- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Detection of land use/cover change in Egyptian Nile Delta using remote sensing
摘要： The present study aims to assess the changes of different land use/land cover classes for Nile Delta of Egypt during the period from 1987 to 2015, to evaluate the impact of land cover change and urban sprawl, before, during and after the 25th of January 2011 using remote sensing and GIS techniques, as a result to unplanned urban sprawl which was done by people during the lack of general security of Egyptian revolution. The results indicated that there was a regular trend characterized in most classes and that the change in different land use/land cover classes ranged between increase and decrease areas. A continuous increase in agricultural, urban, ?sh farms and natural vegetation areas and a continuous decrease in water bodies and sand areas were detected in the studied area. The agricultural area recorded the highest increase during the period from 1987 to 2000 (305296.1 ha.) while it increased by 170578.1 ha., during the period from 2000 to 2015. However, in urban area, the highest increase was recorded during the period from 2000 to 2015 followed by the period 1987–2000 with mean values of 97940.8 and 53112.6 ha., respectively. The analysis of the results showed that most of Egyptian Delta governorates have been signi?cantly affected by the different classes of land use/land cover change due to agriculture activities, urban growth as a result of human activities dynamic impact.
关键词： Change detection,Remote sensing,Nile Delta governorates,Land use/land cover
更新于2025-09-23 15:23:52
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Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images
摘要： To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the proposed MOHD. Firstly, multi-scale objects for the post-event image are extracted through a widely used algorithm called the fractional net evaluation approach. The pixels within a segmental object are taken to construct the pairwise frequency distribution histograms. An arithmetic frequency-mean feature is then defined from the red, green and blue band histogram. Secondly, bin-to-bin distance is adapted to measure the change magnitude between the pairwise objects of bi-temporal images. The change magnitude image (CMI) of the bi-temporal images can be generated through object-by-object. Finally, the classical binary method Otsu is used to divide the CMI to a binary change detection map. Experimental results based on two real datasets with different land-cover change scenes demonstrate the effectiveness of the proposed MOHD approach in detecting land-cover change compared with three widely used existing approaches.
关键词： remote sensing application,detection algorithm,land use and land cover,histogram distance
更新于2025-09-23 15:23:52
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Dictionaries of deep features for land-use scene classification of very high spatial resolution images
摘要： Land-use classification in very high spatial resolution images is critical in the remote sensing field. Consequently, remarkable efforts have been conducted towards developing increasingly accurate approaches for this task. In recent years, deep learning has emerged as a dominant paradigm for machine learning, and methodologies based on deep convolutional neural networks have received particular attention from the remote sensing community. These methods typically utilize transfer learning and/or data augmentation to accommodate a small number of labeled images in the publicly available datasets in this field. However, they typically require powerful computers and/or a long time for training. In this work, we propose a simple and novel method for land-use classification in very high spatial resolution images, which efficiently combines transfer learning with a sparse representation. Specifically, the proposed method performs the classification of land-use scenes using a modified version of the well-known sparse representation-based classification method. While this method directly uses the training images to form dictionaries, which are employed to classify test images, our method utilizes a pre-trained deep convolutional neural network and the Gaussian mixture model to generate more robust and compact 'dictionaries of deep features.' The effectiveness of the proposed method was evaluated on two publicly available datasets: UC Merced and Brazilian Cerrado–Savana. The experimental results suggest that our method can potentially outperform state-of-the-art techniques for land-use classification in very high spatial resolution images.
关键词： Dictionary learning,Land-use classification,Sparse representation,Feature learning,Deep learning
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Environmental Monitoring Using Drone Images and Convolutional Neural Networks
摘要： Recently, drone images have been used in a number of applications, mainly for pollution control and surveillance purposes. In this paper, we introduce the well-known Convolutional Neural Networks in the context of environmental monitoring using drone images, and we show their robustness in real-world images obtained from uncontrolled scenarios. We consider a transfer learning-based approach and compare two neural models, i.e., VGG16 and VGG19, to distinguish four classes: 'water', 'deforesting area', 'forest', and 'buildings'. The results are analyzed by experts in the field and considered pretty much reasonable.
关键词： Land-use classification,Convolutional Neural Networks,Drones
更新于2025-09-23 15:23:52
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[IEEE 2018 International Ural Conference on Green Energy (UralCon) - Chelyabinsk (2018.10.4-2018.10.6)] 2018 International Ural Conference on Green Energy (UralCon) - Concepts of Solar Batteries Integration in Linear Infrastractural Objects
摘要： Land and maintenance costs are often underestimated in solar power costs evaluation, but those become more significant as solar cell prices go down. A shared use of land, infrastructure and maintenance can both reduce the solar electricity price and boost new infrastructure projects by supplying the demanded power and acceleration of investment return. We considered the possible technical, economic and social benefits of PV integration in railroads and powerlines. Some of the conclusions could be also expanded to other linear structures, demanding land alienation (pipe lines, highways, etc.). Own energy generation could significantly reduce the investment return period of such infrastructure. A possibility of getting high DC voltages in PV systems directly could be the most demanded in the 1.5 and 3 kVDC powered railroads. The application of solar batteries could also significantly improve railroads embankment stabilization in permafrost regions, which becomes more pronounced in connection with global warming and Arctic regions development.
关键词： solar batteries,distributed generation,shared use,grid-tied,land use
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Evaluation of the potentiality of polarimetric C- and L-SAR time-series images for the identification of winter land-use
摘要： Land cover and land use monitoring, particularly during winter season, is still a major environmental challenge. Indeed, the presence of a vegetation cover, the dates of sowing, the length of the intercrop period, and land use types have an impact on pollutant transport to water bodies. The objective of this study was to evaluate the potentiality of polarimetric C- and L-SAR time-series to improve the identification and characterization of vegetation cover during winter season in a 130 km2 area. Alos-2, Radarsat-2 and Sentinel-1 time-series were classified using RF algorithm. The best results were obtained from Radarsat-2 polarimetric images acquired between August 2016 and May 2017, with an overall accuracy of ~ 80%.
关键词： Alos-2,Polarimetric SAR,agricultural monitoring,land-use,Sentinel-1,Radarsat-2
更新于2025-09-23 15:22:29
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Urban Land-Use Classification From Photographs
摘要： Land-use (LU) classification of urban areas is conventionally achieved via field survey or remote sensing technologies, which is labor-intensive and time-consuming. With the wide development of social networks such as microblog and ubiquitous network access, images are captured by residents and tourists. In this letter, we propose a method for an automatic urban LU classification using geotagged images from public venues. Our method identifies the LU type depicted in those images that are extrapolated to the local regions bounded by street blocks. Experiments were conducted with geotagged photographs and Open Street Map of an urban area in London, U.K. It was demonstrated that the proposed method achieved overall 76.5% accuracy across five LU types. More importantly, our method demonstrated a greater performance in dealing with a mixture of LU types.
关键词： volunteered geographic information (VGI),Classification,land use (LU),urban
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Introducing Eurosat: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
摘要： In this paper, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The key contributions are as follows. We present a novel dataset based on Sentinel-2 satellite images covering 13 different spectral bands and consisting of 10 classes with in total 27,000 labeled images. We evaluate state-of-the-art deep Convolutional Neural Networks (CNNs) on this novel dataset with its different spectral bands. We also evaluate deep CNNs on existing remote sensing datasets and compare the obtained results. With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The classification system resulting from the proposed research opens a gate towards various Earth observation applications. We demonstrate how the classification system can assist in improving geographical maps.
关键词： Deep Learning,Land Use Classification,Earth Observation,Convolutional Neural Network,Machine Learning,Dataset,Land Cover Classification
更新于2025-09-23 15:21:21
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Land Use Change Detection Using Remote Sensing Technology
摘要： Background: Change detection is useful in many applications related to land use and land cover (LULC) changes, such as shifting cultivation and landscape changes, land degradation and desertification. Remotes sensing technology has been used for the detection of the change in land use land cover in upper Rib watershed. The main objective of this study was to detect the land use change using remote sensing for sustainable land use planning in Upper Rib watershed. Methodology: The two satellite images for the year 2007 and 2018 were downloaded and used for detecting the land cover changes. Maximum likelihood classification was used in ERDAS Imagine tool for classifying the images. Ground truth points were collected and used for verification of image classification. Results: The accuracy of image classification were checked using the Ground truth points and the has showed an overall accuracy of 84% and a kappa coefficient of 0.8 which indicates the method of classification and the images used were very good. During this study period an agricultural land has showed an increasing trend by 13.78%, while grassland had decreased by 15.97% due to an increase of interest to cropland area. Conclusion: In Upper Rib watershed, there has been a significant land use change which was due to an increase in population with a high interest to croplands which resulted in an increase of agricultural land by 13.78% over 11 years period.
关键词： Land use change,Upper Rib watershed,Remote sensing,ERDAS Imagine
更新于2025-09-23 15:21:21
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Ultrafast UV AlGaN Metala??Semiconductora??Metal Photodetector With a Response Time Below 25 ps
摘要： Land-use (LU) scene classification is one of the most challenging tasks in the field of remote sensing (RS) image processing due to its high intraclass variability and low interclass distance. Motivated by the challenge posed by this problem, we propose a novel hybrid architecture, deep filter banks, combining multicolumn stacked denoising sparse autoencoder (SDSAE) and Fisher vector (FV) to automatically learn the representative and discriminative features in a hierarchical manner for LU scene classification. SDSAE kernels describe local patches and a robust global feature of the RS image is built through the FV pooling layer. Unlike previous handcrafted features, we use machine-learning mechanisms to optimize our proposed feature extractor so that it can learn more suitable internal features from the RS data, boosting the final performance. Our approach achieves superior performance compared with the state-of-the-art methods, obtaining average classification accuracies of 92.7% and 90.4%, respectively, on the UC Merced and RSSCN7 data sets.
关键词： Fisher vector (FV),land-use (LU) scene classification,Deep filter banks,stacked denoising sparse autoencoder (SDSAE)
更新于2025-09-23 15:21:01