Egyptian Journal of Remote Sensing and Space Sciences最新文献

{"title":"Comparison of random forest, gradient tree boosting, and classification and regression trees for mangrove cover change monitoring using Landsat imagery","authors":"Nirmawana Simarmata ,&nbsp;Ketut Wikantika ,&nbsp;Trika Agnestasia Tarigan ,&nbsp;Muhammad Aldyansyah ,&nbsp;Rizki Kurnia Tohir ,&nbsp;Adam Irwansyah Fauzi ,&nbsp;Anggita Rahma Fauzia","doi":"10.1016/j.ejrs.2025.02.002","DOIUrl":"10.1016/j.ejrs.2025.02.002","url":null,"abstract":"<div><div>Ineffective land use in coastal areas negatively impacts the environment and destroys mangrove ecosystems, contributing to increasing greenhouse gas emissions and decreasing carbon sequestration. This study aimed to monitor the land use changes in mangrove areas with Landsat data using several machine learning (ML) methods. According to the random forest (RF), gradient tree boosting (GTB), and classification and regression trees algorithms (CART), the mangrove area exhibited significant fluctuations over the study period, with the largest expansion observed from 1999 to 2008 (4,240.57 ha), followed by a slight increase in 2023 (368.36 ha from 2019). Accuracy assessment revealed distinct performance levels across the models. The RF algorithm demonstrated the highest overall accuracy (OA) of 98.8 %, with kappa values ranging from 0.96 to 0.98, indicating high consistency and reliable predictions over time. The CART algorithm, while accurate, showed more variability, especially between 1991 and 1994, with an OA ranging from 85.3 % to 92.5 % and kappa values between 0.92 and 0.96. The GTB algorithm had moderate performance, with OA values between 85.6 % and 95.7 % and kappa values ranging from 0.92 to 0.96, suggesting reliable results but with some inconsistency compared to RF. The RF algorithm’s superior OA and consistency make it the most suitable long-term land cover monitoring method. Future studies can benefit from incorporating RF in assessing ecosystem changes, including carbon sequestration potential in mangrove forests.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 138-150"},"PeriodicalIF":3.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface deformation of the 26 January 2021 earthquake in the Sinjar – Hasakah Area, N Iraq and NE Syria, from Sentinel‑1A InSAR images","authors":"Jamal A.H. Doski","doi":"10.1016/j.ejrs.2025.02.001","DOIUrl":"10.1016/j.ejrs.2025.02.001","url":null,"abstract":"<div><div>The deformation of Earth’s surface caused by earthquakes stands as a critical geological hazard in regions characterized by active tectonic structures. This study investigates the impact of a low-to-moderate magnitude earthquake (Mw 4.9) that occurred on January 26, 2021, in the Sinjar – Hasakah area (N Iraq and NE Syria). This seismic event marks the most significant occurrence in the study area over the past 48 years. The earthquake’s moment tensor solution suggests the presence of a right-lateral (dextral) strike-slip fault. 4 Sentinel-1A SAR images were processed by the DInSAR technique to analyze the surface deformation and identify the seismogenic fault of the 26 January 2021 earthquake. The most significant deformation observed along these active faults ranged from – 7.56 cm (subsidence) to + 3.75 cm (uplift) in the ascending orbit, and from – 4.56 cm (subsidence) to + 4.61 cm (uplift) in the descending orbit along the Line of Sight (LOS). It is inferred that the Hasakah seismogenic fault is responsible for the 26 January 2021 earthquake. This fault is a NW-trending, steeply dipping seismically active dextral strike-slip basement fault that formed during the Late Pliocene structural inversion. It extends over 120 km from the vicinity of Hasakah city in the northwest into the epicentral area in the southeast, traversing the boundary between the Sinjar and Abd El Aziz uplifts. Moreover, this seismogenic fault intersects with an active E-trending, S-dipping thrust basement fault that cuts through the northern limbs of both the Abd El Aziz and Sinjar anticlines.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 128-137"},"PeriodicalIF":3.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143298435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New insights into the Menyuan Ms6.9 Earthquake, China: 3D slip inversion and fault modeling based on InSAR remote sensing approach","authors":"Mohamed I. Abdelaal ,&nbsp;Min Bao ,&nbsp;Mohamed Saleh ,&nbsp;Mengdao Xing","doi":"10.1016/j.ejrs.2024.11.003","DOIUrl":"10.1016/j.ejrs.2024.11.003","url":null,"abstract":"<div><div>Harnessing high-precision spaceborne InSAR data, this study investigates the seismic impacts of the Ms 6.9 Menyuan earthquake in Qinghai, China, on January 8, 2022. The earthquake occurred at the intersection of the Lenglongling (LLLF) and Tuolaishan (TLSF) faults within the Qilian Haiyuan Fault (QL-HYF) zone, causing extensive infrastructure damage but no fatalities. Previous studies explored the step-over rupture zone and slip distribution of the Menyuan event but often relied on oversimplified rectangular dislocation models, insufficient for capturing complex fault ruptures. This simplification impedes accurate representation of curved fault segments in the QL-HYF zone, leading to unclear slip distribution estimates, particularly at the transition from LLLF strike-slip to TLSF thrust behavior. To address these limitations, this study employs a 3D triangulated angular dislocation slip-inversion approach in an isotropic half-space, enabling precise modeling of curved fault geometries. Leveraging Differential InSAR (D-InSAR) and Pixel Offset Tracking (POT), we reconstructed the earthquake’s 3D displacement field and extracted surface fault traces, informing our angular dislocation model for accurate coseismic slip distribution. Our results revealed significant horizontal displacement, with 38.5 cm of left-lateral movement accompanied by a 4 cm downward thrust. The slip model showed 2.7 m of slip along the LLLF and 0.8 m along the TLSF, concentrated at shallow depths between 2 and 7 km, highlighting surface rupture. The transition zone between the faults acted as a valve, modulating rupture progression and controlling energy release. These findings refine the understanding of coseismic deformation and slip distribution, supporting seismic hazard mitigation and emergency response strategies.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 116-127"},"PeriodicalIF":3.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying water-lubricated faults in the vicinity of a dam","authors":"Carolle Fomekong Lambou ,&nbsp;Carolle Fomekong Lambou ,&nbsp;Jorelle Larissa Meli’i ,&nbsp;Harlin Ekoro Nkoungou ,&nbsp;Kasi Njeudjang ,&nbsp;Andre Michel Pouth Nkoma ,&nbsp;Philippe Njandjock Nouck","doi":"10.1016/j.ejrs.2025.01.003","DOIUrl":"10.1016/j.ejrs.2025.01.003","url":null,"abstract":"<div><div>The development of remote sensing, with its many applications, combined with field data collected by geologists, geophysicists and geotechnical scientists, is now contributing to sustainable development in the mining, infrastructure and civil protection sectors. This study integrates remote sensing and the audiomagnetotelluric (AMT) method to identify faults lubricated or potentially lubricated by water in the vicinity of a dam. The data set includes SRTM_DEM images and AMT data from seven stations collected in the study area. The results from remote sensing show 284 lineaments with a main NE-SW direction, including 17 corresponding to existing faults in the area. The lineament density map shows that stations A1, A3 and A7 are located in the most fractured zones. The Bahr dimensional analysis shows that, at the same frequencies, Swift skew values of less than 0.1 and two-dimensionality parameter values of greater than 0.1 are observed at stations A3, A5 and A7, suggesting the presence of 2D structures correlating with the faults at these stations, oriented NE-SW, NE-SW and NNE-SSW respectively. In addition, the 2D and 3D resistivity models make it possible to distinguish at what depth the faults highlighted can be lubricated by water in the study area containing a total of 39 faults, 17 of which are normal and may be partially or fully lubricated depending on whether they interact with the hydrographic or drainage network. These identified lubricated faults need further study, as they could induce weak earthquakes.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 99-115"},"PeriodicalIF":3.7,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cot-DCN-YOLO: Self-attention-enhancing YOLOv8s for detecting garbage bins in urban street view images","authors":"Shan Dong ,&nbsp;Wenhao Xu ,&nbsp;Huihui Zhang ,&nbsp;Litao Gong","doi":"10.1016/j.ejrs.2025.01.002","DOIUrl":"10.1016/j.ejrs.2025.01.002","url":null,"abstract":"<div><div>Accurately and quickly obtaining information from garbage bins has great application value in smart city construction and urban environmental management. However, existing deep learning methods are affected by factors such as occlusion, large geometric appearance differences, and multi-scale, leading to missed detections in garbage bin detection results. We propose a Cot-DCN-YOLO model for garbage bin detection, which is designed to effectively extract contextual information with the Double Convolutions Semantic Transformation (DCST) module, which addresses the vulnerability of garbage bins to occlusion. According to the large geometric appearance differences when garbage bins are damaged, we propose the C2f embedded with DCNv2 (DC2f) module, which can adaptively adjust the target shape with a flexible receptive field. Furthermore, considering the multi-scale characteristics of garbage bins in images, we introduce the SPPCSPC module. Experimental results show that compared with other methods, Cot-DCN-YOLO achieves the best results on our self-made garbage bin dataset, with Precision, Recall, and mAP reaching 77.1%, 69.4%, and 74.0%, respectively, outperforming existing SOTA methods.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 89-98"},"PeriodicalIF":3.7,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusing satellite imagery and ground geochemical data to map alteration zones for gold exploration in western Nigeria","authors":"S.A. Alimi,&nbsp;E.J.M. Carranza","doi":"10.1016/j.ejrs.2025.01.001","DOIUrl":"10.1016/j.ejrs.2025.01.001","url":null,"abstract":"<div><div>As alteration mapping is vital in identifying signatures of specific mineral deposits, this study aimed to map alteration zones associated with orogenic gold mineralization in the Wawa area using remote sensing and geochemical data. Sentinel-2 satellite images were fused with an ALOS PRISM panchromatic image for spatial resolution enhancement. Image processing methods such as color compositing, band rationing, thresholding, and principal component analysis were used for hydrothermal alteration mapping. Field investigations, major, and trace element geochemical analysis of samples were applied for results validation. The findings showed that the significant lithologies in the Wawa area are migmatite, granite gneiss, quartzite, amphibolite/amphibole schist, phyllite, and granites. Gold occurs as micro-veins within amphibolite/amphibole schist and granite gneisses in close association with pyrite. Significant alterations observed at/around the gold mining sites are clay and iron oxide. There is increased alteration intensity at apparent contact zones between granite gneisses and schists. Geochemical data support the findings that most existing gold mining sites are within intense iron oxide and clay alteration zones, and that gold pathfinder elements such as Cu, As, Pb, and Ni occur anomalously within vein quartz and amphibolitic rock samples from the alteration zones in the Wawa area. Future exploration targets for orogenic gold in the Wawa area should be concentrated within similar alteration zones with no gold mining sites.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 77-88"},"PeriodicalIF":3.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GIS-based species distribution modeling of invasive Mnemiopsis leidyi in the southern caspian sea using satellite imageries","authors":"Mahdieh Abadijoo ,&nbsp;Mehdi Gholamalifard ,&nbsp;Mehdi Mokhtarzade ,&nbsp;Parviz Jokar ,&nbsp;Tiit Kutser ,&nbsp;Andrey G. Kostianoy ,&nbsp;Aleksander V. Semenov","doi":"10.1016/j.ejrs.2024.12.002","DOIUrl":"10.1016/j.ejrs.2024.12.002","url":null,"abstract":"<div><div>Due to its unique variety of species, the Caspian Sea has great ecological-economic values and the people living on its coasts use this environmental asset as a source of income. <em>Mnemiopsis leidyi</em>’s invasion of this ecosystem in 1999, however, has led to instability of the ecosystem and decreased access to the services provided by it causing a decline in the population of Kilka fish and in the fishing industry. Accordingly, the present study attempted to carry out spatial modeling of <em>M. leidyi</em> using Multi-Criteria Evaluation (MCE) and Maximum Entropy (MaxEnt) models in summer and autumn. The main goal of comparing these two models was to find the best distribution sites of <em>M. leidyi</em> as an inhibiting species for the ecosystem services. The modelling was based on the following variables including: chlorophyll <em>a</em> concentration, photosynthetic active radiation (PAR), water temperature, turbidity, concentration of nitrogen, phosphorus, oxygen, salinity, sea level anomaly, depth, distance from the coast and bottom slope on Mazandaran coasts of Iran in the southern basin of the Caspian Sea. The findings indicated that the most favorable distribution of <em>M. leidyi</em> was in summer near the central (sub-region N 6563 in Behshahr and 6463 in Babolsar (and eastern coasts (sub-region N 6663 in Noshahr), and the least favorable distribution was near the western coast (sub-region N 6163 in Ramsar). In autumn, however, all the coastal zones had a high level of favorability for the viability of <em>Mnemiopsis leidyi</em>. ‘Distance from the coast’ and ‘depth’ were identified as the most important variables explaining the variation in the distribution of <em>M. leidyi</em> and helping to identify the favorable areas for the viability of <em>M. leidyi</em> in summer and autumn. The obtained results can help to understand better the behavior of <em>M. leidyi</em>, its spatial and temporal distribution, as well as to improve the ecosystem services in the southern basin of the Caspian Sea including in location of cage aquaculture sites.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 63-76"},"PeriodicalIF":3.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison and accuracy assessment of unmanned aerial vehicle and terrestrial measurement in base map production","authors":"Veysel Yildiz,&nbsp;Aydan Yaman","doi":"10.1016/j.ejrs.2024.12.003","DOIUrl":"10.1016/j.ejrs.2024.12.003","url":null,"abstract":"<div><div>In the present era, unmanned aerial vehicles (UAVs) have become a prevalent tool for data and map production in the domain of remote sensing and photogrammetry, driven by advancements in technology. The production of base maps has become more straightforward, precise, economical, and time-efficient in recent years, largely due to the advent of UAVs and the subsequent development of new techniques. The base maps of the area were produced using two methods: Terrestrial measurement and UAV data. The squared mean errors were calculated and found to be my = ±1.49 cm, mx= ±1.58 cm and m_z = ±2.52 cm for ground control points, m_y = ±1.54 cm, m_x= ±1.65 cm and m_z = ±2.55 cm for check points and my = ±2.41 cm, mx= ±2.66 cm and m_z= ±3.47 cm for detail points. The results were found to fall within the specified limit values. It was therefore concluded that UAVs provide the anticipated accuracy for the production of base maps, which are required to be continually updated and form the basis for a range of projects and can be readily employed in this regard. This study demonstrates that base maps produced with UAV data meet the requisite scientific and academic standards, including accuracy and precision. Additionally, it illuminates the advantages of UAV data in base map production, particularly in terms of time, accuracy, and cost.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 53-62"},"PeriodicalIF":3.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced lithological mapping via remote sensing: Employing SVM, random trees, ANN, with MNF and PCA transformations","authors":"Mohamed Ali El-Omairi,&nbsp;Manal El Garouani,&nbsp;Abdelkader El Garouani","doi":"10.1016/j.ejrs.2024.12.001","DOIUrl":"10.1016/j.ejrs.2024.12.001","url":null,"abstract":"<div><div>This study examines the performance of three classification algorithms—Support Vector Machines (SVM), Random Trees (RT), and Artificial Neural Networks (ANN)—applied to Landsat 9 and Sentinel-2 spectral data for lithological mapping. The study area, located in the Central Anti-Atlas, is covered by the 1:50,000 geological map of Aït Semgane, featuring diverse geological formations, ideal for testing advanced remote sensing techniques. Results show that SVM, particularly with Minimum Noise Fraction (MNF) transformation, offers the best performance. For Sentinel-2 images, SVM with MNF achieves high user and producer accuracies and well-defined lithological boundaries. While RT and ANN also show good performance, they are slightly inferior to SVM, with RT achieving a Kappa index of 0.84 for raw Landsat 9 bands and ANN obtaining a maximum of 0.75 for Sentinel-2 data transformed with MNF. The MNF transformation generally improves SVM and ANN performance, whereas Principal Component Analysis (PCA) often produces inferior results. The robustness of SVM for high-dimensional data and its resistance to overfitting make it a promising tool for accurate lithological classification. This research has practical implications for geology and Earth sciences. The use of dimensionality reduction, particularly MNF, can greatly enhance classification quality for multispectral and hyperspectral data. These results are not only valuable for improving geological mapping, mineral exploration, and natural resource management at local and regional scales but also have significant potential for large-scale terrain analysis in diverse global contexts. The findings could support global efforts in geological hazard assessments, resource management, and environmental monitoring, particularly in regions with challenging geological settings. The study also proposes future research directions, such as exploring new dimensionality reduction techniques, evaluating classification methods with different remote sensing datasets, and integrating geophysical or geochemical data to further improve accuracy</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 34-52"},"PeriodicalIF":3.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral–Spatial Adaptive Weighted Fusion and Residual Dense Network for hyperspectral image classification","authors":"Junding Sun ,&nbsp;Hongyuan Zhang ,&nbsp;Xiaoxiao Ma ,&nbsp;Ruinan Wang ,&nbsp;Haifeng Sima ,&nbsp;Jianlong Wang","doi":"10.1016/j.ejrs.2024.11.001","DOIUrl":"10.1016/j.ejrs.2024.11.001","url":null,"abstract":"<div><div>The dense and nearly continuous spectral bands in hyperspectral images result in strong inter-band correlations, which can diminish performance of the model in classification tasks. Moreover, most convolutional neural network-based methods for hyperspectral image classification typically depend on a fixed scale to extract spectral–spatial features, which ignore the detail features of some objects. To address the above issues, a novelty Spectral Spatial Adaptive Weighted Fusion and Residual Dense Network (S<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>AWF-RDN) is proposed for Hyperspectral image classification. Specifically, the proposed S<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>AWF-RDN consists of spectral–spatial adaptive weighted fusion module, multi-channel feature concatenation residual dense module, and spatial feature fusion module. Firstly, the spectral information optimization branch is developed to adjust the weights assigned to various spectral channels. Similarly, the spatial information optimization branch is developed to adjust the weights for different spatial regions. Secondly, to obtain rich spectral spatial information from different levels, multi-channel feature concatenation residual dense module has been proposed. In addition, a multi-channel feature concatenation block is designed guiding the model to extract spectral spatial information at different scales. Finally, spatial feature fusion module is introduced to retain more spatial information. The experimental outcomes illustrate that the proposed network model exhibits superior classification performance on three renowned hyperspectral image datasets. Furthermore, the efficacy of the proposed network model is further corroborated through comparative and ablation studies.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 21-33"},"PeriodicalIF":3.7,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}