Egyptian Journal of Remote Sensing and Space Sciences最新文献

Land Use/Land Cover (LULC) classification using hyperspectral images in remote sensing is a leading technology. However, LULC classification using hyperspectral images is a difficult task and time-consuming process because it has fewer training samples. To overcome these issues, we proposed a deep-Long Short-Term Memory (deep-LSTM) to classify the LULC. Before classifying the LULC, extracting valuable features from an image is needed, and after extracting the features, selecting the bands which are helpful for classification should be done. In this work, we have proposed an auto-encoder model for feature extraction, a ranking-based band selection model to select the bands, and deep-LSTM for classification. We have used three publicly available benchmark datasets; they are Pavia University (PU), Kennedy Space Centre (KSC), and Indian Pines (IP). Average Accuracy (AA), Overall Accuracy (OA), and Kappa Coefficient (KC) are used to measure the classification accuracy. The suggested technique has provided the top outcomes compared to the other state-of-the-art methods.

This study introduces an innovative approach for analyzing bending deformation and strength in textile-reinforced laminated composites, which is crucial for CubeSat structures. Our research develops a dual-scale modelling framework: a microscale model capturing the detailed viscoelastic-viscoplastic behaviour of fibres and matrices and a mesoscale model that integrates this with textile geometry, advanced shear deformation theories, and distributed damage effects. Extensive laboratory experiments validate our model, confirming its accuracy in predicting the composite behaviour under varied conditions. This work notably enhances the understanding and prediction of textile-reinforced composites, offering significant implications for CubeSat structural design and performance.

Space debris is a growing problem for low earth orbit (LEO) and geosynchronous orbit (GEO). The risk of space debris currently affects human activities in Space and is controlled by the collision avoidance alert. However, the risk is growing, which increases future space mission costs to avoid or shield against space debris impact.

The project has evolved over four years, culminating in Meng/BEng graduation projects. At the heart of our innovation is utilising the naturally high temperatures in the exosphere and stratosphere, which can soar to 1200 °C. This environment is ideal for initiating a chemical reaction within a pressurised chamber containing a mix of H2-O2 gases, generating heat sufficient to ablate common space debris materials such as titanium, aluminium, and composites. We have crafted an initial satellite design and performed Multiphysics simulations using COMSOL to validate our concept. The project now seeks investment to enhance four critical areas: the satellite's mechanical design to ensure safe operation within a debris field, the development of a dynamic control system for debris collection and satellite navigation, the management of H2 and O2 tank refilling, and the creation of a mechanism for the safe release of ablated materials back into Space.

Extraction of color and texture features of buildings from high-resolution remote sensing images often encounters the problems of interference of background information and varying target scales. In addition, most of the current attention mechanisms focus on building key feature selection for building extraction optimization, but ignore the influence of the complex background. Hence, we propose incorporating a novel reverse attention module into the network. The innovative module enables the model to selectively extract crucial building features while suppressing the impact of intricate background noise. It mitigates the influence of uniform spectral and structurally similar heterogeneous background targets on building segmentation and extraction. As a result, the overall generalizability of the model is improved. The reverse attention can also emphasize and amplify the specific details pertaining to the boundaries of the target. Furthermore, we couple a new multi-branch convolution block into the encoder, integrating dilated convolutions with multiple dilation rates. Compared to other methods that use only one multi-scale module to extract multi-scale information from high-level features, we use different receptive field convolutions to simultaneously capture multi-scale targets from multi-level features, effectively improving the ability of the model to extract multi-scale building features. The experimental findings demonstrate that our proposed multi-branch reverse attention semantic segmentation network achieves IoU of 90.59% and 81.79% on the well-known WHU building and Inria aerial image datasets, respectively.

Large- and small-scale subsidence coexist in the world's coastal cities due to extensive land reclamation and fast urbanization. Synthetic aperture radar (SAR) images are typically limited by either low resolution or small coverage, making them ineffective for fully monitoring displacement in coastal areas. In this research, a machine learning-based method is developed to investigate the reclaimed land subsidence based on multi-satellite SAR data integration. The proposed method requires at least a pair of SAR images from complementary tracks. First, the line-of-sight (LOS) displacements are recovered in connection to a series of extremely coherent points based on the differential interferometry synthetic aperture radar (DInSAR). These LOS displacements are then converted into their vertical component, geocoded to a common grid, and simultaneously integrated (i.e., pixel-by-pixel) based on Support Vector Regression (SVR). The proposed methodology does not necessitate the simultaneous processing of huge DInSAR interferogram sequences. The experiments include high-resolution COSMO-SkyMed (CSK) and TerraSAR-X (TSX) images, as well as a small monitoring cycle Sentinel-1 (S1) images of reclaimed territories near Hong Kong Kowloon City. The overall average annual displacement (AAD) ranges from -12.86 to 11.63 mm/year derived from 2008 to 2019. The evaluation metrics including RMSE, MAE, correlation coefficient, and R-squared are used to investigate the impact of SVR in the integration of SAR datasets. Based on these evaluation metrics, SVR is superior in terms of integration performance, accuracy, and generalization ability. Thus, the proposed method has potentially performed multi-satellite SAR data integration.

Land surface temperature (LST) is a critical geo-parameter in terrestrial environmental interaction processes, directly related to land cover change (LCC) which modifies surface energy balance. In this study, LST data from 2003 to 2019 were reconstructed in the Sichuan Basin with average R² of 0.85 (daytime) and 0.91 (nighttime), effectively filling in the missing pixels and reducing the noise components. Emerging hot spot analysis (EHSA) and land cover transfer matrix were utilized to analyze the multi-patterns of LST spatiotemporal evolution and responses to LCC. Results indicate that LST hot spots are concentrated in low-altitude basin floor and are dominated by sporadic hot spots. Cold spots are mainly in marginal high-elevation mountains, but the dominant pattern varies with time scale. The largest proportions of hot and cold spots are found in summer (>46 %) and autumn (>29 %), respectively. Moreover, the significant upward and downward trends of LST cold and hot spots are most prominent in western plain and marginal mountains, respectively, and have the largest coverage in summer and autumn, respectively. In total LCC area, cropland-to-forest (CF), cropland-to-impervious (CI), and forest-to-cropland (FC) account for 93.55 %. Among them, CI significantly promotes the aggregation and upward trend of daytime LST hot spots. CF and FC have the strongest effect of aggregating LST cold spots and cooling LST in daytime, with CF being more effective. The information can serve as a reference for regional planning and climate change mitigation measures.

Human-driven Greenhouse gases (GHGs) are the most significant contributor to climate change. World countries and Egypt are moving towards achieving sustainable development goals (SDGs) 2030, and 2050, to reach Net-Zero emissions. Based on satellite observations, this research assesses and monitors the GHG emissions induced by human activities in Egypt. Different satellite sensors were utilized in this study to obtain Methane (CH₄), Carbon Dioxide (CO₂) amounts during 2015–2022. To get a deeper insight into the effects of anthropogenic activities on CO₂ and CH₄ amounts, they were correlated with land use and land cover, fire incidents, and industrial activities in Egypt. Results revealed a noticeable increase in CH₄ and CO₂ emissions over the country with a maximum level in 2022. CO₂ has a seasonal variation mode, with the highest amounts in spring reaching 0.000409 CO₂/mol dry-air. As well, the high CH₄ concentration fluctuates all the year-round, with a peak around 1890 ppbv in August. The high levels of GHGs mostly concentrated in the Nile Delta and Nile Valley, where most of the anthropogenic activities are existing. Fire incidents, industries, and land cover change maps showed a spatial matching with the high emission zones. However, the emissions are increasing in Egypt it does not exceed the global average. In conclusion, unmanaged human activities in Egypt increased GHGs release and affected environmental sustainability. This study attempts to better understand the ambient environment in Egypt and support the decision-makers with full insight into the GHG emission hotspots in the country to mitigate their release into the atmosphere and achieve Net-Zero emissions.

Understanding urban sprawl and its drivers is crucial for sustainable urban development. Most studies on Chinese urbanization have focused on coastal areas, paying little attention to urban centers in western China. This study examines urban expansion based on the Google Earth Engine (GEE), remotely sensed image, urban expansion model, and analysis of buffer and quadrant location in the Geographic Information System (GIS). Additionally, driving forces of urban expansion are examined based on the principle component analysis (PCA). Results indicate that urban land area increased more than 5.60 times, reaching 124,723 ha, an increase of over 400 % during 1990–2020. The urban expansion rate and intensity significantly increased and exhibited spatio-temporal heterogeneity. We identified that urban spatial expansion patterns changed from patch filling to patch border expansion, and urban expansion direction was mainly in the southern, northeastern, southwestern, and northwestern regions, extending along the traffic corridor, ring road, and adjacent cities. We suggest that economic development, population, and urbanization have become the driving factors of urban expansion. The GEE provides a new geographic processing algorithm based on massive image datasets, facilitating remote sensing processing. The results revealed that Chengdu is following trends witnessed in coastal cities of China; however, the significance of various drivers of urban expansion in these cities differs from that of the eastern cities. This study will help formulate policies for better urban land management and sustainable land development.

The acquisition of landslide inventory represents a pivotal challenge in landslide susceptibility mapping. Existing landslide susceptibility maps(LSMs) predominantly rely on manually obtained landslide inventories, leading to an overdependence on expert insights and susceptibilities to topographic and geomorphic influences. In regions characterized by steep terrain, obtaining a landslide inventory can be arduous or even unattainable, subsequently constraining the utility of LSMs. Addressing the limitations of conventional LSMs, this study introduces an innovative method for landslide inventory compilation and LSM creation, utilizing Small Baselines Subset Interferometry Synthetic Aperture Radar(SBAS-InSAR) technology. The study area selected for illustration is the Dongchuan district, notorious for frequent landslide occurrences. The application of SBAS-InSAR facilitated the extraction of surface deformation data, subsequently enabling the selection of landslide deformation points as samples. These samples underwent analysis through a particle swarm optimization-backpropagation neural network(PSO-BPNN) guided by deformation thresholds and the landslide developmental environment. This produced the LSM for the Dongchuan district. Subsequent validation of the LSM employed both qualitative and quantitative measures. Results elucidate that the LSM, as derived from the presented approach, primarily highlights high to very high susceptibility zones in landslide-prone areas, mirroring the spatial distribution of historical landslides. The method also achieved a commendable accuracy(ACC) of 79.59% and an area under the curve(AUC) value of 0.88. Notably, the landslide density exhibited a direct correlation with increasing susceptibility class. Such findings align with previous studies, endorsing the feasibility and reliability of the proposed approach.

The article develops a methodology for analyzing the noise immunity of satellite communication systems under small-scale disturbances of the ionosphere, taking into account the possibility of general and time-selective fading of received signals Differential Phase Shift Keying. The refined dependences of the intervals of time and space correlation of fading in the transionospheric radio channel on the parameters of transmitted signals and the state of the ionosphere are obtained. The analytical dependence of the probability of erroneous reception of signals with Differential Phase Shift Keying on the average signal-to-noise ratio at the receiver input, the frequency-time parameters of the signals and the characteristics of small-scale ionospheric inhomogeneities was obtained.