Real-time Satellite Research Articles

Evaluating the damage of collapsed bridges using remote sensing technologies: Case study: Baltimore’s Francis Scott Key Bridge

Bridges are vital for linking communities and facilitating economic activity. However, in the face of disaster, like ship collisions pose a significant threat to bridge infrastructure, causing structural damage and potential safety hazards. Rapid and precise assessment of the damage is essential for effective emergency response and recovery operations. Remote sensing with near-real-time satellite imagery provides the disaster scenario. This paper presents a change detection using pre- and post-disaster satellite data for Baltimore’s Francis Scott Key Bridge to identify structural damage due to the collision of the ship with support pillars on 26 March 2024. Both optical and microwave satellite data were used from open data sources and analyzed based on geospatial techniques such as change detection and surface profiling. It is estimated that an 1100-meter span of bridge was affected due to this collision, which helped to estimate the damage and mobilize the rescue operations. It may need further validation from ground truth information. Hence, the current study emphasizes the potential of remote sensing satellite data to provide near-real-time impact on disaster analysis.

Design of an Integrated System for Spaceborne SAR Imaging and Data Transmission.

In response to the conflicting demands between real-time satellite communication and high-resolution synthetic aperture radar (SAR) imaging, we propose a method that aligns the data transmission rate with the imaging data volume. This approach balances SAR performance with the requirements for real-time data transmission. To meet the need for mobile user terminals to access real-time SAR imagery data of their surroundings without depending on large traditional ground data transmission stations, we developed an application system based on filter bank multicarrier offset quadrature amplitude modulation (FBMC-OQAM). To address the interference problem with SAR signals' transmission and reception, we developed a signal sequence based on spaceborne SAR echo and data transmission and reception. This system enables SAR and data transmission signals to share the same frequency band, radio frequency transmission system, and antenna, creating an integrated sensing and communication system. Simulation experiments showed that, compared to the equal power allocation scheme for subcarriers, the echo image signal-to-noise ratio (SNR) improved by 2.79 dB and the data transmission rate increased by 24.075 Mbps.

Evaluating the damage of collapsed bridges using remote sensing technologies: Case study: Baltimore’s Francis Scott Key Bridge

<p class="MsoNormal" style="line-height: 120%; layout-grid-mode: char; mso-layout-grid-align: none; margin: 12.0pt 0cm 6.0pt 147.4pt;"><span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-fareast-language: JA; mso-no-proof: no;">Bridges are vital for linking communities and facilitating economic activity. However, in the face of disaster, like ship collisions pose a significant threat to bridge infrastructure, causing structural damage and potential safety hazards. Rapid and precise assessment of the damage is aid for effective emergency response and recovery operations. Remote sensing with near real-time satellite imagery provides the disaster scenario. This paper presents a change detection using pre and post-disaster satellite data for Baltimore’s Francis Scott Key Bridge to identify structural damage due to the collision of the ship with support pillars on 26 March 2024. Both optical and microwave satellite data were used from open data sources and analyzed based on geospatial techniques such as change detection and surface profiling. It is estimated that an 1100 m span of bridge was affected due to this collision, which helped to estimate the damage and mobilize the rescue operations. It may need further validation from ground truth information. Hence, the current study emphasizes the potential of remote sensing satellite data to provide near-real-time impact on disaster analysis.</span></p>

GNSS Real-Time ZTD/PWV Retrieval Based on PPP with Broadcast Ephemerides

GNSS precise point positioning (PPP) plays an important role in retrieving atmospheric water vapor values and performing numerical weather prediction. However, traditional PPP relies on real-time orbits and clocks, which require continuous internet or satellite communication. Improved broadcast ephemerides of GNSSs offer new opportunities for PPP with broadcast ephemerides (BE-PPP) instead of using precise ephemeride products. Therefore, we investigated the feasibility of utilizing BE-PPP for retrieving zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) data. We processed the GPS/Galileo observations from 80 IGS stations during a 30-day experiment to retrieve ZTD values using both real-time PPP (RT-PPP) and BE-PPP solutions. Then, we processed observations from 20 EUREF Permanent GNSS Network (EPN) stations to retrieve PWV data. The IGS final tropospheric products were used to validate the ZTD, and radiosonde (RDS) and ERA5 data were used to validate the PWV. The results show that the real-time ZTD from BE-PPP agrees well with that from RT-PPP: the standard deviation (STD) of the ZTD is 1.07 cm when using BE-PPP and 0.6 cm when using RT-PPP. Furthermore, the STD of the PWV is 1.69 mm when using BE-PPP, and 0.96 mm when using RT-PPP, compared to the ERA5-PWV. Compared to the RDS-PWV, the STD is 3.09 mm when using BE-PPP and 1.39 mm when using RT-PPP. In conclusion, the real-time ZTD/PWV products obtained using the BE-PPP solution are consistent with those of RT-PPP and meet the requirements of NWP, so this method can be used as an effective complement to RT-PPP to expand its application potential.

A comprehensive study on PnP-based pipeline for pose estimation of noncooperative satellite

The monocular 6D pose estimation of a noncooperative satellite is essential in various on-orbit automatic operations. Nevertheless, the application of this technology still faces challenges, such as heavy model parameters and high latency. In this study, we comprehensively explored the critical components of satellite pose estimation, including landmark description, localisation, and the PnP algorithm, and we introduce a high-precision, real-time, and robust pipeline. Existing methods adopt external convex landmarks, such as 11 landmarks for the SPEED dataset, which are insufficient to describe the geometric structure of the satellite. This study adopted a wireframe-based landmark description, encompassing all visually prominent satellite joints connected directly into the satellite’s wireframe model. In addition, unlike most existing methods that rely on heatmap-based landmark localisation, which incurs high computational costs and quantisation errors, we adopt direct coordinate classification for satellite landmark localisation, which is lightweight, fast, and accurate. Experimental results on the SPEED dataset demonstrate that our method outperforms the current state-of-the-art technique, with an accuracy increase of 28% and parameter number decrease of 78%. In particular, our method is very lightweight and robust, and it runs in real-time, making it well-suited for on-orbit real-time satellite pose estimation.

Poverty Impacts of the Pakistan Flood 2022

AbstractIn the summer of 2022, devastating floods hit Pakistan with a profound negative impact on lives and livelihoods. Apart from the irreconcilable loss of life, household welfare was negatively impacted through the loss of household income, loss of assets, and rising food prices. Yet, in the absence of just-in-time household survey data, evaluating the welfare impact is challenging, particularly in terms of estimating monetary poverty. This paper demonstrates how real-time satellite data, in combination with administrative data and historical household survey data, can be used to model the impact of floods on Pakistani households. In the context of data and time constraints, we present a practical methodology to simulate the expected impact of a natural disaster on monetary poverty from an ex-ante perspective. Estimates show that, as a direct consequence of the floods, the national poverty rate would increase by 4.0 to 4.3 percentage points, pushing around 9 million people below the national poverty line. Moreover, a spatially disaggregated analysis showed that the magnitude of shocks varied across locations and households depending on the intensity of the flooding and household characteristics. These estimates, which informed the Post Disaster Needs Assessment and were later used to frame the Donor Conference for Pakistan, provide an example of rapid modeling in the absence of real-time household survey data.

A time-space varying distributed unit hydrograph (TS-DUH) for operational flash flood forecasting using publicly-available datasets

Although flash floods originate in upstream (headwater) catchments causing hazards locally, they often occur at short time scales and affect widely while relatively small size catchments respond to intense rainfall systems. Due to the heavy cost of computing and parameterization, high resolution spatially distributed hydrological models are not effectively deployed for global or large-scale flash flood forecasting. This research proposes a Time-Space varying Distributed Unit Hydrograph (TS-DUH) based on publicly-available data for efficient global scale flash flood forecasting. TS-DUH estimates the runoff travel time (and flow velocity) from each location to the catchment outlet based on topographic and hydroclimatic characteristics, while it delineates the runoff-drainage process by emphasizing the heterogeneous and dynamic runoff contribution corresponding to rainfall and soil moisture variations. The excess rainfall is estimated by two methods: the Soil Conservation Service’s (SCS) curve (CN-Runoff), and the widely used Global Flood Monitoring System (GFMS) which provides both long-term (2000-present) well-archived and real-time online global runoff (GFMS-Runoff) prediction based on a distributed hydrological model. TS-DUH is first validated based on 6,324 flash flood events from 281 small-to-medium-sized catchments broadly distributed across the CONUS, using “perfect” precipitation input (i.e., NLDAS-2 for this study). TS-DUH calibrated with GFMS-Runoff shows better performance than using CN-Runoff, with a probability of detection (POD) value of 0.9 for all the events and 71% of them having a KGE metric above 0.5. More importantly, with the real-time satellite rainfall-driven GFMS-Runoff, the long-term (2003–2020) TS-DUH simulation shows better performance than GFMS, indicating promising value in global flash flood prediction of the TS-DUH-integrated GFMS.

Performance Analysis of an Aerial Remote Sensing Platform Based on Real-Time Satellite Communication and Its Application in Natural Disaster Emergency Response

The frequency of natural disasters has increased recently, posing a huge threat to human society. Rapid, accurate, authentic, and comprehensive acquisition and transmission of disaster information are crucial in emergency response. In this paper, we propose a design scheme for an aerial remote sensing platform based on real-time satellite communication. This platform mainly includes a civilian heavy-duty unmanned aerial vehicle, ground observation system with the self-developed orthographic image stabilization device, wireless communication system with an airborne mobile communication device using Ku band, ground satellite information receiving station, and data processing and application analysis system. The image stabilization capability of the ground observation system and the communication capability of the wireless communication system were verified through ground and flight tests respectively. The results showed that the stability accuracy of the platform was better than the theoretical threshold, the system transmission rate was not less than 2 M bandwidth, the data packet loss rate was low, and the time delay was not more than 2 s. The images captured in the experiment were clear, with a resolution of less than 1cm and an overlap rate of more than 70%. These all results meet the emergency observation requirement, which indicates that the aerial remote sensing platform based on real-time satellite communication has great potential for application in natural disaster emergency response.

A Novel Method of 3D Lyapunov Guidance Vector Field to Avoid Intercepting Satellite Based on Reinforcement Learning

This paper proposes a new 3D Lyapunov guidance vector field(3D-LGV) avoidance strategy based on reinforcement learning for the satellite evasion and interception problem. Combining it with the interfered fluid dynamical system (IFDS) enables the satellite to evade and smoothly enter orbit according to the state of the intercepting satellite in real time. 3D-LGV provides an initial flow field approaching an elliptical orbit, while IFDS provides a perturbed flow field based on the intercepting satellite position. The combined potential field of the initial flow field and the disturbed flow field is the planned velocity direction of the satellite. As a decision-making layer, the proximal policy optimization (PPO) dynamically adjusts the perturbed flow field in the IFDS to increase the avoidance success rate in different scenarios. The experimental results show that, compared with the particle swarm optimization with rolling horizon control algorithm, the algorithm proposed in this paper has a shorter decision time and a higher avoidance success rate. At the same time, Monte Carlo simulation shows that the evasion success rate of the proposed algorithm reaches 98%.

SPATIAL TEMPORAL ANALYSIS OF LAND SURFACE TEMPERATURE CHANGES IN AMBON ISLAND FROM LANDSAT 8 IMAGE DATA USING GEOGLE EARTH ENGINE

This study aims to analyze land surface temperature changes on Ambon Island using Landsat 8 imagery data and Google Earth Engine. The research method involves the use of Ambon Island boundary data from the Geospatial Information Agency (BIG) as well as Landsat 8 Collection 2 Tier 1 and Real-Time satellite image data that have been calibrated to reflect the reflectance of the Earth's surface to the atmosphere. The analysis steps include temperature conversion from Kelvin to Celsius, land surface temperature classification, and data export to Arc GIS software. The results showed an increase in land surface temperature associated with urban development in Ambon City, highlighting the importance of sustainable urban planning and good resource management to mitigate the negative impacts of land surface temperature increase and support adaptation to climate change. Keywords: Ambon Island, Geogle Earth Engine, Land Surface Temperature

Intelligent system based on a satellite image detection algorithm and a fuzzy model for evaluating sugarcane crop quality by predicting uncertain climatic parameters

The increase in uncertain weather affects agriculture, impacting crop yield and quality, mainly due to the interaction of climatic variables such as temperature, wind speed, and humidity. In addition, soil erosion and nutrient loss are regional problems aggravated by inadequate agricultural practices in developing sugarcane agriculture. The present research proposes an Intelligent System based on a detection algorithm and a fuzzy model to estimate the quality of the sugarcane crop and the probability of the presence of pests and diseases through the prediction of uncertain variables. Wind speed, cloudiness, humidity, and thermal amplitude were considered variables of interest because parameters out of control of these variables generate a state of thermal stress, triggering pests and diseases that reduce crop quality and sugar production. This research uses geospatial information to simplify the exchange of information through a detection algorithm using real-time satellite images and a fuzzy model to estimate crop quality and prevent climate change-related problems. The variables humidity and cloudiness determine sugarcane quality as they are related to crop phenology and the probability that the crop will develop pests and diseases. In contrast, the intelligent system showed a correlation of over 93% for predicting the variables of interest.

Multi-Scale Window Spatiotemporal Attention Network for Subsurface Temperature Prediction and Reconstruction

In this study, we investigate the feasibility of using historical remote sensing data to predict the future three-dimensional subsurface ocean temperature structure. We also compare the performance differences between predictive models and real-time reconstruction models. Specifically, we propose a multi-scale residual spatiotemporal window ocean (MSWO) model based on a spatiotemporal attention mechanism, to predict changes in the subsurface ocean temperature structure over the next six months using satellite remote sensing data from the past 24 months. Our results indicate that predictions made using historical remote sensing data closely approximate those made using historical in situ data. This finding suggests that satellite remote sensing data can be used to predict future ocean structures without relying on valuable in situ measurements. Compared to future predictive models, real-time three-dimensional structure reconstruction models can learn more accurate inversion features from real-time satellite remote sensing data. This work provides a new perspective for the application of artificial intelligence in oceanography for ocean structure reconstruction.

Comprehensive assessment of five near-real-time satellite precipitation products in the Lower Yangtze River Basin and the Lixiahe region, China: Dual perspectives from time series and extreme events

Near real-time satellite precipitation products (NSPPs) have significant potential in meteorological hydrological monitoring and forecasting due to their timeliness. Previous evaluation-oriented studies have either focused on the uncertainty of time series or on the monitoring capability of extreme events. There is limited research that comprehensively considers both aspects to provide a systematic knowledge. Moreover, there is still insufficient understanding of the accuracy of hourly precipitation estimates. This study conducts a comprehensive evaluation between five NSPPs (IMERG Early, IMERG Late, GSMaP NRT, GSMaP NRT Gauge and PERSIANN CCS) from two main perspectives at hourly scale: an assessment of time series spanning from 2018 to 2022 over the Lower Yangtze River Basin and the Lixiahe region and an examination of the four extreme events over the Tai Lake Basin and the Chao Lake Basin. HP-metrics are proposed to evaluate bias in the location of extreme rainfall, rainy area, and precipitation amount over time, comprehensively analyzing performance in terms of extreme events. The results show that, for general and seasonal assessment, IMERG Late generally performed best with good statistical metrics, with the highest CSI (0.343), CC (0.445), KGE ‘(0.295) in annual results, and the best agreement in capturing convective rain diurnal curves (summer CC = 0.820). GSMaP showed a better detection capability especially in spring and summer, and also performed better in capturing frequency curves, but always showed large estimation errors. IMERG and GSMaP detected worse CC region close to the water body. And the effect of high terrain on GSMaP NRT is more obvious in winter. For the evaluation in detecting the extreme events, IMERG and GSMaP overestimated the area and intensity and had a certain ability to discriminate the location of extreme precipitation, based on the temporal variation of HP-metrics. The rapid expansion or contraction of the extent of the extreme precipitation region could degrade the detection capability of HP-π. The comprehensive understanding of NSPPs provides valuable insights for upgrading satellite retrieval algorithms and making informed selection for precipitation products in meteorological hydrological applications.

Real-Time Estimation of BDS-3 Satellite Clock Offset with Ambiguity Resolution Using B1C/B2a Signals

The third generation of the BeiDou navigation satellite system (BDS-3) can transmit five-frequency signals. The real-time satellite clock offset of BDS-3 is typically generated utilizing the B1I/B3I combination with the ambiguity-float solutions. By conducting the ambiguity resolution (AR), the reliability of the satellite clock offset can be improved. However, the performance of BDS-3 ambiguity-fixed real-time satellite clock offset with B1C/B2a signals remains unknown and unrevealed. In this contribution, the performance of the BDS-3 ambiguity-fixed satellite clock offset with the new B1C/B2a signals is investigated. One week of observation data from 85 stations was used to perform ambiguity-fixed satellite clock offset estimation. For B1I/B3I and B1C/B2a signals, the wide-lane (WL) uncalibrated phase delay (UPD) on the satellite end is fairly stable for one day, while the narrow-lane (NL) UPD standard deviation (STD) amounts to 0.122 and 0.081 cycles, respectively. The mean ambiguity fixing rate is 80.7% and 78.0% for these two signal combinations, and the time to first fix (TTFF) for the B1C/B2a signals is remarkably shorter than that of the B1I/B3I signals. The STDs of the ambiguity-float and -fixed satellite clock offsets are 0.033 and 0.026 ns, respectively, for the B1I/B3I combination, and it is reduced to 0.024 and 0.023 ns for B1C/B2a signals, respectively. Using the estimated UPD and clock offset products, the positioning performance of the kinematic Precise Point Positioning (PPP)-AR results amounts to 1.56, 1.23, and 4.46 cm in the east, north, and up directions for B1I/B3I signals, respectively. It is improved to 1.36, 1.16, and 4.25 cm using the products estimated with the B1C/B2a signals, with improvements of 12.8%, 5.7%, and 4.7% in three directions, respectively. The experiments showed that the performances of the ambiguity-fixed satellite clock offsets and the PPP-AR results using B1C/B2a signals are better than those of B1I/B3I.

Developing a flood monitoring system by utilizing real-time satellite rainfall estimates and water energy budget-based rainfall-runoff inundation model in West Africa

Abstract. The African regions have been affected by frequent flood-related disasters. To support decision-makers in flood disaster management, a real-time flood monitoring system was established in the Niger and Volta basins under the Water disaster platform to enhance climate resilience in Africa (WADiRE-Africa). To bridge a gap between data unavailability and inaccessibility, the near-real-time satellite rainfall data (GSMaP-NRT) was evaluated and found to have a very low correlation (<0.4) with gauge data at daily scales and a higher correlation (>0.7) at monthly scales. The statistically bias-corrected GSMaP-NRT data was used in the Water and Energy Budget-based Rainfall-Runoff-Inundation (WEB-RRI) model to generate hydrological responses of the basins. As the simulated river discharges were overestimated, an additional correction method was employed for the GSMaP-NRT data to match well with observed discharges. The prototype system provides real-time flood information in both basins.

Evaluation of satellite precipitation products for real-time extreme river flow modeling in data scarce regions

Abstract. Inadequacy of spatio-temporal hydro-climatic data limits the efficacy of hazard monitoring and disaster risk reduction activities in disaster-prone areas. Various satellite missions are recently providing climate data, but prior evaluation and enhancement of these data are necessary for a reliable application. In this study, we conducted performance evaluation and enhancement of three real-time satellite precipitation products (SPPs) (GSMaP, GPM-IMERG, and PERSIANN) for flood modeling in the Blue Nile basin. The bias correction improved the original SPPs, with the largest improvement being for factors generated from 10 d mean data. Flood event hydrograph indicated satisfactory results of error metrics on the devastating flood event of 2012. Employing reliable physical–based distributed hydrologic models provided longer lead time and high-accuracy flood simulation. Furthermore, the results indicate that integrating available initial observed precipitation data improved the efficiency of SPPs simulation, and hence are applicable in operational flood monitoring.

Performance Evaluation and Application Field Analysis of Precise Point Positioning Based on Different Real-Time Augmentation Information

The most commonly used real-time augmentation services in China are the International GNSS Service’s (IGS) real-time service (RTS), PPP-B2b service, and Double-Frequency Multi-Constellation (DFMC) service of the BeiDou Satellite-Based Augmentation System (BDSBAS) service. However, research on the performance evaluation, comparison, and application scope of these three products is still incomplete. This article introduces methods for obtaining real-time augmentation information and real-time orbit and clock offset recovery. Based on real-time orbit and clock offset accuracy, positioning accuracy, and positioning availability, this article systematically evaluates the performance and analyzes the application fields of Centre National d’Études Spatiales (CNES), PPP-B2b, and BDSBAS augmentation information. The results of the evaluation revealed that the radial accuracy of the CNES and PPP-B2b real-time orbit product is consistent, and the Root Mean Square (RMS) is better than 5 cm. The CNES real-time orbit product can achieve centimeter-level accuracy in both along-track and cross-track components, surpassing PPP-B2b’s decimeter-level accuracy. Both services demonstrate consistent accuracy in the real-time clock offset, with PPP-B2b showing similar standard deviations (STDs) of 0.16 ns for different satellites. However, for CNES, the STD of the real-time clock offset varies, with values of 0.10 ns, 0.19 ns, and 0.60 ns, respectively, for GPS, BDS-3 Medium Earth Orbit (MEO), and BDS-3 Inclined Geosynchronous Satellite Orbit (IGSO) satellites. Centimeter-level accuracy is achieved after convergence and positioning availability exceeds 99% for CNES and PPP-B2b services. Therefore, the difference between the two services in application areas depends on the acquisition of augmentation information. However, BDSBAS, which concentrates on code observations, demonstrates inferior performance in real-time orbit, clock offset, positioning accuracy, and positioning availability when compared to the other two services. Its primary application is in the aviation and maritime domains, where there is a greater need for service integrity, continuity, and reliability.

Real-Time Satellite Constellation Scheduling for Event-Triggered Cooperative Tracking of Space Objects

Real-Time Satellite Constellation Scheduling for Event-Triggered Cooperative Tracking of Space Objects

Performance Analysis of Multi-GNSS Real-Time PPP-AR Positioning Considering SSR Delay

For real-time (RT) precise point positioning (PPP), the state space representation (SSR) information is often delayed due to possible communication delays and specific broadcast intervals. In this case, the positioning results will diverge and re-converge due to the increase of SSR products extrapolation errors. In addition, RT orbit and clock offset accuracy, as well as their extrapolation errors, will vary in different systems and satellites. We propose a PPP with ambiguity resolution (PPP-AR) method that combines a time-differenced carrier phase (TDCP) model, in which the characteristics of the orbit and clock are considered. Under normal communication, the PPP-AR solution is obtained by fixing satellites with small SSR product errors. When the communication is abnormal, the TDCP model is utilized to extrapolate user coordinates by considering different extrapolation error characteristics of satellites. The experimental results show that GPS and Galileo SSR products have better accuracy than BDS, with signal-in-space user ranger errors (SISREs) of 2.7, 2.2, and 8.6 cm, respectively. Optimizing the PPP stochastic model based on SISREs can effectively reduce the convergence time. Under 5 min SSR delay, SISREs caused by clock and orbit extrapolation for GPS/Galileo/BDS are 3.5, 1.4, and 2.6 cm, respectively. After optimizing the TDCP stochastic model based on extrapolation errors, the horizontal and vertical positioning accuracies can be maintained at 0.7 cm and 5.0 cm. For multi-GNSS, the combination of the TDCP and PPP-AR can overcome the influence of short delay. After optimizing the stochastic model, the GPS/Galileo/BDS positioning accuracy can be maintained at about 2.4 cm under 3 min delay, showing an accuracy improvement rate of 59.3% compared with the traditional method using only PPP. Additionally, the rapid PPP convergence results can be obtained by inheriting previous filter state information when the communication recovers normally.

A High-Efficiency Satellite Image Classification Method for Real-Time Application Using Augmented Incremental Transfer Learning

Researchers have put forth a range of models for processing satellite images, each with distinct data formats and processing requirements. One limitation arises from variations in the forms of modules, such as the image capturing and feature extraction modules, impacting accuracy and scalability in real-time scenarios. This passage introduces and explores a pioneering very competent temporal engine crafted specifically for classification of real-time satellite images. The proposed approach utilizes augmented incremental transfer learning, aiming to mitigate the limitations associated with diverse processing requirements. This approach involves capturing real-time satellite data through Google’s Earth Engine and subsequently processing it using a Convolutional Neural Network (CNN) based on transfer learning. The CNN employs backscatter coefficient analysis, utilizing coefficients derived from Precision Image's average intensity value across a distributed target. By integrating incremental learning and CNN for classification, the model achieves an impressive average accuracy of 98.06% in detecting crop type and severity of damage. Comparative analysis with state-of-the-art approaches reveals the superiority of the proposed model. It outperforms existing models by 5% in accuracy, showcasing its efficacy in satellite image processing and classification.