Analyzing road network subsidence and sinkholes is crucial for ensuring urban traffic and people's safety. InSAR technique is a non-contact measurement technique with high spatiotemporal resolution, wide monitoring range, and unaffected by road conditions, which is of great significance for the analysis of deformation hazards of man-made linear infrastructures, such as road networks and high-speed railways. In this study, we adopt 44 Sentinel-1A images from January 2022 to July 2023 to study the deformation of the road network in Longgang District, Shenzhen, China based on PS-InSAR. The road deformation is further analyzed associated with sinkhole information from field investigation. The results show that the deformation rate of the road network is between -38.3 mm/year and 16.9 mm/year, and the correlation coefficient between the top burial depth of the sinkholes and the maximum deformation rate is 0.58.

In recent years, the application of deep learning in the field of interferometric synthetic aperture radar (InSAR) measurement has gradually increased. Unlike the real-value data typically handled by conventional deep learning methodologies, InSAR technology grapples with complex-value data. Specifically, the phase values of this complex data are constrained within the interval ($-\pi,+\pi $]. However, in deep learning frameworks, the calculation of loss functions often neglects the mathematical constraint that phase differences should also adhere to the [$-\pi,\pi $] range. This oversight impedes the model’s ability to accurately fit the target data, thereby diminishing training efficiency. To address this challenge, we introduce three foundational loss functions tailored for fitting wrapped phase values: cosine similarity-based (La), mean square error (Lb) for wrapped phase, and ensemble coherence-based (Lc). By combining these approaches, we formulate a total of seven distinct loss functions. To identify the most effective one, we evaluated their performance using a temporal convolutional network (TCN) as a test scenario. Our findings reveal that the optimal combination function, Labc, achieves a 72.78% accuracy rate. Compared to other function with over 72% accuracy, Labc reduces the required iterations for model convergence by at least 30% and effectively delineates terrain feature boundaries.

Precise deformation surveying is essential for ensuring safety and sustainable development in densely populated cities. Synthetic aperture radar interferometry (InSAR) is a technique well suited for large-scale deformation measurement. It is widely applied in monitoring geological disasters such as earthquakes, landslides, and volcanoes. However, it faces challenges when used to monitor urban infrastructure, which requires higher precision and more details. To overcome these challenges, we introduce an innovative, object-oriented multitemporal InSAR (Obj-InSAR) framework. Compared with state-of-the-art InSAR methods, Obj-InSAR shifts the basic processing unit from pixel to object. This allows the use of additional information provided by the object/segment to enable detailed and precise results, as required in urban scenarios. In this framework, a novel object-based plug-in network is designed to establish an impressively dense network for parameters solving. Additionally, a new object-based adjustment procedure through 3-D point cloud alignment is proposed to accurately determine the location of InSAR scatterers and guide network construction. As a result, our approach produces a more detailed, accurate and precisely geolocated 4-D infrastructure point cloud than conventional pixel-oriented frameworks, enabling effective monitoring of urban deformation. A case study confirms the usefulness and adaptability of this approach in urban SAR interferometry. These advancements will contribute to the development of time-series InSAR technology in the new era, enhance the capability of monitoring complex urban deformation in high-density cities, and serve urban safety operations and sustainable economic development.

Accurate automated extraction of coseismic deformation from Synthetic Aperture Radar (SAR) data can be challenging owing to interference from inherent atmospheric noise. Particularly, the limited displacement of small-to-moderate earthquakes (Mw<6.5) can easily be obscured by phase errors and/or noise. To address this issue, we developed an autoencoder model based on a deep learning framework (i.e., Pytorch) to automate the accurate extraction of coseismic displacement from Interferometric SAR (InSAR) interferograms. We constructed a training dataset using simulated interferograms. Our trained model performed well for interferograms with real noise. When applied to worldwide real earthquakes of various rupture styles, the model produced clear coseismic displacement with less noise and a better fit to coseismic fault models compared to the differential InSAR method without noise correction. Additionally, it achieved co-seismic deformation similar to popular InSAR time series and GNSS methods. The approach will enhance the proceduralization and popularization of InSAR applications in earthquake monitoring, providing improved constraints on the kinematic characteristics of earthquakes.

Synthetic Aperture Radar Interferometry (InSAR) technique shows huge potential in the deformation monitoring of coastal seawalls, which is essential to guarantee the safety of people's lives and social property. However, a single view InSAR dataset can only identify satellite-oriented point-like targets (PTs), and the one-dimensional deformation would cause deformation estimation deviation and potential risk undetected. This study developed a multi-view InSAR analysis method to present the first 3D deformation monitoring and structural-level deformation analysis of coastal seawalls. The density and accuracy of selected PTs are improved by a spatial-temporal similarity-based multi-view PTs joint extraction method, and the deformation accuracy is improved through a parallax entropy weighted 3D deformation inversion method. Taking the Donghai Seawall as an example, the 3D deformation of the seawall is revealed, indicating a rapid-slow-steady deformation process.

Standard remote sensing observation (RSO) access and formulization is essential to Internet of Things (IoT) data management, such as in precision agriculture (PA). Because of the heterogeneous characteristics and the petabyte data size of RSO, massive remote sensing processing in RSO management has been hampered. Here, we present a heterogeneous access metamodel for efficient RSO management (HAMERM) and verify it in PA. The structure of basic metadata components is defined. A five-tuple metadata structure based on the metaobject facility is designed. HAMERM consists of identification, platform, observation, product, and access, which represent the five aspects of RSO metadata information. In addition, the flatMap/reduceByKey algorithms and the table structure have been proposed under Sensor Web and Geographic Information Science (GIS) techniques. Intensive experiments in Guangdong Province, China are conducted to test the proposed method. Two RSO metadata formulization instances were conducted to examine the ability of sheltering the differences of multisource and heterogeneous RSO. Experiments containing data storage and data soil moisture (SM) mapping were performed. The results suggest that the HAMERM method achieved a performance 30.1 times higher than that of Hadoop and three times higher than that of Spark (stand-alone). Consequently, the proposed HAMERM can be applied to achieve efficient SM mapping within PA, which is helpful for efficient RSO management for the IoT.

Nighttime light (NTL) radiance can reflect human settlements and activities. A lot of studies indicate that NTL brightness can be a sound proxy of socioeconomic factors on large scales, such as the population size, gross domestic product, electric power consumption, etc. However, few studies have been dedicated to these topics on fine scales. In this study, we examined the correlation between the Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (S-NPP/VIIRS) NTL intensity (NLI) and population density, as well that between NLI and per capita income at two census units’ levels, census tract and block group, in two research areas. The result shows that the NLI has a moderate or weak positive correlation with the population density at both scales. However, when land use type is integrated with population density, the correlation becomes very strong. The NLI and per capita income have a weak or very weak negative correlation at both scales. Moreover, we find that the correlation coefficient is positively correlated with the unit’s size. The larger scale also has a higher correlation coefficient. The research conducted here could be beneficial for the application of S-NPP/VIIRS NTL data in studying socioeconomic activities in human settlement areas.

Interferometric synthetic aperture radar (InSAR) has been more and more applied in acquiring long-term deformation of land surface in a large coverage and is becoming a routine investigation technique. Validation of the InSAR results depends largely on thein situmeasurements. These measurements are usually point-wise and of high cost, as many sensors need to be set up for the long-term monitoring. In applications associated with a large area, qualitative and low-cost validation may be more necessary at the first place, which is still a challenge. In the recent decade, the crowdsourcing and volunteered geographic information (VGI) have been more and more accepted in the field of geoinformatics. Inspired by these, this letter proposes an InSAR crowdsourcing annotation system to integrate the InSAR displacements and the photographs uploaded by volunteers. We processed 119 Sentinel-1A data ranging from 2017 to 2021 to derive long-term displacements. Then, based on the InSAR displacements, task areas were selected and published to public via the system. Volunteers online accepted the task and uploaded photographs, indicating land displacements. In a coastal city, Shenzhen of China, 135 task areas were selected and published in total, and 1742 useful photographs were uploaded. The uploaded photographs were then inspected to validate and analyze the InSAR results. Post-analysis found some high correlation between the uploaded photographs with the InSAR alerting displacements. The proposed system is a prototype, and its interface and functions can be further extended in the future toward an effective and efficient alerting system for risking land deformations.

Digital elevation models (DEMs) are vital in the geosciences and many other fields. Interferometric synthetic aperture radar (InSAR), an advanced earth observation technology, has shown its potential in DEM reconstruction. Multi baseline InSAR (MB-InSAR) is currently improving the precision of DEM reconstruction by combining multiple interferograms. However, MB-InSAR for DEM generation can result in severe decorrelation, which may cause significant gaps in the final DEM product. To solve this problem, an improved MB-InSAR DEM reconstruction method is proposed in this study, which we term as the dynamic DEM calculation algorithm. The proposed method can estimate the DEM pixel-by-pixel, which allowed us to select the interferograms dynamically and therefore minimize the void values. For the performance test of the proposed method, 25 ascending and 20 descending TerraSAR-X images over Heifangtai (China) were collected to form repeat-pass interferograms and produce the DEM using the proposed method. Results showed that the number of valid pixels increased by approximately 20% compared with the traditional MB-InSAR DEM reconstruction method without loss of precision, thereby illustrating the feasibility of the proposed method.

Airborne laser bathymetry (ALB) systems with digital full-waveform signal collection can obtain corresponding temporal positions from several backscattering surfaces by laser beam irradiation. This information can help describe the multi-elevation structures of the target and explore the echo signal attenuation response in different nonuniform mediums during laser propagation. Therefore, a full-waveform echo signal is quite practical for integrated water-land detection. However, the wavelength used in the ALB system is generally in the visible band range of 470~580 nm, and the received signal is constantly interfered with by many nontarget factors, such as imperfections in the receiving channel or the strong scattering from the transmission medium. The conventional processing method transforms nontarget interference into noise point cloud filtering or classification extraction, enabling the detection of a single surface or regular geometry. The accuracy of the identification and extraction for multi-elevation target surfaces echo signal is always reduced due to the significant noise signal intensity. We proposed a signal component detection method by constructing the echo signal feature functions and the conditional random field (CRF) model based on the full-waveform decomposition. The processing result for actual measurement data verified that the CRF strategy can effectively reduce the uncertainty of target surface detection. Compared with the single-beam echo sounder, the root mean square errors of the elevation deviation underwater were reduced by 3.2 cm and 4.9 cm respectively in the two different experimental areas.

In this paper we propose a new line segment detection framework based on a novel Pixel Orientation Estimation (POE) method, which detects line segments from binary edge maps and can be combined with any edge detectors. We show its efficiency by testing it in 1-look SAR images. The novel Pixel Orientation Estimation method estimates the orientation of each edge pixel by counting the number of edge pixels along a set of orientations. As the most edge pixels exist along the orientation of the line segment, the orientation of the edge pixel is given by the orientation that gives the maximum number of counts. Counting the number of edge pixels along different orientations is equivalent to convolving the local neighbourhood of an edge pixel with a set of carefully designed window functions with each window function corresponding to a fixed orientation. With the estimated orientations of pixels in the edge map, pixels can be grouped with a region growing step to form line support regions. Regions with their size larger than a size threshold will be accepted. Finally, rectangles are used to approximate the accepted regions and those rectangles are detected line segments. Experiments in both simulated SAR dataset and three 1-look Sentinel-1 images demonstrate the efficiency of the proposed method. In particular, we advance the state-of-the-art performances by 18 percent (F1-score) on the 1-look dataset simulated from YorkUrban-LineSegment Dataset.

Remotely sensed nighttime light (NTL) data provides an opportunity to observe human economic activities at night. However, existing NTL remote sensing data does not sufficiently reflect human activities at a fine resolution. In this study, we obtain a set of NTL data comprising volunteered passenger aircraft nighttime remote sensing (VPAN-RS) imagery by adopting a passenger plane as the remote sensing platform and using low-cost portable photography equipment as sensors. This method demonstrates the advantages of VPAN-RS data in high spatial resolution, frequent revisits, and low cost, which can supplement existing NTL data. The dataset acquired in this study covers 16 cities in China and one city in Japan, with a spatial resolution up to meter level. The data acquisition method and processing workflow are introduced in this article. Quality assessment shows that the reprojection error of the dataset falls within the range from 5–10 pixels, and the geometric error can be within 15 m. Via a comparison to reference data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor of the Suomi-National Polar-Orbiting Partnership (NPP) polar-orbiting satellite, we found that the VPAN-RS data provides more details of city lighting. Additionally, the radiance of the VPAN-RS data suitably fits that of monthly composite NPP/VIIRS data. We further outline an application example of VPAN-RS nighttime imagery, examine the unique challenges associated with VPAN-RS data with a focus on the influence of the image acquisition method on the data quality, and provide an outlook for the future of VPAN-RS data. We conclude that VPAN-RS NTL images are an effective data source for nighttime earth observations and have the potential for various applications.

The proper use of distributed scatterer (DS) can improve both the density and quality of synthetic aperture radar (SAR) interferometry (InSAR) measurements. A critical step in DS interferometry (DSI) is the restoration of a consistent phase series from SAR interferogram stacks. Most state-of-the-art algorithms adopt an approximate likelihood function to calculate the likelihood by replacing the true coherence matrix with its estimation, more specifically, the sample coherence matrix (SCM). However, this approximation has a drawback in that the coherence estimates are greatly biased when the coherence is low. In this study, we derive a new likelihood function without such an approximation. Accordingly, a DSI framework using this function for phase estimation and point selection is provided. In this framework, the new likelihood function serves as a cost function for phase estimation and a quality measure for DS selection. Its performance is investigated by experiments in a simulation study and a real-world case study using Sentinel-1 data over Shenzhen airport in China. The results reveal that the proposed DSI framework outperforms the existing state-of-the-art approaches in different scenarios, in terms of providing a more accurate estimation and improving DS density and coverage.

The lack of high-resolution digital elevation model (DEM) data presents one major limitation for deformation mapping using synthetic aperture radar interferometry (InSAR) techniques with high-spatial-resolution radar imagery (e.g., TerraSAR-X). This article presents a baseline linear combination (BLC) approach to generate interferograms with nearly zero baselines so as to minimize the effects of the uncertainties in the DEM used. It incorporates the baseline combination (BC) method with adjacent gradient networking to successfully unwrap the interferograms even in abruptly discontinuous areas, which in turn can be used to estimate a high-resolution DEM. The BLC approach does not require any deformation model; instead, it utilizes nearly zero-baseline interferograms to assist with 3-D phase unwrapping. Application of the BLC approach to the TerraSAR-X data set in Shenzhen, China, shows that the BLC-derived DEM agrees with the digital surface model (DSM) obtained from light detection and ranging (LiDAR) with a correlation coefficient of 0.998 and a root-mean-square error (RMSE) of 2.05 m, demonstrating the effectiveness of the BLC approach. Note that the BLC approach is not only able to be employed in urban areas with high buildings but also in mountain areas with steep slopes.

Subsidence has been reported in land reclamation area of Shenzhen. In this study, time series InSAR technique is employed to determine the surface movements in Shenzhen in the period between 2015 and 2018 using ALOS-2 images. Subsidence can be observed in Qianhai, Houhai, and the area to the south of Bao'an International Airport. Subsidence detected are in land reclamation area. This study highlight the necessity of continuous displacement monitoring in Shenzhen.

Scale parameter selection is a key step in an object-based image analysis (OBIA) work. In existing works, the first step is the selection of optimal scale parameter, followed by feature description and later analysis. However, only low-level image features are used at this step, which are not directly related to the purpose of the application. To overcome the limitation, we propose a multiscale object-based image analysis framework, in which, the multiscale classification is performed first, and the optimal scale parameter is estimated using the multiscale classification results and training samples. The experiments have demonstrated the effectiveness of our approach in estimating optimal scale parameter for object-based landcover classification, and showed great potential in automatic analysis of high spatial resolution remote sensing images.

Interferometric synthetic aperture radar (InSAR) is one of the most popular geodetic techniques for studying earthquake-related crustal displacements. Satellite SAR signals interact with the ionosphere when they travel through it during the synthetic aperture time. The condition of the ionosphere and its variation can significantly affect spaceborne InSAR measurements. In this letter, we use the Advanced Land Observation Satellite Phase Array-Type L-band SAR data from the 2009 southern Sumatra earthquake to evaluate the effects of the ionospheric artifacts on the slip distribution inversion of earthquake. The split-spectrum method is used to estimate and correct the ionospheric artifacts in the InSAR results. This letter shows that the long-wavelength ionospheric artifacts in the coseismic interferograms can be effectively mitigated. The slip distribution of the earthquake derived from the interferograms corrected for the ionospheric artifacts is presented. The slip distribution pattern and the magnitude of the slip are significantly refined after correcting the ionospheric artifacts.

Coherence loss is a critical issue in interferometric synthetic aperture radar geodesy, particularly when short-wavelength radar images are used to monitor earthquake deformation, and it may result in isolated fringes in an interferogram. The conventional unwrapping algorithms may incompletely unwrap or wrongly estimate the integer jumps between isolated fringes. In this paper, we propose a novel method to connect the isolated fringes in earthquake slip inversion. We use multiple starting points to unwrap the interferogram and then solve the integer ambiguities among the starting points by a dislocation-model-based integer least squares estimator. This estimator allows us to provide a quantitative evaluation of the reliability of the integer solutions in terms of two indicators (the success rate and residual ratio). The algorithm is robust to a certain degree of data noise and fault geometry error, as tested. Simulated experiments and case studies demonstrate that the proposed method can give better unwrapping results than the conventional approaches such as the minimum-cost flow (MCF), statistical-cost network-flow algorithm for phase unwrapping (SNAPHU), and iterative forms of MCF and SNAPHU with the assistance of a slip model. The earthquake slip inversion therefore benefits from the more accurate unwrapping results.

Downsampling is a routine step before applying interferometric synthetic aperture radar (InSAR) data to earthquake inversion because of the high computational burden. In this letter, we make use of the matrix perturbation theory to describe the downsampling process, which is considered as matrix perturbation on inversion equation. First, we derive a formula to quantitatively assess the perturbation on the inversion solution caused by data downsampling. Next, we propose an equation-based InSAR data downsampling algorithm to better reduce the perturbation. The experiment with simulated data demonstrates that our new algorithm preserves the most details from full data inversion comparing with previous algorithms. Finally, we use our method to study the slip distribution of the 2008 Mw 6.3 Dangxiong earthquake.

A novel coherence estimation method for small data sets is presented for interferometric synthetic aperture radar (SAR) (InSAR) data processing and geoscience applications. The method selects homogeneous pixels in both the spatial and temporal spaces by means of local and nonlocal adaptive techniques. Reliable coherence estimation is carried out by using such pixels and by correcting the bias in the estimated coherence caused by the non-Gaussianity in high-resolution SAR scenes. As an example, the proposed method together with coherence decomposition is applied to extract the temporal decorrelation component over an area in Macao. The results show that the proposed algorithms work well over various types of land cover. Moreover, the coherence change with time can be more accurately detected compared to other conventional methods.