Research

17 pages, 5546 KiB

Open AccessArticle

Focal Mechanism and Regional Fault Activity Analysis of 2022 Luding Strong Earthquake Constraint by InSAR and Its Inversion

by Wenshu Peng, Xuri Huang and Zegen Wang

Remote Sens. 2023, 15(15), 3753; https://doi.org/10.3390/rs15153753 - 28 Jul 2023

Cited by 2 | Viewed by 1045

Abstract

On 5 September 2022, an Ms6.8 magnitude earthquake occurred in Luding County, Sichuan Province, China. Based on Sentinel-1 SAR images, this paper uses the D-InSAR approach to obtain the displacement field of the earthquake, invert the coseismic sliding distribution, and then calculate the [...] Read more.

On 5 September 2022, an Ms6.8 magnitude earthquake occurred in Luding County, Sichuan Province, China. Based on Sentinel-1 SAR images, this paper uses the D-InSAR approach to obtain the displacement field of the earthquake, invert the coseismic sliding distribution, and then calculate the static coulomb stress changes of the coseismic deformation on the aftershock distribution and surrounding faults. Further, the seismic structure is analyzed and discussed. The InSAR coseismic deformation field demonstrates that the maximum LoS displacement of the surface deformation caused by the Luding earthquake is about 15 cm. The Luding Ms 6.8 earthquake is dominated by the Moxi fault, which is a left-lateral strike-slip fault that ruptures along the NNW-SSE trend at about 160.3°, and the dip is 81°. The fault depth is mainly 5~15 km, the maximum sliding amount is about 174.8 cm, and the corresponding depth is 8.5 km. The seismic moment tensor obtained by inversion is 1.06 × 10¹⁹ Nm, Mw = 6.65. The Coulomb stress generated by the Luding earthquake on the northern end of the Anninghe fault zone exceeded the trigger threshold. The risk of the Anninghe fault’s future earthquake was greater, and continuous monitoring and risk assessment were required. Full article

(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)

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37 pages, 11449 KiB

Open AccessArticle

Polarimetric L-Band ALOS2-PALSAR2 for Discontinuous Permafrost Mapping in Peatland Regions

by Ridha Touzi, Steven M. Pawley, Paul Wilson, Xianfeng Jiao, Mehdi Hosseini and Masanobu Shimada

Remote Sens. 2023, 15(9), 2312; https://doi.org/10.3390/rs15092312 - 27 Apr 2023

Cited by 3 | Viewed by 1776

Abstract

Recently, it has been shown that the long penetrating polarimetric L-band ALOS is very promising for boreal and subarctic peatland mapping and monitoring. The unique information provided by the Touzi decomposition, and the dominant-scattering-type phase in particular, on peatland subsurface water flow permits [...] Read more.

Recently, it has been shown that the long penetrating polarimetric L-band ALOS is very promising for boreal and subarctic peatland mapping and monitoring. The unique information provided by the Touzi decomposition, and the dominant-scattering-type phase in particular, on peatland subsurface water flow permits an enhanced discrimination of bogs from fens, two peatland classes that can hardly be discriminated using conventional optical remote sensing sensors and C-band polarimetric SAR. In this study, the dominant and medium-scattering phases generated by the Touzi decomposition are investigated for discontinuous permafrost mapping in peatland regions. Polarimetric ALOS2, LiDAR, and field data were collected in the middle of August 2014, at the maximum permafrost thaw conditions, over discontinuous permafrost distributed within wooded palsa bogs and peat plateaus near the Namur Lake (Northern Alberta). The ALOS2 image, which was miscellaneously calibrated with antenna cross talk (−33 dB) much higher than the actual ones, was recalibrated. This led to a reduction of the residual calibration error (down to −43 dB) and permitted a significant improvement of the dominant and medium-scattering-type phase (20

^{\circ}

to −30

^{\circ}

) over peatlands underlain by discontinuous permafrost. The Touzi decomposition, Cloude–Pottier

α

-H incoherent target scattering decomposition, and the HH-VV phase difference were investigated, in addition to the conventional multipolarization (HH, HV, and VV) channels, for discontinuous permafrost mapping using the recalibrated ALOS2 image. A LiDAR-based permafrost classification developed by the Alberta Geological Survey (AGS) was used in conjunction with the field data collected during the ALOS2 image acquisition for the validation of the results. It is shown that the dominant- and scattering-type phases are the only polarimetric parameters which can detect peatland subsurface discontinuous permafrost. The medium-scattering-type phase,

ϕ_{s 2}

, performs better than the dominant-scattering-type phase,

ϕ_{s 1}

, and permits a better detection of subsurface discontinuous permafrost in peatland regions.

ϕ_{s 2}

also allows for a better discrimination of areas underlain by permafrost from the nonpermafrost areas. The medium Huynen maximum polarization return (m

_{2}

) and the minimum degree of polarization (DoP), pmin, can be used to remove the scattering-type phase ambiguities that might occur in areas with deep permafrost (more than 50 cm in depth). The excellent performance of polarimetric PALSAR2 in term of NESZ (−37 dB) permits the demonstration of the very promising L-band long-penetration SAR capabilities for enhanced detection and mapping of relatively deep (up to 50 cm) discontinuous permafrost in peatland regions. Full article

(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)

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12 pages, 817 KiB

Open AccessCommunication

Design of Extensible Structured Interferometric Array Utilizing the “Coarray” Concept

by Qiang Wang, Cong Xue, Shurui Zhang, Renli Zhang and Weixing Sheng

Remote Sens. 2023, 15(7), 1943; https://doi.org/10.3390/rs15071943 - 05 Apr 2023

Viewed by 1333

Abstract

The optimum placement of receiving telescope antennas is a central topic for designing radio interferometric arrays, and this determines the performance of the obtained information. A variety of arrays are designed for different purposes, and they perform poorly in scalability. In this paper, [...] Read more.

The optimum placement of receiving telescope antennas is a central topic for designing radio interferometric arrays, and this determines the performance of the obtained information. A variety of arrays are designed for different purposes, and they perform poorly in scalability. In this paper, we consider a subclass of structured sparse arrays, namely nested arrays, and examine the important role of “coarray” in interferometric synthesis imaging, which is utilized to design nested array configurations for a complete uniform Fourier plane coverage in both supersynthesis and instantaneous modes. Both nested arrays and the theory of the coarray have rich research achievements, and we apply them to astronomy to design arrays with good scalability and imaging performance. Simulated celestial source image retrieval performance validates the effectiveness of nested interferometric arrays. Full article

(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)

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15 pages, 9363 KiB

Open AccessArticle

General Five-Component Scattering Power Decomposition with Unitary Transformation (G5U) of Coherency Matrix

by Rashmi Malik, Gulab Singh, Onkar Dikshit and Yoshio Yamaguchi

Remote Sens. 2023, 15(5), 1332; https://doi.org/10.3390/rs15051332 - 27 Feb 2023

Cited by 3 | Viewed by 1050

Abstract

The polarimetric synthetic aperture radar (PolSAR) provides us with a two-by-two scattering matrix data set. The ensemble averaged coherency matrix in an imaging window derived using a scattering matrix has all non-zero elements in its three-by-three matrix. It is a full 3 × [...] Read more.

The polarimetric synthetic aperture radar (PolSAR) provides us with a two-by-two scattering matrix data set. The ensemble averaged coherency matrix in an imaging window derived using a scattering matrix has all non-zero elements in its three-by-three matrix. It is a full 3 × 3 matrix that bears nine real-valued and independent polarimetric parameters inside. In the proposed decomposition method, G5U, we preprocess observed coherency matrix [T] by using two consecutive unitary transformations to become an ideal form for five-component decomposition. The transformation reduces nine parameters to seven, which is the best fit for five-component scattering model expansion. We can retrieve five powers corresponding to surface scattering, double bounce scattering, volume scattering, oriented dipole scattering, and compound dipole scattering, directly. These powers can be calculated easily and used to display superb polarimetric RBG images as never before, and are further applicable for polarimetric calibration, classification, validation, etc. Full article

(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)

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24 pages, 4992 KiB

Open AccessArticle

A Three-Dimensional Block Adjustment Method for Spaceborne InSAR Based on the Range-Doppler-Phase Model

by Rui Wang, Xiaolei Lv, Huiming Chai and Li Zhang

Remote Sens. 2023, 15(4), 1046; https://doi.org/10.3390/rs15041046 - 14 Feb 2023

Cited by 2 | Viewed by 1420

Abstract

The block adjustment method can correct systematic errors in the bistatic Synthetic Aperture Radar Interferometry (InSAR) satellite system and effectively improve the accuracy of the InSAR-generated Digital Elevation Model (DEM). Presently, non-parametric methods, which use the polynomial to model the systematic errors of [...] Read more.

The block adjustment method can correct systematic errors in the bistatic Synthetic Aperture Radar Interferometry (InSAR) satellite system and effectively improve the accuracy of the InSAR-generated Digital Elevation Model (DEM). Presently, non-parametric methods, which use the polynomial to model the systematic errors of InSAR-generated DEMs, are most frequently used in spaceborne InSAR-DEM adjustment. However, non-parametric methods are not directly related to the physical parameters in the InSAR imaging process. Given the issue, this paper conducts adjustments in the parameter domain and proposes a three-dimensional block adjustment method for spaceborne bistatic InSAR systems based on the Range-Doppler-Phase (RDP) model. First, we theoretically analyze the sensitivities of spatial baseline, azimuth time, and slant range to the RDP geolocation model and confirm the analysis method with a simulated geolocation result. Second, we use total differential and differential geometry theories to derive adjustment equations of available control data based on sensitivity analysis. Third, we put forward an iterative solution strategy to solve the corrections of parallel baseline, azimuth time, and slant range to improve the plane and elevation accuracies of InSAR-generated DEMs. We used 29 scenes of TanDEM-X Co-registered Single look Slant range Complex (CoSSC) data to conduct simulated and real data experiments. The simulated results show that the proposed method can improve the accuracies of baseline, range, and timing to 0.05 mm, 0.1 m, and 0.006 ms, respectively. In the real data experiment, the proposed method improves the plane and elevation accuracies to 4.14 m and 1.34 m, respectively, and effectively suppresses the fracture phenomenon in the DEM mosaic area. Full article

(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)

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Graphical abstract

19 pages, 10071 KiB

Open AccessEditor’s ChoiceArticle

Extension of Scattering Power Decomposition to Dual-Polarization Data for Tropical Forest Monitoring

by Ryu Sugimoto, Ryosuke Nakamura, Chiaki Tsutsumi and Yoshio Yamaguchi

Remote Sens. 2023, 15(3), 839; https://doi.org/10.3390/rs15030839 - 02 Feb 2023

Cited by 2 | Viewed by 2009

Abstract

A new scattering power decomposition method is developed for accurate tropical forest monitoring that utilizes data in dual-polarization mode instead of quad-polarization (POLSAR) data. This improves the forest classification accuracy and helps to realize rapid deforestation detection because dual-polarization data are more frequently [...] Read more.

A new scattering power decomposition method is developed for accurate tropical forest monitoring that utilizes data in dual-polarization mode instead of quad-polarization (POLSAR) data. This improves the forest classification accuracy and helps to realize rapid deforestation detection because dual-polarization data are more frequently acquired than POLSAR data. The proposed method involves constructing scattering power models for dual-polarization data considering the radar scattering scenario of tropical forests (i.e., ground scattering, volume scattering, and helix scattering). Then, a covariance matrix is created for dual-polarization data and is decomposed to obtain three scattering powers. We evaluated the proposed method by using simulated dual-polarization data for the Amazon, Southeast Asia, and Africa. The proposed method showed an excellent forest classification performance with both user’s accuracy and producer’s accuracy at >98% for window sizes greater than 7 × 14 pixels, regardless of the transmission polarization. It also showed a comparable deforestation detection performance to that obtained by POLSAR data analysis. Moreover, the proposed method showed better classification performance than vegetation indices and was found to be robust regardless of the transmission polarization. When applied to actual dual-polarization data from the Amazon, it provided accurate forest map and deforestation detection. The proposed method will serve tropical forest monitoring very effectively not only for future dual-polarization data but also for accumulated data that have not been fully utilized. Full article

(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)

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20 pages, 36553 KiB

Open AccessArticle

A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data

by Dodi Sudiana, Anugrah Indah Lestari, Indra Riyanto, Mia Rizkinia, Rahmat Arief, Anton Satria Prabuwono and Josaphat Tetuko Sri Sumantyo

Remote Sens. 2023, 15(3), 728; https://doi.org/10.3390/rs15030728 - 26 Jan 2023

Cited by 3 | Viewed by 2680

Abstract

Forest and land fires are disasters that greatly impact various sectors. Burned area identification is needed to control forest and land fires. Remote sensing is used as common technology for rapid burned area identification. However, there are not many studies related to the [...] Read more.

Forest and land fires are disasters that greatly impact various sectors. Burned area identification is needed to control forest and land fires. Remote sensing is used as common technology for rapid burned area identification. However, there are not many studies related to the combination of optical and synthetic aperture radar (SAR) remote sensing data for burned area detection. In addition, SAR remote sensing data has the advantage of being a technology that can be used in various weather conditions. This research aims to evaluate the burned area model using a hybrid of convolutional neural network (CNN) as a feature extractor and random forest (CNN-RF) as classifiers on Sentinel-1 and Sentinel-2 data. The experiment uses five test schemes: (1) using optical remote sensing data; (2) using SAR remote sensing data; (3) a combination of optical and SAR data with VH polarization only; (4) a combination of optical and SAR data with VV polarization only; and (5) a combination of optical and SAR data with dual VH and VV polarization. The research was also carried out on the CNN, RF, and neural network (NN) classifiers. On the basis of the overall accuracy on the part of the region of Pulang Pisau Regency and Kapuas Regency, Central Kalimantan, Indonesia, the CNN-RF method provided the best results in the tested schemes, with the highest overall accuracy reaching 97% using Satellite pour l’Observation de la Terre (SPOT) images as reference data. This shows the potential of the CNN-RF method to identify burned areas, mainly in increasing precision value. The estimated result of the burned area at the research site using a hybrid CNN-RF method is 48,824.59 hectares, and the accuracy is 90% compared with MCD64A1 burned area product data. Full article

(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)

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20 pages, 21897 KiB

Open AccessArticle

Assessment of L-Band SAOCOM InSAR Coherence and Its Comparison with C-Band: A Case Study over Managed Forests in Argentina

by Santiago Ariel Seppi, Carlos López-Martinez and Marisa Jacqueline Joseau

Remote Sens. 2022, 14(22), 5652; https://doi.org/10.3390/rs14225652 - 09 Nov 2022

Cited by 2 | Viewed by 2490

Abstract

The objective of this work is to analyze the behavior of short temporal baseline interferometric coherence in forested areas for L-band spaceborne SAR data. Hence, an exploratory assessment of the impacts of temporal and spatial baselines on coherence, with emphasis on how these [...] Read more.

The objective of this work is to analyze the behavior of short temporal baseline interferometric coherence in forested areas for L-band spaceborne SAR data. Hence, an exploratory assessment of the impacts of temporal and spatial baselines on coherence, with emphasis on how these effects vary between SAOCOM-1 L-band and Sentinel-1 C-band data is presented. The interferometric coherence is analyzed according to different imaging parameters. In the case of SAOCOM-1, the impacts of the variation of the incidence angle and the ascending and descending orbits over forested areas are also assessed. Finally, short-term 8-day interferometric coherence maps derived from SAOCOM-1 are especially addressed, since this is the first L-band spaceborne mission that allows us to acquire SAR images with such a short temporal span. The analysis is reported over two forest-production areas in Argentina, one of which is part of the most important region in terms of forest plantations at the national level. In the case of SAOCOM, interferometric configurations are characterized by a lack of control on the spatial baseline, so a zero-baseline orbital tube cannot be guaranteed. Nevertheless, this spatial baseline variability is crucial to exploit volume decorrelation for forest monitoring. The results from this exploratory analysis demonstrates that SAOCOM-1 short temporal baseline interferograms, 8 to 16 days, must be considered in order to mitigate temporal decorrelation effects and to be able to experiment with different spatial baseline configurations, in order to allow appropriate forest monitoring. Full article

(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)

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31 pages, 10548 KiB

Open AccessArticle

Offshore Bridge Detection in Polarimetric SAR Images Based on Water Network Construction Using Markov Tree

by Chun Liu, Jian Yang, Jianghong Ou and Dahua Fan

Remote Sens. 2022, 14(16), 3888; https://doi.org/10.3390/rs14163888 - 11 Aug 2022

Cited by 4 | Viewed by 1403

Abstract

It is difficult to detect bridges in synthetic aperture radar (SAR) images due to the inherent speckle noise of SAR images, the interference generated by strong coastal scatterers, and the diversity of bridge and coastal terrain morphologies. In this paper, we present a [...] Read more.

It is difficult to detect bridges in synthetic aperture radar (SAR) images due to the inherent speckle noise of SAR images, the interference generated by strong coastal scatterers, and the diversity of bridge and coastal terrain morphologies. In this paper, we present a two-step bridge detection method for polarimetric SAR imagery, in which the probability graph model of a Markov tree is used to build the water network, and bridges are detected by traversing the graph of the water network to determine all adjacent water branch pairs. In the step of the water network construction, candidate water branches are first extracted by using a region-based level set segmentation method. The water network is then built globally as a tree by connecting the extracted water branches based on the probabilistic graph model of a Markov tree, in which a node denotes a single branch and an edge denotes the connection of two adjacent branches. In the step of the bridge detection, all adjacent water branch pairs related to bridges are searched by traversing the constructed tree. Each bridge is finally detected by merging the two contours of the corresponding branch pair. Three polarimetric SAR data acquired by RADARSAT-2 covering Singapore and Lingshui, China, and by TerraSAR-X covering Singapore, are used for testing. The experimental results show that the detection rate, the false alarm rate, and the intersection over union (IoU) between the recognized bridge body and the ground truth are all improved by using the proposed method, compared to the method that constructs a water network based on water branches merging by contour distance. Full article

(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)

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Review

Jump to: Research, Other

48 pages, 26374 KiB

Open AccessReview

On the Interpretation of Synthetic Aperture Radar Images of Oceanic Phenomena: Past and Present

by Kazuo Ouchi and Takero Yoshida

Remote Sens. 2023, 15(5), 1329; https://doi.org/10.3390/rs15051329 - 27 Feb 2023

Cited by 2 | Viewed by 2533

Abstract

In 1978, the SEASAT satellite was launched, carrying the first civilian synthetic aperture radar (SAR). The mission was the monitoring of ocean: application to land was also studied. Despite its short operational time of 105 days, SEASAT-SAR provided a wealth of information on [...] Read more.

In 1978, the SEASAT satellite was launched, carrying the first civilian synthetic aperture radar (SAR). The mission was the monitoring of ocean: application to land was also studied. Despite its short operational time of 105 days, SEASAT-SAR provided a wealth of information on land and sea, and initiated many spaceborne SAR programs using not only the image intensity data, but also new technologies of interferometric SAR (InSAR) and polarimetric SAR (PolSAR). In recent years, artificial intelligence (AI), such as deep learning, has also attracted much attention. In the present article, a review is given on the imaging processes and analyses of oceanic data using SAR, InSAR, PolSAR data and AI. The selected oceanic phenomena described here include ocean waves, internal waves, oil slicks, currents, bathymetry, ship detection and classification, wind, aquaculture, and sea ice. Full article

(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)

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Other

Jump to: Research, Review

20 pages, 29608 KiB

Open AccessTechnical Note

Construction of a Database of Pi-SAR2 Observation Data by Calibration and Scattering Power Decomposition Using the ABCI

by Yuya Arima, Toshifumi Moriyama, Yoshio Yamaguchi, Ryosuke Nakamura, Chiaki Tsutsumi and Shoichiro Kojima

Remote Sens. 2023, 15(3), 849; https://doi.org/10.3390/rs15030849 - 02 Feb 2023

Cited by 1 | Viewed by 1500

Abstract

Pi-SAR2 is an airborne polarimetric synthetic aperture radar operated by the National Institute of Information and Communications Technology. The polarimetric observation data of Pi-SAR2 are very valuable because of its high resolution, but it cannot be used effectively because the data are not [...] Read more.

Pi-SAR2 is an airborne polarimetric synthetic aperture radar operated by the National Institute of Information and Communications Technology. The polarimetric observation data of Pi-SAR2 are very valuable because of its high resolution, but it cannot be used effectively because the data are not well calibrated with respect to elevation. Therefore, we have calibrated the data according to the observation conditions. The Pi-SAR2 observation data are very large due to its high resolution and require sufficient computational resources to be calibrated. We utilized the AI Bridging Cloud Infrastructure (ABCI), constructed and operated by the National Institute of Advanced Industrial Science and Technology, to calculate them. This paper reports on the calibration, scattering power decomposition, and orthorectification of the Pi-SAR2 observation data using the ABCI. Full article

(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)

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10 pages, 3112 KiB

Open AccessTechnical Note

Detection of Ships Cruising in the Azimuth Direction Using Spotlight SAR Images with a Deep Learning Method

by Takero Yoshida and Kazuo Ouchi

Remote Sens. 2022, 14(19), 4691; https://doi.org/10.3390/rs14194691 - 20 Sep 2022

Cited by 8 | Viewed by 1595

Abstract

Spotlight synthetic aperture radar (SAR) achieves a high azimuth resolution with long integration times. Meanwhile, the long integration times also cause defocused and smeared images of moving objects such as cruising ships This is a typical imaging mechanism for moving objects in Spotlight [...] Read more.

Spotlight synthetic aperture radar (SAR) achieves a high azimuth resolution with long integration times. Meanwhile, the long integration times also cause defocused and smeared images of moving objects such as cruising ships This is a typical imaging mechanism for moving objects in Spotlight SAR images. Conversely, ships can be classified as stationary or moving from the amount of smearing, and this classification method is, in general, based on manual observation. This paper proposes an automatic method for detecting cruising ships using deep learning known as the “You Only Look Once (YOLO) v5 model”, which is one of the frameworks of the YOLO family. In this study, ALOS-2/PALSAR-2 L-band Spotlight SAR images over the waters around the Miura Peninsula, Japan, were analyzed using the YOLO v5 model with a total of 53 ships’ images and compared with Automatic Identification System (AIS) data. The results showed a precision of approximately 0.85 and a recall rate of approximately 0.89 with an F-measure of 0.87. Thus, sufficiently high values were achieved in the automatic detection of moving ships using the deep learning method with the YOLO v5 model. As for false detections, images of breakwaters were classified as ships cruising in the azimuth direction. Further, range moving ships were found to be difficult to detect. From the present preliminary study, it was found that the YOLO v5 model is limited to ships cruising predominantly in the azimuth direction. Full article

(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)

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