Conference Agenda

1. Introduction

InSAR technology currently stands as a prominent field within remote sensing monitoring techniques, finding widespread application in diverse areas such as ground subsidence monitoring and geological hazard investigation, among others [1-2]. The processing workflow of InSAR data involves a blend of knowledge across multiple disciplines, including computer science, geodesy, signal processing, and remote sensing. The theoretical models underlying InSAR are intricate, and the steps involved in processing are numerous. Furthermore, the sheer volume of data necessitates significant computing power, storage resources, and other hardware devices. Harnessing the vast SAR remote sensing data and cloud computing resources available on the "AI Earth" Cloud platform, we have introduced a cloud-based InSAR comprehensive computation system. This system effectively dismantles barriers and complexities associated with InSAR technology, empowering users to directly apply InSAR results to address diverse industry applications without becoming overly focused on the intricacies of the technology itself. The algorithms employed by the system encompass a range of application scenarios, satisfying the requirements for comprehensive surveys and the emergency monitoring of natural disasters.

2. GPU-assisted InSAR processing

This paper aims to employ Graphic Processing Unit (GPU) techniques to accelerate the deformation extraction process from big Interferometric Synthetic Aperture Radar (InSAR) datasets, using Geospatial Cyber-Infrastructure (GCI) form AI Earth Geoscience Cloud Platform to bridge GPU with InSAR domain knowledge. We choose GPU technology because this technique has rapid development, and it offers local parallel data processing with low costs and latency. Our GCI integrates GPU and parallel deformation extraction algorithms to achieve near real-time extraction of deformation areas from big Sentinel-1 InSAR images, which also provides a high-performance computing platform for other InSAR data analytics in future studies. Using GPU technology and high-performance computing cluster to realize massive InSAR data calculation issues in a wide area is a critical technical problem, including efficient registration and high-precision ESD estimation methods [3].

(1) Rapid registration of SAR images. This study will use GPU technology to improve the efficiency of SAR image registration based on the Cross-Correlated method in traditional SRA image registration.

(2) ESD (Enhanced Spectral Diversity) estimation. This study combines GPU high-performance computing based on existing ESD theory to improve the efficiency of ESD estimation.

(3) We use traditional methods to realize the InSAR time series analysis process. This GCI will accelerate the deformation

extraction and could be applied to address significant data challenges for other InSAR studies.

3. Easy-to-use service cloud-based computing system

For different types of disasters and application researchers, the customizable service is designed to remove the application obstacles the user may encounter, including the constraints problems of full process data processing. The service applied a GPU rapid processing technology to provide users with quasi-real deformation analysis results in time with limited parameter input. The customized service system is built with adaptive parameters to reduce the difficulty for primary users of InSAR and considers different application scenarios to meet various monitoring requirements of natural hazards. The system provides wide-area crustal deformation monitoring results and fine deformation monitoring in critical areas [4].

The InSAR comprehensive computation system, built on cloud services, surmounts traditional limitations such as data downloading, storage, parameter configuration, and computing resources. It simplifies the input of parameters for various types of surface deformation and applications, providing users with rapid deformation analysis outcomes . At present, the InSAR processing environment has been successfully launched on the AI Earth cloud platform. Users can promptly submit tasks and leverage its capabilities via the platform (https://engine-aiearth.aliyun.com/).

4. Conclusion

This paper constructs Easy-to-use and fast-processing algorithm for InSAR data based on GPU technology and a high-performance computing cluster in AI Earth Geoscience Cloud Platform. The GPU acceleration optimization is carried out for the relatively time-consuming steps in processing InSAR data to achieve the fast calculation of InSAR deformation of massive Sentinel-1 SAR data. Based on the massive computing resources of the AI Earth Geoscience Cloud Platform, a customizable InSAR service is built for different application demands. Based on this system, it can realize targeted early identification of geological hazards, hidden dangers, dangerous situation, and disaster identification and provide efficient technical means for rapid post-disaster emergency response.

References

[1] Dai K, Li Z, Xu Q, Bürgmann R, Milledge DG, Tomas R, Fan X, Zhao C, Liu X, Peng J, Zhang Q (2020) Entering the era of Earth-Observation based landslide warning system. IEEE Geosci Remote Sens Magaz 8(1):136–153

[2] Cigna F, Tapete D (2021) Sentinel-1 big data processing with P-SBAS InSAR in the geohazards exploitation platform: an experiment on coastal land subsidence and landslides in Italy. Remote Sens 13(5):885

[3] Yu Y, Balz T, Luo H, Liao M, Zhang L (2019) GPU accelerated interferometric SAR processing for Sentinel-1 TOPS data. Comput Geosci 129:12–25. https://doi.org/10.1016/j.cageo.2019.04.010

[4] Li, Y., Jiang, W. & Zhang, J. A time series processing chain for geological disasters based on a GPU-assisted sentinel-1 InSAR processor. Nat Hazards 111, 803–815 (2022). https://doi.org/10.1007/s11069-021-05079-9

Sentinel-1 satellite provides free access to dual-polarization (VV and VH) images. The integration of information from both VV and VH channels in polarimetric persistent scatterer interferometry (PolPSI) techniques is expected to enhance the accuracy of ground deformation monitoring as compared to conventional PSI techniques, which utilize only the VV channel for Sentinel-1.

Persistent scatterer (PS) and distributed scatterer (DS) points play a crucial role in the PSI techniques. PSs with high phase qualities are commonly found in urban areas. As a complementary for PSs, DS points whose phase is affected by noise are commonly present in rural areas.

In this study, the identification and selection of PS and DS is based on an optimal channel created by combining the two polarimetric channels. PS candidates are selected through the amplitude dispersion (DA) criterion. To jointly utilize both PS and DS points, an adaptive speckle filtering based on the selection of homogeneous pixels (HP) was applied to the coherency matrix. Then, DS candidates were identified by using the average coherence criterion. Finally, using both PS and DS points, the Coherent Pixels Technique (CPT) was employed as the Persistent Scatterer Interferometry (PSI) processing method.

To analyze how the introduction of the VH channel helps improve the deformation measurement results, an experiment over Barcelona in Spain was carried out. The dataset consists of 189 dual-polarization SAR images acquired between December 2016 and January 2021. A wide variety of scenarios are present in this region, i.e., airport, harbor, and urban areas which exhibit diverse orientations of streets and buildings with respect to the acquisition geometry. Additionally, ground deformation is expected over some areas due to settlement of recent constructions and in the harbor.

Regarding PS, there are two cases in which the VH data contribute to improve the PS density. The first corresponds to scatterers that are oriented with respect to the incidence plane. The VH amplitude value of those scatterers are higher than VV channel. The second case appears more frequently than the first case and corresponds to pixels in which the VH amplitude is low but stable. Through the application of PolPSI technique, the VH channel can contribute to the selection of high-quality pixels by reducing the presence of peaks and fluctuations present in the VV channel, thus enabling the selection of pixels with good quality which would not have been identified if only VV data were processed (Luo, et al., 2022).

Instead of increasing the density, the contribution of VH channel for the identification of DS points is associated with a more accurate selection of HP (Luo, et al., 2023). The polarimetric information enables the differentiation of pixels that belong to different targets but have similar amplitude values in the VV channel. This results in a more reliable deformation measurement, as the HP group becomes more accurate.

A comparison with experimental data and all cases (single- and dual-pol) serves to illustrate and evaluate the performance of PolPSI in this domain.

Reference:

Luo, J., Lopez-Sanchez, J. M., De Zan, F., Mallorqui, J. J., & Tomás, R. (2022). Assessment of the Contribution of Polarimetric Persistent Scatterer Interferometry on Sentinel-1 Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 7997-8009.

Luo, J., Lopez-Sanchez, J. M., De Zan, F. (2023). Analysis of the performance of polarimetric PSI over distributed scatterers with Sentinel-1 data. International Journal of Applied Earth Observation and Geoinformation, 125, 103581.

The heavy industrial district in the Shenyang municipality in Northeast China plays a relevant role in the economic and social development. The hard mining activities have a strong impact on local environment due to continuous ground excavations related to coal and iron extraction. Therefore, Shenyang is subject to a multi-hazard exposure including subsidence, landslides, ground fissure and building inclination. In particular, starting from the ESA DRAGON-4 project we begun to study the Shenyang city and the Fushun open pit mine by means of multi-source remote sensed data. One of the most important adopted methodology consisted on the use of the Advanced InSAR (A-InSAR) technique able to provide ground velocity and displacement time series with millimetric accuracy per year. Then, in the framework of the Dragon-5 project, we went on to monitor such areas, and to achieve this goal, a new COSMO-SkyMed (CSK) images dataset, operated by the Italian Space Agency (ASI), was required to extend the investigated period using the Persistent Scatterers Interferometry (PSI) technique. In fact, results from the previous Dragon-4 project indicated landslides around open-pit mines, building instability and structural damages. Furthermore, the tunnel construction of underground lines in Shenyang has caused surface fissuring, subsidence and sinkholes.

The city of Shenyang is covered by two distinct descending CSK frames along the descending orbit. One frame covers the western part, whereas the other covers the eastern part. The request of CSK images was carried out by submitting a project card to the Italian Space Agency (ASI). The project card ID 896 – DRAGON-5 was submitted via the ASI portal (https://portal.cosmo-skymed.it/CDMFE/home#).

The selected CSK images have been acquired along the descending orbit in the STR_HIMAGE mode. A total of 71 images were captured for the western part of Shenyang city, spanning from April 7, 2019 to November 11, 2023, while 94 images were acquired for the eastern part, interesting the Fushun open pit mine area, and covering the period from October 13, 2018 to December 30, 2023.

In this work, we show the updated results for the Shenyang and Fushun areas retrieved through the Enhanced PS technique, especially focusing on the pit mine site and the urban infrastructures (i.e. bridges, underground lines, embankments, etc.).

Acknowledgments

The COSMO-SkyMed data are provided by ASI through the project card ID 896.

In the framework of the research line that the Dragon 5 project n. 58113 “SARchaeology: Exploiting Satellite SAR for Archaeological Prospection and Heritage Site Protection” has dedicated to the improvement of satellite techniques for monitoring and conservation of archaeological sites, the present study has aimed to trial a multi-temporal approach based on optical multispectral imagery as a complement of Synthetic Aperture Radar (SAR) based investigations. The scope was to assess the impacts due to climate change-induced transformations of water bodies and marshlands in semi-arid environments that may threaten the conservation of archaeological sites.
The demonstration site is located in central-southern Iraq, encompassing a number of water bodies (lakes, swaps, streams and springs) that have been largely affected by changes over the last decade, including significant influences due to climate change.
Recent studies converge on warning about climate change impacts on Iraq’s natural resources, including a water shortage crisis. With surface waters projected to dry up within the next 20 years, environmental consequences are also likely to affect local cultural landscapes and heritage. Indeed, Iraqi archaeological sites and historic settlements, canals and palaeo-landscapes, are often located in proximity to water bodies and marshlands, thus shrink-swell cycles due to drought and flooding caused by extreme events, and consequent surface water run-off and accumulation, may accelerate cultural heritage deterioration, up to severe damage and disappearance.
On the other side, there is a growing literature and several national and international efforts are being conducted to document the rich cultural heritage of Iraq and improve the digital recording and databases of sites. Therefore, an analysis of the impacts due to climate change on Iraqi archaeological sites supported by Earth Observation data is more than timely and can rely on abundant authoritative geospatial datasets.
In order to assess the scale of recent impacts, a multi-temporal back-analysis was performed across a 8.000 km2 area, spanning from south of Baghdad to Basra. Multispectral Sentinel-2 images were processed to estimate annual changes in water level and marshland surface extent of both permanent and ephemeral lakes (Hammar Lake, Najaf Sea, Hor Al-Shuwaija), artificial reservoirs (Razzaza, El Delmej) and the Ahwar of Southern Iraq UNESCO World Heritage Site. Normalized Difference Vegetation (NDVI), Normalized Difference Water (NDWI) and Moisture Indexes enabled automatic per-pixel image classification, followed by thresholding and, when needed, manual refinement, to assess surface extent changes and spatio-temporal trends of water bodies and marshlands in 2015-2023. The observed divergent behaviour between the analysed bodies highlights a diverse range of situations, and thus different risk levels for heritage assets conservation. The integration with environmental and contextual data, and information from UNESCO reports, confirms the current challenges in preserving historical marshlands and the relationships with anthropogenic activities. The present paper therefore provides a spatio-temporal account of the evolving situation across a wide cultural landscape, and attempts first considerations about future projections should the same water-cycle dynamics continue as per the Sentinel-2 based observations.