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Tytuł:
Restoration of Remote Satellite Sensing Images using Machine and Deep Learning : a Survey
Autorzy:
Abdellaoui, Meriem
Benabdelkader, Souad
Assas, Ouarda
Powiązania:
https://bibliotekanauki.pl/articles/31339413.pdf
Data publikacji:
2023
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
Tematy:
image restoration
remote sensing images
artificial intelligence
AI
machine learning
ML
deep learning
DL
convolutional neural network
CNN
Opis:
Remote sensing satellite images are affected by different types of degradation, which poses an obstacle for remote sensing researchers to ensure a continuous and trouble-free observation of our space. This degradation can reduce the quality of information and its effect on the reliability of remote sensing research. To overcome this phenomenon, the methods of detecting and eliminating this degradation are used, which are the subject of our study. The original aim of this paper is that it proposes a state of art of recent decade (2012-2022) on advances in remote sensing image restoration using machine and deep learning, identified by this survey, including the databases used, the different categories of degradation, as well as the corresponding methods. Machine learning and deep learning based strategies for remote sensing satellite image restoration are recommended to achieve satisfactory improvements.
Źródło:
Machine Graphics & Vision; 2023, 32, 2; 147-167
1230-0535
2720-250X
Pojawia się w:
Machine Graphics & Vision
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Satellite Image Fusion Using a Hybrid Traditional and Deep Learning Method
Autorzy:
Hammad, Mahmoud M.
Mahmoud, Tarek A.
Amein, Ahmed Saleh
Ghoniemy, Tarek S.
Powiązania:
https://bibliotekanauki.pl/articles/27314300.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
deep learning image fusion
remote sensing image fusion
remote sensing optical image
pan-sharpening
remote sensing image
Opis:
Due to growing demand for ground-truth in deep learning-based remote sensing satellite image fusion, numerous approaches have been presented. Of these approaches, Wald’s protocol is the most commonly used. In this paper, a new workflow is proposed consisting of two main parts. The first part targets obtaining the ground-truth images using the results of a pre-designed and well-tested hybrid traditional fusion method. This method combines the Gram–Schmidt and curvelet transform techniques to generate accurate and reliable fusion results. The second part focuses on the training of a proposed deep learning model using rich and informative data provided by the first stage to improve the fusion performance. The demonstrated deep learning model relies on a series of residual dense blocks to enhance network depth and facilitate the effective feature learning process. These blocks are designed to capture both low-level and high-level information, enabling the model to extract intricate details and meaningful features from the input data. The performance evaluation of the proposed model is carried out using seven metrics such as peak-signal-to-noise-ratio and quality without reference. The experimental results demonstrate that the proposed approach outperforms state-of-the-art methods in terms of image quality. It also exhibits the robustness and powerful nature of the proposed approach which has the potential to be applied to many remote sensing applications in agriculture, environmental monitoring, and change detection.
Źródło:
Geomatics and Environmental Engineering; 2023, 17, 5; 145--162
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Use of Spatial Remote Sensing to Study the Temporal Evolution of the Water Retention of Al Massira Dam in Morocco
Autorzy:
Bounif, Mohammed
Rahimi, Abdelmejid
Boutafoust, Rachid
El Mjiri, Ikram
Powiązania:
https://bibliotekanauki.pl/articles/2202368.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
spatial remote sensing
Al Massira dam reservoir
spectral indices
Landsat
satellite image
Doukkala
irrigated perimeter
Opis:
In Morocco, irrigated agriculture is still very much linked to the climate and the water retention of dams. With climate change, this country is experiencing recurrent drought, which has led to deficits in water inflow from the rivers to the various dams. The Al Massira dam, the area of study, does not escape this trend. This dam is the only surface water source for the irrigated area of Doukkala. Therefore, special attention must be paid to monitoring this resource at this dam. Thus, the proposed study examined the possibilities offered by spatial remote sensing to improve the current information system. It aims to evaluate this dam’s reservoir by exploiting the data generated by using satellite images. The Landsat satellite images were used to assess the area of this dam by adopting an approach combining spectral indices with thresholding. Then, the existing relationship between the area of the dam lake were examined, determined by spatial remote sensing and its water retention measured in situ. The results obtained revealed a strong correlation between the two parameters. Therefore, a study was conducted to find the best model for predicting the dam’s impoundment based on its lake. The second-degree polynomial model showed a better performance. Given the results obtained, it is recommended to use geospatial methods in the current and prospective monitoring and steering system of water resources.
Źródło:
Journal of Ecological Engineering; 2023, 24, 2; 340--349
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Machine Learning Model for Improving Building Detection in Informal Areas: A Case Study of Greater Cairo
Autorzy:
Taha, Lamyaa Gamal El-deen
Ibrahim, Rania Elsayed
Powiązania:
https://bibliotekanauki.pl/articles/2055780.pdf
Data publikacji:
2022
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
multi-source image fusion
random forest
support vector machine
DEM extraction
unplanned unsafe areas
remote sensing
Opis:
Building detection in Ashwa’iyyat is a fundamental yet challenging problem, mainly because it requires the correct recovery of building footprints from images with high-object density and scene complexity. A classification model was proposed to integrate spectral, height and textural features. It was developed for the automatic detection of the rectangular, irregular structure and quite small size buildings or buildings which are close to each other but not adjoined. It is intended to improve the precision with which buildings are classified using scikit learn Python libraries and QGIS. WorldView-2 and Spot-5 imagery were combined using three image fusion techniques. The Grey-Level Co-occurrence Matrix was applied to determine which attributes are important in detecting and extracting buildings. The Normalized Digital Surface Model was also generated with 0.5-m resolution. The results demonstrated that when textural features of colour images were introduced as classifier input, the overall accuracy was improved in most cases. The results show that the proposed model was more accurate and efficient than the state-of-the-art methods and can be used effectively to extract the boundaries of small size buildings. The use of a classifier ensample is recommended for the extraction of buildings.
Źródło:
Geomatics and Environmental Engineering; 2022, 16, 2; 39--58
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Discrimination different lithological units using a remote sensing application: A case study in the Dokan Area, Kurdistan Region - Iraq
Autorzy:
Bety, Azhar Kh. S.
Powiązania:
https://bibliotekanauki.pl/articles/2174298.pdf
Data publikacji:
2022
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
Dokan Area
image enhancement
image transformation
lithological units
rapid eye
remote sensing
Opis:
This study discriminates different lithological units of the Dokan Area, Kurdistan Region, NE-Iraq, using rapid-eye satellite data by image enhancement techniques, namely the false colour composite (FCC), optimum index factor (OIF), minimum noise fraction (MNF), principal component analysis (PCA) and band ratio (BR). Results of analyses show that the FCC (R: 5; G: 4: B: 1); MNF (R: 2, G: 3, B: 5); PCA (R: 5, G: 2, B: 1), and band ratio (R: 5/4, G: 2/1, B: 5/3) are the best to different geological formations. The results are confirmed in the field support with the geological maps available for the area. Geological formations appeared as a result of the collision process between the Arabian plate and the Iranian plate. In general, the study area is mountainous, which is usually represented by anticline folds with the main NW - SE trend in the study area, with very a rugged relief mainly due to the continuous collision between the Arabian plate and Iranian plate. The digital image processing of satellite data has demonstrated the sensor’s capability and efficiency of the image processing methods in identifying and mapping geological units in the study area.
Źródło:
Journal of Water and Land Development; 2022, 55; 109--114
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Methodology for determining deforestation areas in Lviv region using remote sensing data
Autorzy:
Chetverikov, Borys
Trevoho, Ihor
Babiy, Lubov
Malanchuk, Mariia
Powiązania:
https://bibliotekanauki.pl/articles/43852817.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
teledetekcja
monitoring satelitarny
obraz satelitarny
Landsat 8
remote sensing
space image
satellite monitoring system
Opis:
The object of the study is the processing of space images on the territory of the Carpathian territory in the Lviv region, obtained from the Landsat-8 satellite. The work aims to determine the area of deforestation in the Carpathian territory of the Lviv region from different time-space images obtained from the Landsat-8 satellite. Methods of cartography, photogrammetry, aerospace remote sensing of the Earth and GIS technology were used in the experimental research. The work was performed in Erdas Imagine software using the unsupervised image classification module and the DeltaCue difference detection module. The results of the work are classified as three images of Landsat-8 on the territory of the Carpathian territory in the Lviv region. The areas of forest cover for each of them for the period of 2016-2018 have been determined. During the three years, the area of forests has decreased by 14 hectares. Our proposed workflow includes six stages: analysis of input data, band composition of space images on the research territory, implementation of unsupervised classification in Erdas Imagine software and selection of forest class and determination of implementing this workflow, the vector layers of the forest cover of the Carpathians in the Lviv region for 2016, 2017, 2018 were obtained, and on their basis, the corresponding areas were calculated and compared.
Źródło:
Advances in Geodesy and Geoinformation; 2022, 71, 1; art. no. e21, 2022
2720-7242
Pojawia się w:
Advances in Geodesy and Geoinformation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
On the applicability of integrated UAV photogrammetry and automatic feature extraction for cadastral mapping
Autorzy:
Ajayi, Oluibukun Gbenga
Oruma, Emmanuel
Powiązania:
https://bibliotekanauki.pl/articles/43852813.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
zarządzanie gruntami
segmentacja obrazu
mapowanie
land management
remote sensing applications
image segmentation
automatic boundary extraction
UAV mapping
Opis:
The applicability of integratedUnmannedAerialVehicle (UAV)-photogrammetry and automatic feature extraction for cadastral or property mapping was investigated in this research paper. Multi-resolution segmentation (MRS) algorithm was implemented on UAVgenerated orthomosaic for mapping and the findings were compared with the result obtained from conventional ground survey technique using Hi-Target Differential Global Positioning System (DGPS) receivers. The overlapping image pairs acquired with the aid of a DJI Mavic air quadcopter were processed into an orthomosaic using Agisoft metashape software while MRS algorithm was implemented for the automatic extraction of visible land boundaries and building footprints at different Scale Parameter (SPs) in eCognition developer software. The obtained result shows that the performance of MRS improves with an increase in SP, with optimal results obtained when the SP was set at 1000 (with completeness, correctness, and overall accuracy of 92%, 95%, and 88%, respectively) for the extraction of the building footprints. Apart from the conducted cost and time analysis which shows that the integrated approach is 2.5 times faster and 9 times cheaper than the conventional DGPS approach, the automatically extracted boundaries and area of land parcels were also compared with the survey plans produced using the ground survey approach (DGPS) and the result shows that about 99% of the automatically extracted spatial information of the properties fall within the range of acceptable accuracy. The obtained results proved that the integration of UAVphotogrammetry and automatic feature extraction is applicable in cadastral mapping and that it offers significant advantages in terms of project time and cost.
Źródło:
Advances in Geodesy and Geoinformation; 2022, 71, 1; art. no. e19, 2022
2720-7242
Pojawia się w:
Advances in Geodesy and Geoinformation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Estimation of Suspended Sediment Concentration in Downstream of the Ba River Basin using Remote Sensing Images
Autorzy:
Nguyen, Ba Dung
Bui, Ngoc An
Dang, Tuyet Minh
Powiązania:
https://bibliotekanauki.pl/articles/2020132.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Przeróbki Kopalin
Tematy:
remote sensing image
suspended sediment concentration
downstream of the Ba river basin
teledetekcja
stężenie zawiesiny
Wietnam
Opis:
Assessing the tendency of suspended sediment concentration (SSC) in the river watersheds enables a better understanding of the hydromorphological properties of its basins and the associated processes. In addition, analyzing this trend is essential to address several important issues such as erosion, water pollution, human health risks, etc. Therefore, it is critical to determine a proper method to quantify spatio-temporal variability in SSC. In recent years, remote sensing and GIS technologies are being widely applied to support scientists, researchers, and environmental resource investigators to quickly and synchronously capture information on a large scale. The combination of remote sensing and GIS data will become the reliable and timely updated data source for the managers, researchers on many fields. There are several tools, software, algorithms being used in extracting information from satellites and support for the analysis, image interpretation, data collection. The information from satellite images related to water resources includes vegetational cover, flooding events on a large scale, rain forecast, population distribution, forest fire, landslide movements, sedimentation, etc., and especially information on water quality, sediment concentration. This paper presents the initial result from LANDSAT satellite image interpretation to investigate the amount of sediment carried downstream of the Ba river basin.
Źródło:
Inżynieria Mineralna; 2021, 2; 293--303
1640-4920
Pojawia się w:
Inżynieria Mineralna
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Extraction of urban construction development with using Landsat satellite images and geoinformation systems
Autorzy:
Arifjanov, Aybek M.
Akmalov, Shamshodbek B.
Samiev, Luqmon N.
Powiązania:
https://bibliotekanauki.pl/articles/1844383.pdf
Data publikacji:
2021
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
ArcGIS
geographic information system
GIS
Landsat satellite image
remote sensing
RS
urban
Opis:
In recent times there have been many changes on Earth, which have appeared after anthropogenic impact. Finding solutions to problems in the environment requires studying the problems quickly, make proper conclusions and creating safe and useful measures. Humanity has always had an effect on the environment. There can be many changes on the Earth because of direct and indirect effects of humans on nature. Determining these changes at the right time and organizing meas-urements of them requires the creation of quick analysing methods. This development has improved specialists’ interest for remote sensing (RS) imagery. Moreover, in accordance with analysis of literature sources, agriculture, irrigation and ecology have the most demand for RS imagery. This article is about using geographic information system (GIS) and RS technologies in cadastre and urban construction branches. This article covers a newly created automated method for the calculation of artificial surface area based on satellite images. Accuracy of the analysis is verified according to the field experiments. Accuracy of analysis is 95%. According to the analysis from 1972 to 2019 artificial area enlargement is 13.44%. This method is very simple and easy to use. Using this data, the analysis method can decrease economical costs for field measures. Using this method and these tools in branches also allows for greater efficiency in time and resources.
Źródło:
Journal of Water and Land Development; 2021, 48; 65-69
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Performance of Unsupervised Change Detection Method Based on PSO and K-means Clustering for SAR Images
Autorzy:
Shehab, Jinan N.
Abdulkadhim, Hussein A.
Powiązania:
https://bibliotekanauki.pl/articles/1844494.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
change detection
k-means clustering
multitemporal satellite image
PSO
Gabor wavelet filter
remote sensing
Opis:
This paper presents unsupervised change detection method to produce more accurate change map from imbalanced SAR images for the same land cover. This method is based on PSO algorithm for image segmentation to layers which classify by Gabor Wavelet filter and then K-means clustering to generate new change map. Tests are confirming the effectiveness and efficiency by comparison obtained results with the results of the other methods. Integration of PSO with Gabor filter and k-means will providing more and more accuracy to detect a least changing in objects and terrain of SAR image, as well as reduce the processing time.
Źródło:
International Journal of Electronics and Telecommunications; 2021, 67, 3; 403-408
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Study of Land Cover Change Detection in Oddusuddan DS Division of Mullaitivu District in Sri Lanka Based on GIS and RS Technology
Autorzy:
Pathmanandakumar, V.
Powiązania:
https://bibliotekanauki.pl/articles/1031751.pdf
Data publikacji:
2020
Wydawca:
Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
Tematy:
Change Matrix
Change detection
Geographical Information System & Remote Sensing
Land covers Mapping
Satellite Image Analysis
Opis:
Land cover change analysis between 1997 and 2016 was conducted in Oddusuddan Divisional Secretariat, Mullaitivu District, using remote sensing and geographic information system incorporated with field verifications. Various Satellite images and different digital maps have been used for extracting information. The overall objective of this study was to detect the magnitude of land cover change in Oddusuddan between 1997 and 2016. The methodology of this study was a change detection analysis of satellite imagery with Landsat ETM data. Two dates of Landsat image data of the 1997 and 2016 were used to produce a land cover map. The Maximum Likelihood algorithm was used for supervised classification to detect changes for twenty years. The result showed that during the last twenty years, the forest cover declined from 453.02 km2 in 1997 to 447.14 km2 in 2016. It was noticed that socio-economic factors were the major driving forces for the land cover change.
Źródło:
World News of Natural Sciences; 2020, 29, 3; 198-211
2543-5426
Pojawia się w:
World News of Natural Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Klasyfikacja pokrycia terenu z wykorzystaniem obrazów Sentinel-2A przetworzonych za pomocą metody głównych składowych (PCA)
Land cover classification using Sentinel-2A images processed by the principal components method (PCA)
Autorzy:
Kałużna, Urszula
Będkowski, Krzysztof
Powiązania:
https://bibliotekanauki.pl/articles/2058371.pdf
Data publikacji:
2020
Wydawca:
Polskie Towarzystwo Geograficzne
Tematy:
teledetekcja
pokrycie terenu
EGiB
Sentinel-2A
PCA
nadzorowana klasyfikacja obrazu
remote sensing
land cover
Land and Buildings Register
supervised image classification
Opis:
Celem badań jest ocena możliwości realizacji klasyfikacji nadzorowanej z wykorzystaniem obrazów (komponentów) uzyskiwanych w wyniku przetworzenia oryginalnych obrazów Sentinel-2A za pomocą metody głównych składowych (PCA). Klasyfikację wykonano w ośmiu wariantach, z wykorzystaniem algorytmów najmniejszej odległości (MD, Minimum Distance) oraz największego prawdopodobieństwa (ML, Maximum Likelihood), przy czym zastosowano oryginalne kanały 2, 3, 4, 8 Sentinel-2A oraz różną liczbę komponentów. Wyniki klasyfikacji oceniono poprzez porównanie z danymi o pokryciu terenu według Ewidencji Gruntów i Budynków (EGiB). Przeprowadzenie klasyfikacji na ograniczonej do dwóch liczbie komponentów uzyskanych w procedurze PCA tylko nieznacznie zmieniło wyniki w porównaniu do klasyfikacji na oryginalnych, nieprzetworzonych kanałach Sentinel-2A. Najbardziej zbliżone do danych EGiB rezultaty uzyskano stosując klasyfikację ML kanałów oryginalnych, nieprzetworzonych lub używając wszystkich komponentów PCA. Podjęta próba porównania pokrycia terenu ustalonego za pomocą klasyfikacji obrazów satelitarnych z klasami pokrycia, które zostały wyodrębnione z mapy EGiB wykazała, że przetworzenie mapy z postaci wektorowej na rastrową wpływa istotnie na uzyskiwane wyniki.
The aim of the research is to assess the feasibility of supervised classification using images (components) obtained through processing the original Sentinel-2A images by means of the principal component method (PCA). The classification was performed in eight variants, using the algorithms of the minimum distance (MD) and the maximum likelihood (ML), with the original channels 2, 3, 4, 8 of Sentinel-2A and a various number of components. The results of the classification were assessed by comparing them to the land coverage data of Land and Buildings Register (Ewidencja Gruntów i Budynków – EGiB). Performing the classification on a number of PCA components limited to two only slightly altered the results compared to the classification on the original, raw Sentinel-2A channels. The results most similar to the EGiB data were obtained using the ML classification of the original channels, i.e. raw channels or using all PCA components. The attempt to compare the land coverage established by the classification of satellite images to the coverage classes that were extracted from the EGiB map revealed that processing the map from vector to raster form significantly influences the obtained results.
Źródło:
Teledetekcja Środowiska; 2020, 61; 19-37
1644-6380
Pojawia się w:
Teledetekcja Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of land use/land cover change in Adei watershed, Central Highlands of Ethiopia
Analiza użytkowania i pokrycia terenu w zlewni Adei na Wyżynie Centralnej w Etiopii
Autorzy:
Dinka, Megersa Olumana
Chaka, Degefa Dhuga
Powiązania:
https://bibliotekanauki.pl/articles/292985.pdf
Data publikacji:
2019
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
change analysis
GIS
image analysis
land use and land cover
remote sensing
analiza obrazów
analiza zmian
teledetekcja
użytkowanie i pokrycie terenu
Opis:
Land use/land cover changes (LULCC) at Adei watershed (Ethiopia) over a period of 23 years (1986–2009) has been analysed from LANDSAT imagery and ancillary data. The patterns (magnitude and direction) of LULCC were quantified and the final land use/land cover maps were produced after a supervised classification with appropriate post-processing. Image analysis results revealed that the study area has undergone substantial LULCC, primarily a shift from natural cover into managed agro-systems, which is apparently attributed to the increasing both human and livestock pressure. Over the 23 years, the aerial coverage of forest and grass lands declined by 8.5% and 4.3%, respectively. On the other hand, agricultural and shrub lands expanded by 9.1% and 3.7%, respectively. This shows that most of the previously covered by forest and grass lands are mostly shifted to the rapidly expanding farm land use classes. The findings of this study suggested that the rate of LULCC over the study period, particularly deforestation due to the expansion of farmland need to be given due attention to maintain the stability and sustainability of the ecosystem.
Zmiany użytkowania i sposobu pokrycia terenu w zlewni Adei (Etiopia) analizowano w ciągu 23 lat (1986–2009) z użyciem obrazów LANDSAT i dodatkowych danych. Oceniono ilościowo schemat zmian (wielkość i kierunek) oraz wykonano mapy użytkowania i pokrycia terenu po odpowiednim przetworzeniu danych. Analiza obrazów ujawniła, że badany obszar podlegał znaczącym zmianom – głównie od naturalnego pokrycia do gospodarczych agrosystemów, co wynikało z rosnącej presji ze strony człowieka i zwierząt gospodarskich. W ciągu 23 lat powierzchnie leśne i trawiaste zmalały odpowiednio o 8,5 i 4,3%, a powierzchnie użytkowane rolniczo i tereny zakrzaczone powiększyły się odpowiednio o 9,1 i 3,7%. Oznacza to, że tereny uprzednio zajmowane przez lasy i systemy trawiaste zostały zajęte przez tereny rolnicze. Przeprowadzone badania sugerują, że należy zwrócić szczególną uwagę na szybkie zmiany pokrycia powierzchni terenu, aby utrzymać stabilność i trwałość ekosystemu.
Źródło:
Journal of Water and Land Development; 2019, 41; 146-153
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Determination of iron minerals with landsat ETM+, Kırşehir, Turkey
Wykorzystanie danych teledetekcyjnych do identyfikacji złóż żelaza z Landsat ETM+, Kırşehir, Turcja
Autorzy:
Basibuyuk, Z.
Ekdur, E.
Powiązania:
https://bibliotekanauki.pl/articles/216804.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN
Tematy:
dane teledetekcyjne
przetwarzanie obrazu
mapowanie mineralne
hematyt
getyt
remote sensing
image processing
mineral mapping
hematite
goethite
Opis:
Image processing techniques (band rationing, color composite, Principal Component Analyses) are widely used by many researchers to describe various mines and minerals. The primary aim of this study is to use remote sensing data to identify iron deposits and gossans located in Kaman, Kırşehir region in the central part of Anatolia, Turkey. Capability of image processing techniques is proved to be highly useful to detect iron and gossan zones. Landsat ETM+ was used to create remote sensing images with the purpose of enhancing iron and gossan detection by applying ArcMap image processing techniques. The methods used for mapping iron and gossan area are 3/1 band rationing, 3/5 : 1/3 : 5/7 color composite, third PC and PC4 : PC3 : PC2 as RG B which obtained result from Standard Principal Component Analysis and third PC which obtained result from Developed Selected Principal Component Analyses (Crosta Technique), respectively. Iron-rich or gossan zones were mapped through classification technique applied to obtained images. Iron and gossan content maps were designed as final products. These data were confirmed by field observations. It was observed that iron rich and gossan zones could be detected through remote sensing techniques to a great extent. This study shows that remote sensing techniques offer significant advantages to detect iron rich and gossan zones. It is necessary to confirm the iron deposites and gossan zones that have been detected for the time being through field observations.
Głównym celem tego artykułu jest wykorzystanie danych teledetekcyjnych do identyfikacji złóż żelaza i gossan (rdzawe tlenkowe i wodorotlenkowe minerały żelaza i manganu, które występują nad złożem rudy) znajdujących się w Kaman, w regionie Kırşehir, w centralnej części Anatolii, w Turcji. Udowodniono, że możliwości przetwarzania obrazów są bardzo użyteczne w wykrywaniu stref żelaza i gossan. Landsat ETM+ został użyty do stworzenia obrazów teledetekcyjnych w celu poprawy wykrywania złóż żelaza i gossan poprzez zastosowanie ArcMap technik przetwarzania obrazu. Metody mapowania złóż żelaza i gossan stosują proporcje pasma 3/1, złożoność koloru 3/5: 1/3: 5/7, trzeci główny składnik PC (Principal Component) uzyskany w wyniku Developed Selected PCA (Crosta Technique) i proporcje PC4: PC3: PC2 jako RG B uzyskane w wyniku standardowej analizy głównych składowych PCA (Principal Component Analysis). Strefy bogate w żelazo lub strefy gossan zostały odwzorowane za pomocą techniki klasyfikacji zastosowanej do uzyskanych obrazów. Mapy zawartości żelaza i gossan zaprojektowano jako produkty końcowe. Dane te zostały potwierdzone w obserwacjach terenowych. Zaobserwowano, że strefy bogate w żelazo i strefy gossan mogą być w dużym stopniu wykrywane za pomocą technik teledetekcji. Badanie to pokazuje, że techniki teledetekcji dają znaczne korzyści w wykrywaniu stref bogatych w żelazo i gossan; jednak koniecznie należy potwierdzić wykryte złoża żelaza za pomocą obserwacji terenowych.
Źródło:
Gospodarka Surowcami Mineralnymi; 2018, 34, 3; 23-36
0860-0953
Pojawia się w:
Gospodarka Surowcami Mineralnymi
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Klasyfikacja zorientowana obiektowo w inwentaryzacji obiektów Zielonej Infrastruktury na przykładzie dzielnicy Ursynów w Warszawie
Object-oriented classification in the inventory of Green Infrastructure objects on the example of the Ursynów district in Warsaw
Autorzy:
Pyra, M.
Adamczyk, J.
Powiązania:
https://bibliotekanauki.pl/articles/132279.pdf
Data publikacji:
2018
Wydawca:
Polskie Towarzystwo Geograficzne
Tematy:
teledetekcja
klasyfikacja obiektowa
zielona infrastruktura
planowanie przestrzenne
remote sensing
Geographic Object-Based Image Analysis
green infrastructure
spatial management
Opis:
Zielona Infrastruktura jest koncepcją zintegrowanego podejścia do funkcjonalnego i przestrzennie powiązanego planowania obszarów zurbanizowanych wraz z ochroną elementów środowiska, która na przestrzeni ostatnich lat została doceniona przez podmioty odpowiedzialne za planowanie przestrzenne. Niniejsza praca przedstawia możliwości wykorzystania przetworzeń zobrazowań satelitarnych metodami klasyfikacji obiektowej w inwentaryzacji, planowaniu i monitorowaniu obiektów Zielonej Infrastruktury. Do tego celu wykorzystano zobrazowanie satelitarne pozyskane przez satelitę Pleiades w maju 2012 roku, reprezentujące obszar części dzielnicy Ursynów m.st. Warszawy. Wykorzystane w pracy metody klasyfikacji obiektowej wykazały wysoką efektywność w realizacji założonych zadań.
Green Infrastructure is a conception of an integrated approach to functional and spatially related planning of urban areas, along with environmental protection, which in recent years has been appreciated by spatial planning specialists. This study presents the capabilities of using satellite image processing with Geographic Object-Based Image Analysis methods in the inventory, planning and monitoring of Green Infrastructure objects. For this purpose, a satellite image acquired by the Pleiades satellite in May 2012, representing the area of a part of the Ursynów district of the capital city of Warsaw, was used. The object-oriented classification methods used in this work showed high effectiveness in the implementation of the tasks defined.
Źródło:
Teledetekcja Środowiska; 2018, 59; 29-49
1644-6380
Pojawia się w:
Teledetekcja Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 38

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