Current remote sensing image dehazing models often encounter challenges under dense haze conditions due to the significant loss of high-frequency information, impairing accurate scene recovery. In addition, these models typically do not incorporate additional sensor information to guide the generation of dehazed images. However, satellites generally house a variety of image sensors, among which panchromatic sensors are included. Panchromatic (PAN) images, which usually present clear boundary information, could potentially assist models in generating dehazed images. We introduce Guidance Net, an innovative model that utilizes historical PAN images to enhance the dehazing process. Specifically, we introduce the Adaptive Self-Attention Interest Texture Filter (ASAITF) for the effective integration of guidance information from PAN images. Additionally, recognizing the limitations of existing methods that predominantly emphasize low-frequency features, we propose the Redundancy Filtering Mechanism (RFM), aimed at efficient high-frequency feature extraction and seamless integration within Vision Transformer architectures. To ensure a comprehensive evaluation, we also present the PAN Guidance dataset. Experimental results indicate that Guidance-Net surpasses state-of-the-art methods in generating dehazed images guided by prompts.

Convolutional neural network (CNN) and Transformer-based hybrid models have been successfully applied to hyperspectral image (HSI) classification, enhancing the local feature extraction capability of single Transformer-based models. However, these Transformers in the hybrid models suffer from structural redundancy in components such as positional encoding (PE) and multi-layer perceptron (MLP). To address the issue, we propose a novel attention architecture termed twice attention convolution module and simplified Transformer (TAST) for HSI classification. The proposed TAST primarily consists of a twice attention convolution module (TACM) and a simplified Transformer. TACM is designed to improve the ability to extract local features. In addition, we introduce the simplified Transformer by removing the PE and MLP components from the original Transformer, which captures long-range dependencies while simplifying the structure of the original Transformer. Experimental results on four public datasets demonstrate that the proposed TAST model outperforms both state-of-the-art CNN and Transformer models in terms of classification performance, with improvements in terms of overall accuracy (OA) around 3.87%-34.95% (Indian Pines), 0.35%-23.43% (Salinas), 0.37%-6.05% (WHU-Hi-LongKou), and 0.65%-10.79% (WHU-Hi-HongHu).

Deep learning-based hyperspectral image (HSI) classification models typically utilize multiple feature extraction layers to learn the features of land covers. Nevertheless, they encounter challenges, e.g., 1) Transformers require substantial computational resources, and 2) these layers are carefully assembled and designed. Recently, large kernel convolutional neural networks (LKCNNs) show excellent performance in natural visual tasks. To tackle these limitations and explore the capability of LKCNNs for HSI classification, we present a novel simple and powerful multi-scale large kernel convolutional neural network architecture (MSLKCNN) with the largest kernel size as large as 15 × 15, in contrast to commonly used 3 × 3, for HSI classification. MSLKCNN avoids these specialized designs, comprising a noise suppression module (NSM) and a multi-scale large kernel convolution (MSLKC). Specifically, NSM is first used to suppress the noise and reduce the number of the bands before extracting the features. Then, MSLKC, as the only feature extraction layer of MSLKCNN, joints three parallel convolutions to capture the features of various types (i.e. spectral, spectral-spatial) and ranges (i.e., small local, larger local, and global) from the dimension of scale: (C1) convolution with a kernel size of 1 × 1 is used to extract spectral features; (C2) multi-scale large kernel depthwise separable convolution (MLKDC) is proposed to learn the spectral-spatial features of different ranges including short-range, middle-range, and long-range; and (C3) multi-scale dilated depthwise separable convolution (MDDC) is designed to aggregate the spectral-spatial features between land covers at various distances. Extensive experimental results on three public HSI datasets demonstrate the competitiveness of the proposed MSLKCNN compared with several state-of-the-art methods.

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.

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).

Interferometric synthetic aperture radar (InSAR) has proven to be a unique tool for investigating large-scale surface deformation. Although the applications of InSAR are widespread, especially following the deployment of the Sentinel-1A/B twin satellites, large-scale automatic InSAR processing is primarily limited by phase unwrapping for interferograms with low coherence and strong atmospheric artifact. Fixing phase unwrapping errors in steep relief and dense vegetation scenarios is challenging for the existing methods that rely on either redundant interferogram loops [e.g., linear absolute shrinkage and selection operator (LASSO)] or spatial connectivity of the unwrapped patches [e.g., flux analysis (FA)]. Here we develop a hybrid approach by integrating Tikhonov regularization (TR) and FA to automatically correct phase unwrapping errors, abbreviated as TR and FA corrector (TRAFAC). Our methodology involves three major steps. First, we identify phase unwrapping errors by analyzing phase closures. Second, we make a TR model to estimate phase unwrapping errors for pixels within multiple loops. Importantly, our approach departs from pixel-by-pixel inversion, opting instead to estimate an unknown for the entire block, ensuring uniform correction across all pixels within the block. For the remaining few blocks within isolated loops, we employ the FA algorithm as the third step. We test our approach using both synthetic and real data collected along the Longmenshan fault zone (LMSFZ) in Sichuan, China. The results show that our approach can successfully correct 95% of unwrapping errors in such a demanding scenario. Our TRAFAC approach is able to correct phase unwrapping errors in isolated loops and isolated patches, thereby can contribute to the reliability of automatic large-scale InSAR processing systems.

The implementation of a wide-land deformation monitoring program in Eastern states of Australia, characterized by an extensive landmass and extensive coastlines, necessitates the generation of deformation maps with a significantly larger spatial extent. A pragmatic approach for achieving such coverage involves the integration of multiple Interferometric Synthetic Aperture Radar (InSAR) time series results, encompassing various tracks and frames. The Sentinel-1 satellite constellation is facilitating the generation of expansive ground surface deformation, while the initial challenge in obtaining such product arises from the varying spatial coverage of the Sentinel-1 data. To overcome this obstacle, we proposed a novel Sentinel-1 image definition to facilitate consistent interferometric processing. Nevertheless, prior to the formation of any large displacement map, the inconsistency between different SAR image scenes must be addressed. Several factors contribute to these inconsistencies in observations, such as differences in the angle of observation at the overlapping regions of adjacent image tracks, and imprecise estimations within the dataset itself (e.g., burst or swath discontinuities). This study delves into solutions for these challenges, specifically: 1) introducing a computer-vision-based algorithm for InSAR dataset quality assessment, and 2) proposing a global least square mosaicking procedure for the amalgamation of tiles from multi-tracks and frames. Statistical analyses show that better accuracy can be achieved. The mosaicking InSAR product demonstrates the capacity to quantify ground surface changes, which also exhibits a correlation with other geological layers, and the subsidence range of [-7, 10] mm/yr shows a strong association with clay content levels ranging from 10% to 30% along the Darling River.

In this work, we implement a hybrid method to utilize sufficient information by aggregating both fine-grained and globally contextual features for point cloud semantic segmentation with a hierarchical network. By surpassing the defects of convolution operation mainly for extracting low-level features, we combine higher-level cross-attention based Transformer to investigate the importance of long-range relations together with position embedding for multiscale feature representation. Specifically, adding a learnable token to the feature sequence of a layer, a Transformer encoder is first implemented with limited scope to embed these features. Furthermore, instead of performing all-to-all attention, we merely fuse tokens spanning various scales. To improve efficiency, we propose a simple yet efficient token-fusing architecture based on cross-attention, in which the computation of attention maps can be restricted within linear time by only using a token to calculate the query. The cross-attention module can be efficiently aggregated in a multiscale network to further enlarge the scope of the receptive field for attention. Experiments show that our MCTNet achieves promising results on three largest point cloud datasets, DALES, DublinCity and S3DIS datasets. For the DALES benchmark dataset, MCTNet improves the mean intersection-over-union (mIoU) to 83.3% and the overall accuracy (OA) to 98.3%, which outperforms other existing baselines. We also perform abundant ablation studies on various attention and normalization modules and discuss the effect of parameters to validate the descriptive power of cross-attention module and provide an understanding of how long-range dependency can be used to learn fair and unbiased features.

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.

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.

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.

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, 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.

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.

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.

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.

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.