Egyptian Journal of Remote Sensing and Space Sciences最新文献

{"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}

{"title":"New radio-seismic indicator for ELF seismic precursors detectability","authors":"Andrea Mariscotti ,&nbsp;Renato Romero","doi":"10.1016/j.ejrs.2024.10.003","DOIUrl":"10.1016/j.ejrs.2024.10.003","url":null,"abstract":"<div><div>This work considers the effectiveness of earthquakes (EQs) radio precursors mainly in the Extremely Low Frequency (ELF) range and below, and carries out an analysis based on a comprehensive set of EQ events documented in past publications and provided by the Opera 2015 project (six stations located in Italy). A new Radio-Seismic Indicator (RSI) is proposed, with the magnitude-distance relationship physically justified by path-loss expressions of the transverse magnetic mode. Classification performances of past and proposed RSIs are assessed calculating confusion matrices and on those the balanced accuracy and Matthews’ coefficient: the RSI performs significantly better reducing fall-outs and increasing precision for both classes, positive and negative precursors. Performance improvement is inherently limited by the overlap of the classes.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 12-20"},"PeriodicalIF":3.7,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744458","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":"Estimation of above ground biomass of mangrove forest plot using terrestrial laser scanner","authors":"Yeshwanth Kumar Adimoolam ,&nbsp;Nithin D. Pillai ,&nbsp;Gnanappazham Lakshmanan ,&nbsp;Deepak Mishra ,&nbsp;Vinay Kumar Dadhwal","doi":"10.1016/j.ejrs.2024.11.002","DOIUrl":"10.1016/j.ejrs.2024.11.002","url":null,"abstract":"<div><div>Above-Ground Biomass (AGB) is an important parameter in the conservation of mangrove ecosystem owing to their ecological and economic benefits. LiDAR technologies in forest studies have become popular, due to its highly accurate 3D spatial data acquisition. In this study, we propose an end-to-end framework for estimating AGB of mangroves from Terrestrial Laser Scanner (TLS) point clouds. The framework includes pre-processing of data, segmenting the wood and foliage at tree level using Weighted Random Forest (WRF) classifier and constructing Quantitative Structure Model (QSM) of the wooden components to estimate its biomass. The flow was extended to AGB estimation of 33 x 33 m plot by integrating tree level framework. The study also finds a unique solution to estimate the contribution of pneumatophores in the AGB. Segmentation of wood/foliage of tree point cloud using WRF yielded better results with an increment of 15.27 % in Balanced accuracy, 0.2 of Cohen’s Kappa coefficient, and 7.45 % in F1score than RF classifier. AGB estimation of mangroves using our approach using TLS data is 47.54 T/ha which has a mean bias of 0.0044 T/ha and RMS variation of 0.026 T/ ha when compared with the allometric methods. A Breadth-first graph-search segmentation approach was used to count the pneumatophores, aerial roots seen in few mangrove species (R² = 0.94 with manual counting) and estimate its contribution to AGB of mangroves which is first of its kind using TLS point cloud. This outcome could also aid future studies in modeling the underlying root network and estimating the below-ground biomass.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 1-11"},"PeriodicalIF":3.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705214","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":"Efficient bundle optimization for accurate camera pose estimation in mobile augmented reality systems","authors":"Shanglin Li ,&nbsp;Yalan Li ,&nbsp;Yulin Lan ,&nbsp;Anping Lin","doi":"10.1016/j.ejrs.2024.10.006","DOIUrl":"10.1016/j.ejrs.2024.10.006","url":null,"abstract":"<div><div>Augmented reality has a long research history in computer vision and computer graphics communities. It aims to enhance the user experience for real scenes via overlapping virtual objects. Nowadays, mobile augmented reality has attracted much attention from researchers and developers due to the development of hardware techniques. Modern mobile devices such as mobile phones have a powerful computational ability for augmented reality applications. As a result, many researchers have paid attention to mobile augmented reality. From the technical viewpoint of augmented reality, mobile augmented reality largely depends on camera pose estimation. However, existing methods make it difficult to achieve the best balance between accuracy and efficiency, according to our investigation, and this may handicap the performance of mobile augmented reality systems. To overcome the problem, in this paper, we propose a novel approach to camera pose estimation based on bundle optimization. Our proposed method is evaluated on real-world datasets and is also tested in the mobile augmented reality system. Both experiments demonstrate that our proposed method has fast speed and high accuracy.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 743-752"},"PeriodicalIF":3.7,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655057","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":"Revealing Potential Mineralization Zones Utilizing Landsat-9, ASTER and Airborne Radiometric Data at Elkharaza-Dara Area, North Eastern Desert, Egypt","authors":"Mahmoud Abd El-Rahman Hegab,&nbsp;Islam Abou El Magd,&nbsp;Kareem Hamed Abd El Wahid","doi":"10.1016/j.ejrs.2024.10.005","DOIUrl":"10.1016/j.ejrs.2024.10.005","url":null,"abstract":"<div><div>The present work enhances mineral exploration in Egypt’s Eastern Desert by mapping lithological units and identifying hydrothermal alteration zones, potentially leading to the discovery of economically viable mineral deposits. This study employs a comprehensive approach of integrating multispectral bands from Landsat-9 and ASTER images with airborne radiometric data. Various image enhancement techniques such as False Color Composite (FCC), Minimum Noise Fraction (MNF), and Principal Component Analysis (PCA) are utilized to map enhanced lithological units. Additionally, image classification techniques, including Spectral Angle Mapper (SAM) and Crosta Principal Component (CROSTA PC), are applied to emphasize hydrothermal alteration minerals like alunite, calcite, hematite, illite, chlorite, epidote, kaolinite, montmorillonite, and sericite. Furthermore, radioelement ratios (eU/eTh, eU/K, eTh/K, and eU-(eTh/3.5)) and the F-parameter (K*(eU/eTh)) are utilized. Mineral percentages are determined using Scanning Electron Microscope (SEM), allowing for the observation of ore minerals from the Elkharaza-Dara area deposits, which exhibit varying compositions. Maximum values are recorded for specific elements: aluminum (10.48 wt% Al), silicon (65.38 wt% Si), silver (0.32 wt% Ag), copper (2.65 wt% Cu), gold (5.25 wt% Au), potassium (4.32 wt% K), hafnium (3.84 wt% Hf), calcium (26.94 wt% Ca), carbon (56.92 wt% C), and oxygen (53.71 wt% O). These findings offer valuable insights into the elemental composition of the mineralized deposits in the study area. The multi-algorithm integration approach has been confirmed through various methods, including comparison with existing geological maps, fieldwork, and microscopic analysis of selected samples from alteration zones across the study area.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 716-733"},"PeriodicalIF":3.7,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655056","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":"Potential of temporal satellite data analysis for detection of weed infestation in rice crop","authors":"Manju Tiwari ,&nbsp;Prasun Kumar Gupta ,&nbsp;Nitish Tiwari ,&nbsp;Shrikant Chitale","doi":"10.1016/j.ejrs.2024.10.002","DOIUrl":"10.1016/j.ejrs.2024.10.002","url":null,"abstract":"<div><div>Weeds are unwanted vegetation that compete with main crops for essential resources like light, water, and nutrients, leading to significant reductions in food crop yield and economic losses. Addressing this issue is crucial, particularly during the Kharif cropping season when cloud cover interferes with remote sensing capabilities. This study is an attempt to investigate the potential of satellite-based temporal analysis in weed detection from agricultural fields. The research focused on rice cultivation at the Research cum Instructional farms of Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh. The study explored the utility of satellite imagery for assessing crop health, demonstrating how weed infestation influences vegetative indices. The study utilized satellite images from PlanetScope and Sentinel-2 to examine the temporal variation in vegetation indices across two treatments: pure rice and rice with weeds. NDVI analysis revealed a significant decline in treatments affected by weeds (upto 41% less), suggesting that time-series satellite data can serve as an early indicator of weed infestation in standing rice crops. These findings were further verified by backscatter values from the Sentinel-1 dataset, which indicated a reduction in backscatter (upto 18% less) due to the suboptimal growth conditions in weed-infested treatments compared to weed-free rice. While the technology has shown efficacy at a preliminary stage, there is significant potential for its broader application and scalability in operational contexts.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 734-742"},"PeriodicalIF":3.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655125","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":"Visualization of humpback whale tracking on edge device using space-borne remote sensing data for Indian Ocean","authors":"S. Vasavi,&nbsp;Vasanthi Sripathi,&nbsp;Chandra Mouli Simma","doi":"10.1016/j.ejrs.2024.10.004","DOIUrl":"10.1016/j.ejrs.2024.10.004","url":null,"abstract":"<div><div>The conservation of humpback whale populations faces ongoing challenges, including human-induced mortality, despite the ban on commercial whaling. Recent advancements in high-resolution satellite imagery offer promise for estimating whale populations, particularly in remote and inaccessible regions. However, significant research gaps persist, necessitating innovative approaches for effective monitoring and conservation efforts. This paper presents a novel methodology that integrates high- resolution satellite imagery with state-of-the-art deep learning techniques to monitor and conserve humpback whale populations, with a focus on the Indian Ocean region. Specifically, application of cutting-edge deep learning models such as YOLO for object detection and EfficientNet for classification to automate the detection, classification, and tracking of humpback whales in satellite images is explored. By leveraging deep convolutional neural networks (CNNs), the proposed ensemble system offers a robust and generalizable approach for automatically detecting, classifying, and tracking whales in space-borne satellite imagery, thereby addressing the challenge of uncertain whale populations in the world’s oceans. The results demonstrate promising accuracy and performance metrics: the Segment Anything Model(SAM) achieves an accuracy of 89.2%, YOLO achieves an accuracy of 99.2%, EfficientNet achieves an accuracy of 99% across various tasks.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 705-715"},"PeriodicalIF":3.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655055","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":"Phenology-based winter wheat classification for crop growth monitoring using multi-temporal sentinel-2 satellite data","authors":"Solomon W. Newete ,&nbsp;Khaled Abutaleb ,&nbsp;George J Chirima ,&nbsp;Katarzyna Dabrowska-Zielinska ,&nbsp;Radoslaw Gurdak","doi":"10.1016/j.ejrs.2024.10.001","DOIUrl":"10.1016/j.ejrs.2024.10.001","url":null,"abstract":"<div><div>Wheat is one of the most important staple crops consumed by more than four billion people in the world. However, its production is challenged by the impact of climate change which accounts for a 5.5 % reduction in wheat yield and it is predicted to dwindle further by about 30 % in 2050, due to trends in temperature, precipitation, and carbon dioxide. An effective annual crop estimate is necessary not only to inform governments the status of national food security, but also to determine the benchmark on which agricultural commodities are priced in the market. Thus, annual crop monitoring and yield estimate is paramount to determine the amount of wheat imports required to make up for the shortfalls in the national wheat production in South Africa, which has been a net importer of wheat since 1998. This study aimed at investigating the most distinguishable crop phenology for accurate winter wheat classification during the growing season from August – December 2020 using Sentinel-2 imageries and Random Forest algorithm. The winter wheat crop was more accurately identified during the crop ‘heading’ stage in October yielding the highest user’s (75.56 %) and producer’s (92.52 %) accuracies, despite the relatively lower overall accuracy (78.14 %) compared to that of December with overall accuracy of 83.58 % obtained during the maturity stage. This study, therefore, found that the extraction of NDVI values of the winter wheat crop over the period of the growing season using the Sentinel-2 NDVI series method and grouping these values into distinct classes using the K-means unsupervised clustering techniques assist to identify the different crop phenologies based on which the winter wheat crop could be detected and mapped accurately. The phenology-based classification of the winter wheat crop during the heading stage, reduce the ambiguity of spectral confusion created with surrounding grass and maize crops.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 695-704"},"PeriodicalIF":3.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444566","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":"Space-based mid-wavelength infrared camera module for peatland fires and volcanic activities of Andesite rock","authors":"Bustanul Arifin ,&nbsp;Irwan Priyanto ,&nbsp;Ahmad Fauzi ,&nbsp;Andi Mukhtar Tahir ,&nbsp;Moedji Soedjarwo","doi":"10.1016/j.ejrs.2024.09.001","DOIUrl":"10.1016/j.ejrs.2024.09.001","url":null,"abstract":"<div><div>Two major perenial disasters are prevalent in Indonesia, namely, peatland fires and volcanic activities associated with Andesite rock. Thus, the Indonesian Government has prioritized the prevention and mitigation of both disasters. Indonesia’s Research Center for Satellite Technology-National Research and Innovation Agency then implemented the program as a satellite payload project. In this study, we describe the design of a space-based mid-wavelength infrared (SMWIR) camera module to monitor peatland fires and volcanic activities associated with Andesite rock. Using the spectral range as the basis of design and the iteration process of general steps in designing a camera, a SMWIR camera module was successfully designed. First, the spectral range was obtained by an intersection of four methods of determining spectral bands. Subsequently, the optical section, was conducted using Zemax by applying three criteria to analyze the optical performance, such as the spot diagram, encircled energy, and modulation transfer function (MTF). Thereafter, the mechanical design was achieved through the SOLIDWORKS software. The fourth step, namely, the structure or thermal design, was achieved by both Thermal Desktop/SINDA FLUINT and Zemax. In the electronic section, both the camera and detector were developed. Finally, a calibration system was specified over the module. Results in the form of graphs, pictures, and tables indicate that all established conditions, including those of the technical side, were achieved. Therefore, high performance in terms of the image, durability, transmission, and thermal stability can easily be achieved; additionally, the module is feasible, lightweight, and compact.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 686-694"},"PeriodicalIF":3.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356975","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":"Gap filling of missing satellite data from MODIS and CMEMS for chlorophyll-a in the waters of Aceh, Indonesia","authors":"M.N. Hidayat ,&nbsp;R. Wafdan ,&nbsp;M. Ramli ,&nbsp;Z.A. Muchlisin ,&nbsp;S. Rizal","doi":"10.1016/j.ejrs.2024.08.004","DOIUrl":"10.1016/j.ejrs.2024.08.004","url":null,"abstract":"<div><div>The motivation behind our study is to identify a robust method to enhance the accuracy of missing data, particularly chlorophyll-a data, which often goes undetected due to various factors. This study analyzes chlorophyll-a concentrations and sea level changes due to tides using three methods: Linear Interpolation, Fillgaps, and Modified Fillgaps. Two experiments were conducted: Experiment I involved random data removal (60% and 70%), and Experiment II combined sequential and random data removal (25% sequentially on the right, 35% and 45% randomly on the left). In Experiment I, the Modified Fillgaps method showed high correlation coefficients (up to 0.96) between original and reconstructed data, demonstrating its effectiveness in accurately filling significant data gaps. This method also exhibited low Root Mean Square Error and Mean Absolute Error values, confirming its predictive precision. In Experiment II, despite structured and realistic data loss patterns, the method maintained high correlation and low prediction errors, with low Normalized Root Mean Squared Error and Mean Absolute Percentage Error values, further validating its reliability. Additionally, the method excelled in two-dimensional chlorophyll-a maps, outperforming Linear Interpolation and Fillgaps methods in scenarios with 50% and 60% data loss, achieving higher correlation and lower prediction errors. These findings are crucial for environmental and climatological studies relying on satellite-derived data, confirming the Modified Fillgaps method as the most reliable and effective for handling significant data loss in chlorophyll-a map analyses. Future research should explore its application to other environmental data types and more complex data loss patterns.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 669-685"},"PeriodicalIF":3.7,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328196","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}