=0.82). In conclusion, the investigation confirmed the potential of ASAR and PALSAR data monitoring biomass and its seasonal change in the temperate Southwestern Australia, and provided a solid foundation for further research concerning quantitative estimation of biomass.

Geologic Carbon Sequestration (GCS) has been proposed as one of the "clean coal" technologies for mitigating the more extreme impacts of global warming. GCS has being studied by many nations which aim to reduce atmospheric CO2emissions over the last two decades. However, the safety of underground geologic storage (sequestration) of CO2must be evaluated properly. Therefore, careful site monitoring is the single most important way to manage shortand long- term risks of GCS. Besides, the natural analogs of Geologic Carbon Sequestration can be very useful for evaluating the impact of carbon dioxide leaks from engineered geologic storage reservoir. The natural leakage at Mammoth Mountain site is an example of diffuse CO2gas seepage which could affect vegetation health as revealed by using change detection method. The multi-temporal satellite data sets of Landsat 5TM were used and analyzed. The result shows that NDVI change detection can identify the change of vegetation health around Mammoth Mountain site, CA.

It is a huge challenge to utilise them for interferometry due to the impacts of non-linear azimuth phases and insufficient parameters, although the TerraSAR-X ScanSAR system is able to provide SLC data with both large coverage and high resolution. A Blind Estimation Method for Azimuth Phase Elimination (BEMAPE) is proposed here aiming to resolve these problems through eliminating the azimuth phases blindly. Testing of the method on a pair of TerraSAR-X ScanSAR SLC data has proven its quality and performance, finally making TerraSAR-X ScanSAR interferometry based on SLC data possible.

This study investigates the use of multi-frequency (X- and L-band) Interferometric Synthetic Aperture Radar (InSAR) data for Digital Elevation Model (DEM) generation, coastline detection and land cover mapping over Heard, McDonald and Macquarie Islands in Australian Antarctic Territory. Conventional interferometric processing was applied to generate DEMs using recently acquired SAR data. The variable capacity to extract height information from X- and L-band SAR data was investigated. Elevation data was also compared with previously available DEMs generated using NASA JPL TOPSAR and RADARSAT-1 data. Land cover and surface dynamics and the capacity for coastline detection were also investigated using the multi-temporal, multi-frequency SAR intensity data. InSAR is invaluable as a source of elevation data and for extraction of land cover features and surface dynamics, especially in remote and cloud-affected regions such as the Antarctic.

Subsidence in urban area has the potential to cause severe damage to ecosystems as well as economic loss. Therefore it is important to understand the subsidence phenomenon in urban area. The objective of this study is to investigate the terrain deformation in the metropolitan area of Jakarta, Indonesia using multiple satellite radar imagery. In this study the GEOS-PSI, a software developed at UNSW for persistent scatterer radar interferometry, was used to map the land subsidence in Jakarta region with L-band ALOS PALSAR radar images. A total of 17 ALOS PALSAR images acquired from 31 January 2007 to 26 September 2010 over Jakarta were used in this study. The results demonstrated that the land in the area of Jakarta was deforming at different rates. Several subsidence bowls with peak displacement rates over -150 mm/yr along the radar looking direction have been observed at the northern Jakarta.

Digital elevation models (DEMs) can be generate by interferometric SAR (InSAR) and radargrammetry techniques from different acquisition of Synthetic Aperture Radar (SAR) images. Radar imaging systems record both the phase and intensity information of the backscattered signals from ground. InSAR utilizes the phase information of the images to extract useful geodetic information, such as the height of terrain, ground deformation and movement. However, InSAR technique is constrained by the temporal and spatial baselines between the images used, as well as the various atmospheric conditions at the time of acquisitions. In comparison, radargrammetry technique uses the intensity (power) information in a stereo-pair of radar images. It is similar to stereo-grammetry or photogrammetry which is a classic method for relief reconstruction using airborne/spaceborne optical imageries. In this paper, the radargrammetric DEM quality improvement method using SAR image processing is presented by ENVISAT/ASAR imageries.

Phase unwrapping is one of the most challenging steps in synthetic aperture radar (SAR) interferometry processing. With the rapid advancement of SAR technologies, the interferometric data sets from newly launched satellites are becoming larger and larger. When the computing resources required for unwrapping an input data set exceed computer hardware capabilities, phase unwrapping becomes even more problematic. In this paper, a new method is proposed in order to address the problem of unwrapping large data sets. The proposed method separates the unwrapping procedure into two stages. First, an approximateL1-norm phase unwrapping solution is efficiently obtained from a simplified minimum-cost flow network. Then, the blocks partitioned from the input data set are unwrapped by solving the corresponding independent network optimization problems directed by the approximate solution, either in parallel or in series. By then simply aligning the unwrapped blocks, a full-size unwrapped result is obtained. A significant advantage of the proposed method is that the globality of phase unwrapping solutions can be guaranteed. Using an interferogram with a size of 120 000 × 9274 pixels, the authors demonstrate that the proposed method is able to efficiently unwrap very large interferometric data sets using limited computing resources.

This paper discusses the synergistic use of multi-temporal ALOS/PALSAR and SPOT multi-spectral images for land cover classification in the Ho Chi Minh city area in Vietnam. Five PALSAR images and SPOT 2 multispectral image were used for classification. Integration of additional information such as interferometric coherence, textural data was also studied. Different combinations of multi-temporal SAR backscatter images, coherence data, SPOT multi-spectral bands, texture measures were generated and tested in order to determine the best combination, which gives the highest classification accuracy. Results indicate that the combination of SAR and optical images gives significantly higher classification accuracy than using a single type of data, and that the Support Vector Machine (SVM) classifier could outperform the Maximum Likelihood (ML) classifier in cases of classification of the combined datasets.

Satellite Differential Radar Interferometry (DInSAR) has demonstrated its ability for monitoring mine-induced ground subsidence. However, it is still a challenging task to routinely identify all mining activities from the large-scale coverage interferogram, especially the illegal mines. In response to this challenge an underground mining detection system based on DInSAR is described. The system is tested over a dense mining area in Asia. With such a system it is hoped that the detection efficiency of illegal underground mining using DInSAR can be improved.

On the 23rd of September 2009, Sydney encountered its most severe dust storm in 70 years. The dusts were originated from the Lake Eyre Basin and elevated and swept across the Australian Capital Territory, New South Wales, and Queensland by gusty winds. Ground air quality observation indicated that the dust particle density was 70 times higher than the normal when the dusts struck Sydney. The authors have researched MODIS satellite optical imagery in order to monitor this severe dust storm, and have extracted the information from the satellite images through computing the brightness temperature difference of two thermal infrared channels of MODIS imagery. This method is effective in separating dust and clouds. The mass of the dust plume, therefore, has been estimated using a retrieval model. However, the result of the mass is believed to be under-estimated because the extent of dusts was too great to be covered by a single MODIS image.

The Digital Elevation Models (DEMs) are an important source of topographical data for many scientific and engineering applications. Where topographical data are unavailable, global coverage elevation data sets, typically DEMs from remotely sensed data, are the main sources of such information. Interferometric SAR (InSAR), is a useful method for low-cost, relatively precise and wide-coverage surface DEM generation. However, ground deformation should somehow be excluded from InSAR-based DEM generation. To identify surface deformation areas, the so-called Differential InSAR (DInSAR) is a commonly used method. In this paper, the authors propose a two-step DEM generation method: the ground deformation area detection using DInSAR technique and deformation area exclusion in InSAR DEM generation by detected mask.

The `baseline' is one of the most important parameters in Interferometric Synthetic Aperture Radar (InSAR). The quality of InSAR products is significantly affected by the accuracy of baseline estimation. In this paper, a new approach to improving the baseline estimation is proposed. The main advantage of estimating baseline by the proposed method is that the calculation can be performed without the need for phase unwrapping and ground control points (GCPs). The final result shows that a better differential interferogram can be generated using the proposed baseline estimation method.

Geodetic information of terrain can be measured using remote sensing techniques such as photogrammetry, airborne laser scanner (ALS) and interferometric synthetic aperture radar (InSAR). They are considered to be relatively more cost-effective than and complementary to conventional ground-based surveying methods. Our previous studies demonstrated the capability of using differential InSAR for underground longwall mining subsidence monitoring in New South Wales, Australia. The mining subsidence (vertical surface deformation) was measured using DInSAR with the assumption of negligible horizontal deformation. However, the ground surveying data shows that the underground mining activity may induce horizontal surface deformation. Therefore, both ascending and descending orbits and different swath modes of ENVISAT/ASAR data are used in this paper to quantify the vertical and horizontal vectors of mining deformation.

The purpose of this paper is to demonstrate the application of C-band ERS-1/2 and ENVISAT radar images to investigate the urban subsidence due to groundwater extraction. Cities in Australia without groundwater being over-extracted are compared to cities in Australia and China with groundwater being over-extracted. The Persistent Scatterer InSAR results are interpreted and compared to investigate the effect of groundwater extraction to urban subsidence. The GIS software is used to interpret the Persistent Scatterer InSAR results. The combined methods between Persistent Scatterer InSAR and GIS allow an integration of information from various sources and hence improve the efficiency for interpreting the data. A total of 15, 18 and 27 images of ERS-1/2 images acquired from 08/1992∼12/1996, 04/1992∼04/1997 and 08/1992∼07/2002 for Canberra, Sydney and Newcastle respectively are chosen to be investigated with Persistent Scatterer InSAR. Together with the above images, ten ERS-1/2 images from 06/1992∼12/1996 and nine ENVISAT images from 12/2003∼06/2006 acquired over Perth (Australia) and Northern China respectively are also chosen for similar investigation. The results show that the deformation rate from the cities with groundwater overextracted, are significantly larger than the cities without groundwater over-extracted. The results have demonstrated the effect of groundwater extraction to urban subsidence.

This paper describes about accuracy of differential interferometric synthetic aperture radar (DInSAR) using different resolutions of external digital elevation models from ERS-1/2, STRM, LiDAR. For DInSAR technique, external DEMs have to have correct surface information. Typical DInSAR processes used the space-bone SAR data such as SRTM, ERS-1/2, JERS, and RADARSAT. However, they did not remove the vegetation and non-terrain feature. It is the cause of errors in final result using non-correct DEM. For improved accuracy of DInSAR results, in this paper, LiDAR DEM which has high spatial resolution with removed non-terrain data is used and compare with other DEMs.

This letter proposes a new technique for noise reduction applied to synthetic aperture radar interferometry. This technique involves a nonlinear filter that separates the interferogram into two components: one containing the smooth (low frequency) part and the other containing the detail (high frequency) part. The smooth part is obtained using a combination of a median filter and a smoothing filter. The detail component is obtained by subtracting the smooth component from the original signal. This detail component is filtered to remove noise and then added to the smooth component to generate the final output. Both simulated and real data are used to evaluate the performance of the proposed technique under different conditions. The experimental results show that the proposed technique outperforms most commonly used interferometric phase filters

Abstract-This paper demonstrates the capability of differential interferometric synthetic aperture radar (DInSAR) for measuring the subsidence at Wairakei and Tauhara geothermal fields in New Zealand. Both conventional two-pass DInSAR and coherent targets were utilised. The potential of integration of DInSAR and mathematical modelling for geothermal subsidence monitoring and future subsidence prediction are also discussed.

This paper demonstrates the use of differential interferometric synthetic aperture radar (DInSAR) for mine subsidence monitoring in Australia. The C-band SAR imagery acquired by ERS-1/2 and Radarsat-1 and L-band data acquired by JERS-1 were tested. As the satellites have different re-visit periods so that the mine subsidence occurred during the intervals of 1, 24, 35 and 44 days can be observed. The C-band InSAR results generally have lower coherence over vegetated areas, but the Radarsat-1 fine-beam mode data demonstrated that decorrelation can be reduced by having finer imaging resolution and shorter temporal separation. Another difficulty of DInSAR for mine subsidence monitoring is to resolve the phase ambiguity in interferogram. The L-band SAR data with comparatively longer wavelength than C-band showed it is more suitable for mining subsidence monitoring where large displacement over a small spatial extent occurs. © 2005 IEEE.

A high resolution digital elevation model (DEM) enables easy derivation of subsequent information for various applications. This work uses real-time kinematic (RTK) GPS to examine the quality of some DEMs generated by such means as radar interferometry (InSAR), airborne laser scanning (ALS) and photogrammetry. The preliminary results show that a DEM generated from ALS has the highest accuracy with a RMS error of 0.09 /spl sim/ 0.3 m. The RMS errors of DEMs derived by photogrammetric and radar interferometric techniques are 1.03 /spl sim/ 3.75m and 4.26 /spl sim/ 27.81 m respectively.

Differential radar interferometry (DInSAR) has been used more and more widely to monitor crustal deformations due to underground mining and oil extraction, earthquakes, volcanoes, landslides, and so on. However, tropospheric heterogeneities have been identified as one of the major errors in DInSAR, which can be up to 40 cm as derived from dual-frequency GPS measurements in the example given in this paper. Therefore, it is crucial to correct the tropospheric heterogeneities in the DInSAR results for monitoring crustal deformation. These corrections from several GPS stations in the radar imaging area can be interpolated and applied to the DInSAR results. The discussions are based on data from the Tower Colliery test site southwest Sydney, Australia.