Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks

He, Yufang; Zhang, Guangzong; Kaufmann, Hermann; Xu, Guochang

doi:10.3390/rs13214468

Cited by 5 publications

(3 citation statements)

References 57 publications

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“…The SBAS-InSAR technique [ 21 , 22 , 23 , 24 ] performs deformation measurements by means of a small baseline differential interferometric atlas, which can reduce the influence of spatial de-correlation and terrain errors, and then applies the singular value decomposition (SVD) method based on the least-paradigm criterion of the deformation rate to obtain the deformation rate of coherent targets and their time series [ 25 ]. Its technological flow is shown in Figure 2 .…”

Section: Methodsmentioning

confidence: 99%

Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm–Convolutional Neural Network–Long Short-Term Memory Model

Chen,

Du,

Huang

et al. 2024

Sensors

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With the acceleration of urbanisation, urban areas are subject to the combined effects of the accumulation of various natural factors, such as changes in temperature leading to the thermal expansion or contraction of surface materials (rock, soil, etc.) and changes in precipitation and humidity leading to an increase in the self-weight of soil due to the infiltration of water along the cracks or pores in the ground. Therefore, the subsidence of urban areas has now become a serious geological disaster phenomenon. However, the use of traditional neural network prediction models has limitations when examining the causal relationships between time series surface deformation data and multiple influencing factors and when applying multiple influencing factors for predictive analyses. To this end, Sentinel-1A data from March 2017 to February 2023 were used as the data source in this paper, based on time series deformation data acquired using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. A sparrow search algorithm–convolutional neural network–long short-term memory (SSA-CNN-LSTM) neural network prediction model was built. The six factors of temperature, humidity, precipitation, and ground temperature at three different depths below the surface (5 cm, 10 cm, and 15 cm) were taken as the input of the model, and the surface deformation data were taken as the output of the neural network model. The correlation between the spatial and temporal evolution characteristics of the ground subsidence in urban areas and various influencing factors was analysed using grey correlation analysis, which proved that these six factors contribute to some extent to the deformation of the urban surface. The main urban area of Hohhot City, Inner Mongolia Autonomous Region, was used as the study area. In order to verify the efficacy of this neural network prediction model, the prediction effects of the multilayer perceptron (MLP), backpropagation (BP), and SSA-CNN-LSTM models were compared and analysed, with the values of the correlation coefficients of the feature points of A1, B1, and C1 being in the range of 0.92, 0.83, and 0.93, respectively. The results show that compared with the traditional MLP and BP neural network models, the SSA-CNN-LSTM model achieves a higher performance in predicting time series surface deformation data in urban areas, which provides new ideas and methods for this area of research.

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Section: Methodsmentioning

confidence: 99%

Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm–Convolutional Neural Network–Long Short-Term Memory Model

Chen,

Du,

Huang

et al. 2024

Sensors

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“…In summary, the SBAS-InSAR technique was chosen as the preferred method for surface deformation monitoring in the four towns of the southern region of Qijiang District. The specific operational workflow in the SBAS-InSAR technology is illustrated in Figure 2 [41][42][43].…”

Section: Identification Of Mountain Element: Sbas-insarmentioning

confidence: 99%

“…In summary, the SBAS-InSAR technique was cho as the preferred method for surface deformation monitoring in the four towns of southern region of Qijiang District. The specific operational workflow in the SBAS-InS technology is illustrated in Figure 2 [41][42][43]. In practice, to reduce temporal and spatial decorrelation, thresholds of 2% and days were set as the maximum spatial baseline and time baseline, respectively.…”

Section: Identification Of Mountain Element: Sbas-insarmentioning

confidence: 99%

The Comprehensive Management Zoning of Mountains, Rivers, Forests, and Farmlands Based on Element Recognition

Yu,

2023

Land

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Land subsidence, soil erosion, and landscape fragmentation frequently occur in regions of backward production capacity. Therefore, it is imperative to carry out efforts for ecological restoration in these mine-closed regions. The proposal of holistic conservation of mountains, rivers, forests, farmlands, lakes, grasslands, and deserts provides important guidance for ecological restoration. To support the governance of regional ecology, this paper aims to discuss the layout of element governance zoning in four southern towns of Qijiang District, Chongqing. SBAS-InSAR technology, vulnerability assessment, linear trend analysis, and suitability evaluation were used to divide the mountain, water, forest, and farmland management areas. Regional comprehensive management work was implemented according to the severity of each element’s existing ecological questions. Eventually, a comprehensive management pattern of mountains, rivers, forests, and farmlands can be obtained. The results show that the mountain management area is 8.03 km2 (3.24% of the total management area), the hydrological management area is 212.07 km2 (85.80% of the total management area), the forest management area is 7.04 km2 (2.84% of the total management area), and the farmland management area is 20.07 km2 (8.12% of the total management area). In light of the current circumstances, this study advocates for the implementation of three integrated governance approaches, with a focus on managing hydrological factors. These approaches include ridge-based mountain, water, and forest governance, valley-based mountain, water, and farmland governance, and undergrowth economy-based water, forest, and farmland governance. This study explores the spatial layout and priority of the governance areas from the perspective of elements, which provides a new development direction for the current research on the life community based on policy analysis.

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InSAR-based rapid damage assessment of urban building portfolios following the 2023 Turkey earthquake

Du,

Feng,

2024

International Journal of Disaster Risk Reduction

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Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks

Cited by 5 publications

References 57 publications

Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm–Convolutional Neural Network–Long Short-Term Memory Model

Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm–Convolutional Neural Network–Long Short-Term Memory Model

The Comprehensive Management Zoning of Mountains, Rivers, Forests, and Farmlands Based on Element Recognition

InSAR-based rapid damage assessment of urban building portfolios following the 2023 Turkey earthquake

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