Radar Images Research Articles

JPSSL: SAR Terrain Classification Based on Jigsaw Puzzles and FC-CRF

Effective features play an important role in synthetic aperture radar (SAR) image interpretation. However, since SAR images contain a variety of terrain types, it is not easy to extract effective features of different terrains from SAR images. Deep learning methods require a large amount of labeled data, but the difficulty of SAR image annotation limits the performance of deep learning models. SAR images have inevitable geometric distortion and coherence speckle noise, which makes it difficult to extract effective features from SAR images. If effective semantic context features cannot be learned for SAR images, the extracted features struggle to distinguish different terrain categories. Some existing terrain classification methods are very limited and can only be applied to some specified SAR images. To solve these problems, a jigsaw puzzle self-supervised learning (JPSSL) framework is proposed. The framework comprises a jigsaw puzzle pretext task and a terrain classification downstream task. In the pretext task, the information in the SAR image is learned by completing the SAR image jigsaw puzzle to extract effective features. The terrain classification downstream task is trained using only a small number of labeled data. Finally, fully connected conditional random field processing is performed to eliminate noise points and obtain a high-quality terrain classification result. Experimental results on three large-scene high-resolution SAR images confirm the effectiveness and generalization of our method. Compared with the supervised methods, the features learned in JPSSL are highly discriminative, and the JPSSL achieves good classification accuracy when using only a small amount of labeled data.

A Deep Learning Model with Axial Attention for Radar Echo Extrapolation

ABSTRACT Radar echo extrapolation is an important approach in precipitation nowcasting which utilizes historical radar echo images to predict future echo images. In this paper, we introduce the self-attention mechanism into Trajectory Gated Recurrent Unit (TrajGRU) model. Under the sequence-to-sequence framework, we have developed a novel convolutional recurrent neural network called Self-attention Trajectory Gated Recurrent Unit (SA-TrajGRU), which incorporates the self-attention mechanism. The SA-TrajGRU model which combines spatiotemporal variant structure in TrajGRU and self-attention module is simple and effective. We evaluate our approach on the Moving MNIST-2 dataset and the CIKM AnalytiCup 2017 radar echo dataset. The experimental results show that the performance of the proposed SA-TrajGRU model is comparable to other convolutional recurrent neural network models. HSS and CSI scores of the SA-TrajGRU model are higher than scores of other models under the radar echo threshold of 25 dBZ, indicating that the SA-TrajGRU model has the most accurate prediction results under this threshold.

Where is my attention? An explainable AI exploration in water detection from SAR imagery

Attention mechanisms have found extensive application in Deep Neural Networks (DNNs), with numerous experiments over time showcasing their efficacy in improving the overall performance of DNNs. Considering the black-box nature of DNNs, it is unclear how attention mechanisms affect model decision-making processes. Therefore, it is of great significance to explore the internal relationship between the focused area of attention mechanisms and the prediction results. For the first time, an explainable framework for the attention mechanism of Synthetic Aperture Radar (SAR) image analytics is proposed, integrating DNN and eXplainable Artificial Intelligence (XAI), which realizes the effective explanation for attention mechanisms across different locations. The framework consists of three parts: the water extraction network which contains the attention mechanism at different locations; the proposed Attention- Gradient Class Activation Mapping (A-GCAM) method for attribution analysis of attention mechanisms; and we invent Category-specific channel Score of Confidence Mapping (CSCM) to perform geo-visualization for the output features of attention mechanisms. Experiments are conducted with the widely used Sentinel-1 system for water detection, in which three attention mechanisms with different characteristics are added to the encoder and decoder in DNNs. The results show that the framework can make the decision-making process of attention mechanisms transparent, thus improving their comprehensiveness and trustworthiness in various tasks and providing a reliable approach to selecting suitable attention mechanisms for the given task.

Radar and Optical Observations and Physical Modeling of Binary Near-Earth Asteroid 2018 EB

We report radar, photometric, and visible-wavelength spectrophotometry observations of NEA 2018 EB obtained in 2018. The radar campaign started at Goldstone (8560 MHz, 3.5 cm) on April 7, and it was followed by more extensive observations from October 5 to 9 by both Arecibo (2380 MHz, 12.6 cm) and Goldstone. 2018 EB was observed optically on April 5, 8, and 9 and again on October 18. Spectrophotometry was obtained on October 19 with the SOAR telescope, and the data suggest that 2018 EB is an Xk-class object. The echo power spectra and delay-Doppler radar images revealed that 2018 EB is a binary system. Radar images constrained the satellite's diameter to 0.15−0.05+0.02 km, but the data were not sufficient for shape modeling. Shape modeling of lightcurves and radar data yielded an oblate primary with an effective diameter D = 0.30 ± 0.04 km and a sidereal rotation period of 4.3−0.5+0.6 hr. Measurements of delay-Doppler separations between the centers of mass of the primary and the satellite, along with the timing of a radar eclipse observed on October 9, resulted in an orbit fit for the satellite with a semimajor axis of 0.50−0.01+0.04 km, an eccentricity of 0.15 ± 0.04, a period of 16.85−0.26+0.33 hr, and an orbit pole constrained to the ecliptic longitudes and latitudes of λ=93−43°+27° and β=48−18°+7° . The system mass was estimated to be 2.03−0.08+0.52×1010 kg, which yielded a bulk density of 1.4−0.5+0.6 g cm−3. Our analysis suggests that 2018 EB has a low optical albedo of p V = 0.028 ± 0.016 and a relatively high radar albedo of η OC = 0.29 ± 0.11 at Arecibo and η = 0.22 ± 0.10 at Goldstone.

Waveform design of radar coincidence imaging radiation field based on image entropy

AbstractRadar coincidence imaging (RCI) is a high‐resolution radar imaging mode which constructs a temporal spatial stochastic radiation field (TSSRF) and uses the correlation between reference signal and echoes for imaging. The correlation between the reference matrix and the echoes is the main factor affecting the imaging performance. In fact, radar target characteristics cause fluctuations in the radar cross section and variations in the scattering intensity of each resolution element. The latter degrades the correlation between the reference matrix and the echoes, seriously affecting the image reconstruction. This paper designs the waveform based on image entropy under fixed transmitting and receiving arrays. The targets with fluctuating scattering intensity at each resolution element are statistically modelled. Simulation results show this approach can improve the imaging performance and reduce the energy dissipation degree of the target grid.

Unlock insights from hydrographic data with GeoAI

Hydrographic offices are collecting hundreds of terabytes of data every day. This information not only comes from bathymetry data sensors, but also from weather stations, radar, ships, satellites, aerial and drone imagery, and other sensors. With all this data pouring in, hydrographic offices need to be able to automate time consuming processes and adopt modern technologies. One such technology is GeoAI, the intersection of spatial data and artificial intelligence. GeoAI can be considered an enabling technology, in that it allows you collect the data once and apply different algorithms to the data for it to be for multiple purposes. Data collected from multibeam echo sounders can be analyzed to update ENCs by finding new obstructions such as rocks and shipwrecks. Using that same point cloud, GeoAI can then be used to understand marine animal habitat by identifying underwater structures and seafloor patterns that lead to increased biodiversity. GeoAI can be used to aid in coastal resilience projects by analyzing aerial imagery from drones captured in multiple seasons and years for change detection, highlight the areas that need the most attention. Machine Learning, a part of the GeoAI portfolio, can additionally use that same imagery data set to run predictive analytics, highlighting areas that are susceptible to erosion, flooding, and landslides. Many of the same GeoAI algorithms can be used to help maximize investments in the blue economy by bringing location intelligence to the decision-making process. Models can predict the best locations to establish aquaculture, Marine Protected Areas, and offshore energy production. In addition to its applications in coastal resilience projects and maximizing investments in the blue economy, GeoAI offers a wide array of benefits in the domain of hydrospatial data management. The integration of GeoAI in hydrographic offices revolutionizes the way hydrographic data is processed and utilized. Traditionally, processing and interpreting vast amounts of hydrospatial data, including bathymetry, weather, radar, and imagery, required extensive human resources and time-consuming manual efforts. However, with GeoAI, these offices can automate complex tasks and streamline data analysis, significantly improving efficiency and accuracy.

SAR Image Fusion Classification Based on Improved D-S Evidence Theory

Applications for classifying Synthetic Aperture Radar (SAR) images are critical to environmental monitoring, urban planning, and land resource surveying. Fusion approaches work well for increasing SAR image categorization accuracy. However, effective processing of uncertainty information is frequently overlooked by conventional fusion classification systems. This paper presented a fusion classification approach for SAR images based on improved Dempster-Shafer (D-S) evidence theory, taking into account the uncertainty and possible conflicts in the classifiers’ output. First, after adaptively identifying the optimal hyperparameters of models, the SAR data is identified using Gradient Boosting Machine (GBM), Multilayer Perceptron (MLP), and Random Forest (RF). Different weights are then allocated in accordance with the variations in classifier performance. Ultimately, enhanced D-S evidence theory is used to integrate the output of these classifiers. In addition, uncertainty in classification results is also modeled and visualized. Experiments with Flevoland and Oberpfaffenhofen datasets show that the accuracy of this method is 85.21% and 91.88% respectively, outperforming both the soft voting ensemble classifier (SVE) and D-S evidence theory fusion method. This approach enhances the comprehension and capacity to manage uncertainty in the model output in addition to increasing the accuracy and reliability of the classification of SAR data.

Electromagnetic scattering and imaging simulation of extremely large-scale sea-ship scene based on GPU parallel technology

Aiming to solve the bottleneck problem of electromagnetic scattering simulation in the scenes of extremely large-scale seas and ships, a high-frequency method by using graphics processing unit (GPU) parallel acceleration technique is proposed. For the implementation of different electromagnetic methods of physical optics (PO), shooting and bouncing ray (SBR) and physical theory of diffraction (PTD), a parallel computing scheme based on the central processing unit (CPU)-GPU parallel computing scheme is realized to balance computing tasks. Finally, a multi-GPU framework is further proposed to solve the computational difficulty caused by the massive number of ray tubes in the ray tracing process. By using the established simulation platform, signals of ships at different seas are simulated and their images are achieved as well. It is shown that the higher sea states degrade the averaged peak signal-to-noise ratio (PSNR) of radar image.

Multichannel Repeater for Coherent Radar Networks Enabling High-Resolution Radar Imaging

Multichannel Repeater for Coherent Radar Networks Enabling High-Resolution Radar Imaging

Terminal Trajectory Planning for Synthetic Aperture Radar Imaging Guidance Based on Chronological Iterative Search Framework.

Synthetic aperture radar (SAR) is capable of obtaining the high-resolution 2-D image of the interested target scene, which enables advanced remote sensing and military applications, such as missile terminal guidance. In this article, the terminal trajectory planning for SAR imaging guidance is first investigated. It is found that the guidance performance of an attack platform is determined by the adopted terminal trajectory. Therefore, the aim of the terminal trajectory planning is to generate a set of feasible flight paths to guide the attack platform toward the target and meanwhile obtain the optimized SAR imaging performance for enhanced guidance precision. The trajectory planning is then modeled as a constrained multiobjective optimization problem given a high-dimensional search space, where the trajectory control and SAR imaging performance are comprehensively considered. By utilizing the temporal-order-dependent property of the trajectory planning problem, a chronological iterative search framework (CISF) is proposed. The problem is decomposed into a series of subproblems, where the search space, objective functions, and constraints are reformulated in chronological order. The difficulty of solving the trajectory planning problem is thus significantly alleviated. Then, the search strategy of CISF is devised to solve the subproblems successively. The optimization results of the preceding subproblem can be utilized as the initial input of the subsequent subproblems to enhance the convergence and search performance. Finally, a trajectory planning method is put forward based on CISF. Experimental studies demonstrate the effectiveness and superiority of the proposed CISF compared with the state-of-the-art multiobjective evolutionary methods. The proposed trajectory planning method can generate a set of feasible terminal trajectories with optimized mission performance.

Iterative Adaptive Based Multi-Polarimetric SAR Tomography of the Forested Areas

Synthetic aperture radar tomography (TomoSAR) is an extension of synthetic aperture radar (SAR) imaging. It introduces the synthetic aperture principle into the elevation direction to achieve three-dimensional (3-D) reconstruction of the observed target. Compressive sensing (CS) is a favorable technology for sparse elevation recovery. However, for the non-sparse elevation distribution of the forested areas, if CS is selected to reconstruct it, it is necessary to utilize some orthogonal bases to first represent the elevation reflectivity sparsely. The iterative adaptive approach (IAA) is a non-parametric algorithm that enables super-resolution reconstruction with minimal snapshots, eliminates the need for hyperparameter optimization, and requires fewer iterations. This paper introduces IAA to tomographicinversion of the forested areas and proposes a novel multi-polarimetric-channel joint 3-D imaging method. The proposed method relies on the characteristics of the consistent support of the elevation distribution of different polarimetric channels and uses the L2-norm to constrain the IAA-based 3-D reconstruction of each polarimetric channel. Compared with typical spectral estimation (SE)-based algorithms, the proposed method suppresses the elevation sidelobes and ambiguity and, hence, improves the quality of the recovered 3-D image. Compared with the wavelet-based CS algorithm, it reduces computational cost and avoids the influence of orthogonal basis selection. In addition, in comparison to the IAA, it demonstrates greater accuracy in identifying the support of the elevation distribution in forested areas. Experimental results based on BioSAR 2008 data are used to validate the proposed method.

Full‐scale measurements of thunderstorm outflows in the Northern Mediterranean

AbstractDownbursts are severe wind systems originating from thunderstorm clouds, and their strong horizontal outflows can pose serious hazards to natural and built environments. In the context of the activities of the European project THUNDERR—Detection, simulation, modelling and loading of thunderstorm outflows to design wind‐safer and cost‐efficient structures—a comprehensive database of full‐scale downburst measurements was built. All records were acquired by bi‐ or tri‐axial ultrasonic anemometers installed in the main ports of the High Tyrrhenian Sea, namely Genova, Livorno and La Spezia, within the European projects ‘Wind and Ports’ and ‘Wind, Ports and Sea’. The very limited space and time structure of downburst outflows makes the available records in nature inadequate for developing models that could be used in the atmospheric science and engineering communities. The database described herein represents a step forward in attempting to fill this gap. The downburst nature of all events contained in the dataset was verified through detailed meteorological analyses, including comparisons with radar and satellite images and lightning recordings. The wind speed records associated with the events detected by the anemometric network are made publicly available through the online repository Zenodo and can be reused for multiple purposes. The dataset is expected to convey an important impulse towards the physical characterization and modelling of downburst winds and their codification into design tools for the assessment of wind loading and its effects on structures and infrastructure. Furthermore, it could serve as a promising, essential tool for researchers and risk‐related insurance companies.

HyBNN: Quantifying and Optimizing Hardware Efficiency of Binary Neural Networks

Binary neural network (BNN), where both the weight and the activation values are represented with one bit, provides an attractive alternative to deploy highly efficient deep learning inference on resource-constrained edge devices. However, our investigation reveals that, to achieve satisfactory accuracy gains, state-of-the-art (SOTA) BNNs, such as FracBNN and ReActNet, usually have to incorporate various auxiliary floating-point components and increase the model size, which in turn degrades the hardware performance efficiency. In this article, we aim to quantify such hardware inefficiency in SOTA BNNs and further mitigate it with negligible accuracy loss. First, we observe that the auxiliary floating-point (AFP) components consume an average of 93% DSPs, 46% LUTs, and 62% FFs, among the entire BNN accelerator resource utilization. To mitigate such overhead, we propose a novel algorithm-hardware co-design, called FuseBNN , to fuse those AFP operators without hurting the accuracy. On average, FuseBNN reduces AFP resource utilization to 59% DSPs, 13% LUTs, and 16% FFs. Second, SOTA BNNs often use the compact MobileNetV1 as the backbone network but have to replace the lightweight 3 × 3 depth-wise convolution (DWC) with the 3 × 3 standard convolution (SC, e.g., in ReActNet and our ReActNet-adapted BaseBNN) or even more complex fractional 3 × 3 SC (e.g., in FracBNN) to bridge the accuracy gap. As a result, the model parameter size is significantly increased and becomes 2.25× larger than that of the 4-bit direct quantization with the original DWC (4-Bit-Net); the number of multiply-accumulate operations is also significantly increased so that the overall LUT resource usage of BaseBNN is almost the same as that of 4-Bit-Net. To address this issue, we propose HyBNN , where we binarize depth-wise separation convolution (DSC) blocks for the first time to decrease the model size and incorporate 4-bit DSC blocks to compensate for the accuracy loss. For the ship detection task in synthetic aperture radar imagery on the AMD-Xilinx ZCU102 FPGA, HyBNN achieves a detection accuracy of 94.8% and a detection speed of 615 frames per second (FPS), which is 6.8× faster than FuseBNN+ (94.9% accuracy) and 2.7× faster than 4-Bit-Net (95.9% accuracy). For image classification on the CIFAR-10 dataset on the AMD-Xilinx Ultra96-V2 FPGA, HyBNN achieves 1.5× speedup and 0.7% better accuracy over SOTA FracBNN.

FEMSFNet: Feature Enhancement and Multi-Scales Fusion Network for SAR Aircraft Detection

Aircraft targets, as high-value subjects, are a focal point in Synthetic Aperture Radar (SAR) image interpretation. To tackle challenges like limited SAR aircraft datasets and shortcomings in existing detection algorithms (complexity, poor performance, weak generalization), we present the Feature Enhancement and Multi-Scales Fusion Network (FEMSFNet) for SAR aircraft detection. FEMSFNet employs diverse image augmentation and integrates optimized Squeeze-and-Excitation Networks (SE) with residual network (ResNet) in a SdE-Resblock structure for a lightweight yet accurate model. It introduces ssppf-CSP module, an improved pyramid pooling model, to prevent receptive field deviation in deep network training. Tailored for SAR aircraft detection, FEMSFNet optimizes loss functions, emphasizing both speed and accuracy. Evaluation on the SAR Aircraft Detection Dataset (SADD) demonstrates significant improvements compared to the contrasted algorithms: precision rate (92%), recall rate (96%), and F1 score (94%), with a maximum increase of 12.2% in precision, 12.9% in recall, and 13.3% in F1 score.

Accuracy Assessment of Geometric-Distortion Identification Methods for Sentinel-1 Synthetic Aperture Radar Imagery in Highland Mountainous Regions.

SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods-LSM, R-Index, and P-NG-against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings.

RadarTCN: Lightweight Online Classification Network for Automotive Radar Targets Based on TCN.

Automotive radar is one of the key sensors for intelligent driving. Radar image sequences contain abundant spatial and temporal information, enabling target classification. For existing radar spatiotemporal classifiers, multi-view radar images are usually employed to enhance the information of the target and 3D convolution is employed for spatiotemporal feature extraction. These models consume significant hardware resources and are not applicable to real-time applications. In this paper, RadarTCN, a novel lightweight network, is proposed that achieves high-accuracy online target classification using single-view radar image sequences only. In RadarTCN, 2D convolution and 3D-TCN are employed to extract spatiotemporal features sequentially. To reduce data dimensionality and computational complexity, a multi-layer max pooling down-sampling method is designed in a 2D convolution module. Meanwhile, the 3D-TCN module is improved through residual pruning and causal convolution is introduced for leveraging the performance of online target classification. The experimental results demonstrate that RadarTCN can achieve high-precision online target recognition for both range-angle and range-Doppler map sequences. Compared to the reference models on the CARRADA dataset, RadarTCN exhibits better classification performance, with fewer parameters and lower computational complexity.

Towards real-time processing for UAV-mounted GPR-SAR imaging systems

Ground Penetrating Radar (GPR) systems mounted on Unmanned Aerial Vehicles (UAVs) are becoming a promising solution to inspect beneath the soil surface. In particular, resorting to Synthetic Aperture Radar (SAR) techniques, these systems are able to retrieve subsurface radar images, in which buried targets can be detected. However, one of the major bottlenecks of this technology is the significant amount of time required for computing the radar images (due to the usage of computationally expensive algorithms to handle non-regular acquisition domains). To face this challenge, this work proposes an efficient interpolation approach, combining a height shift operation with a 2D interpolation, that enables the adoption of a migration algorithm called Phase Shift Migration (PSM). As a result, the processing time is drastically improved, offering close to real-time operation. The proposed methodology has been validated in a realistic scenario for detecting buried explosive targets, comparing it with conventional backprojection techniques in terms of image quality and processing time.

Satellite Image Analysis Approach for Identifying Flood Impacts in DKI Jakarta

Jakarta, as the capital city of Indonesia, is one of the major cities in the world that is inevitably affected by disasters resulting from climate change. The topographical characteristics of Jakarta, situated in a low-lying area, make it susceptible to floods during the rainy season. In 2020, Jakarta experienced a rainfall of 377 mm/day, marking the highest precipitation during the period of 1866 to 2020. To mitigate the impact of flood disasters, the availability of flood distribution data is crucial. The process of identifying flood distribution utilizes Sentinel-1 radar satellite imagery with the Normalized Difference Sigma-Naught Index (NDSI) method. NDSI is sensitive to open water bodies due to changes in land surface properties during floods. Based on NDSI analysis, the flood distribution in DKI Jakarta was identified on January 2, 2020, covering 17,38% of the total area of DKI Jakarta, which is 661,52 km2.

Airborne Platform Three-Dimensional Positioning Method Based on Interferometric Synthetic Aperture Radar Interferogram Matching

As the demand for precise navigation of aircraft increases in modern society, researching high-precision, high-autonomy navigation systems is both theoretically valuable and practically significant. Because the inertial navigation system (INS) has systematic and random errors, its output information diverges. Therefore, it is necessary to combine them with other navigation systems for real-time compensation and correction of these errors. The SAR matching positioning and navigation system uses synthetic aperture radar (SAR) image matching for platform positioning and compensates for the drift caused by errors in the inertial measurement unit (IMU). Images obtained by SAR are matched with digital landmark data, and the platform’s position is calculated based on the SAR imaging geometry. However, SAR matching positioning faces challenges due to seasonal variations in SAR images, the need for typical landmarks for matching, and the lack of elevation information in two-dimensional SAR image matching. This paper proposes an airborne platform positioning method based on interferometric SAR (InSAR) interferogram matching. InSAR interferograms contain terrain elevation information, are less affected by seasonal changes, and provide higher positioning accuracy and robustness. By matching real-time InSAR-processed interferograms with simulated interferograms using a digital elevation model (DEM), three-dimensional position information about the matching points has been obtained. Subsequently, a three-dimensional positioning model for the platform has bene established using the unit line-of-sight vector decomposition method. In actual flight experiments using an FMCW Ku-band Interferometric SAR system, the proposed platform positioning framework demonstrated its ability to achieve precise positioning in the absence of signals from the global navigation satellite system (GNSS).

Spherical‐Harmonic Distribution Analysis of Coronae in Relation to Volcanic Features on Venus

AbstractVenus boasts an abundance of volcanoes and volcano‐like structures. Synthetic aperture radar images of the surface have revealed extensive evidence of volcanism, including lava flows and edifices. Volcanic activity is further supported by crater statistics, and analysis of topography and gravity data. Unique to Venus, coronae are quasi‐circular volcano‐tectonic features exhibiting diverse volcanic characteristics. Despite this, volcanism is often under‐represented in formation models. We identify a new subset of coronae that display topographic changes subsequent to the emplacement of lava flows within their fracture annuli, pointing to the critical role of volcanic and magmatic processes in the formation of these coronae. Through spherical‐harmonic distribution analysis, we find that this new subset is spatially related to the full coronae database, pointing to an intrinsic process of coronae formation. Furthermore, coronae exhibit strong correlations and similar spectral shapes at low spherical harmonic degrees with large volcanoes, suggesting a shared geodynamic origin. Our findings underscore the pivotal role of volcanism in coronae formation and highlight the need for future research to integrate magmatic and volcanic processes more comprehensively into geophysical models. Such models would better capture the complex interactions between volcanic emplacement, magmatic activity, and lithospheric dynamics on Venus.