scholarly journals Identification of Hydrothermal Alteration Minerals for Exploring Gold Deposits Based on SVM and PCA Using ASTER Data: A Case Study of Gulong

2019 ◽  
Vol 11 (24) ◽  
pp. 3003
Author(s):  
Kai Xu ◽  
Xiaofeng Wang ◽  
Chunfang Kong ◽  
Ruyi Feng ◽  
Gang Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Ant Colony Algorithm ◽  
Principal Component ◽  
Resource Depletion ◽  
Gold Deposits ◽  
Support Vector ◽  
Mixed Pixel ◽  
Alteration Minerals ◽  
Metallogenic Prediction ◽  
Remote Sensing Methods

Dayaoshan, as an important metal ore-producing area in China, is faced with the dilemma of resource depletion due to long-term exploitation. In this paper, remote sensing methods are used to circle the favorable metallogenic areas and find new ore points for Gulong. Firstly, vegetation interference was removed by using mixed pixel decomposition method with hyperplane and genetic algorithm (GA) optimization; then, altered mineral distribution information was extracted based on principal component analysis (PCA) and support vector machine (SVM) methods; thirdly, the favorable areas of gold mining in Gulong was delineated by using the ant colony algorithm (ACA) optimization SVM model to remove false altered minerals; and lastly, field surveys verified that the extracted alteration mineralization information is correct and effective. The results show that the mineral alteration extraction method proposed in this paper has certain guiding significance for metallogenic prediction by remote sensing.

scholarly journals THE EFFECTIVE OF DIFFERENT EXCITATION WAVELENGTHS ON THE IDENTIFICATION OF PLANT SPECIES BASED ON FLUORESCENCE LIDAR

2016 ◽  
Vol XLI-B1 ◽  
pp. 147-150
Author(s):  
Jian Yang ◽  
Wei Gong ◽  
Shuo Shi ◽  
Lin Du ◽  
Jia Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Fluorescence Spectra ◽  
Principal Component ◽  
Laser Induced Fluorescence ◽  
Support Vector ◽  
Excitation Wavelength ◽  
Lidar System ◽  
Excitation Light

Laser-induced fluorescence (LIF) served as an active technology has been widely used in many field, and it is closely related to excitation wavelength (EW). The objective of this investigation is to discuss the performance of different EWs of LIF LiDAR in identifying plant species. In this study, the 355, 460 and 556 nm lasers were utilized to excite the leaf fluorescence and the fluorescence spectra were measured by using the LIF LiDAR system built in the laboratory. Subsequently, the principal component analysis (PCA) with the help of support vector machine (SVM) was utilized to analyse fluorescence spectra. For the three EWs, the overall identification rates of the six plant species were 80 %, 83.3 % and 90 %. Experimental results demonstrated that 556 nm excitation light source is superior to 355 and 460 nm for the classification of the plant species for the same genus in this study. Thus, an appropriate excitation wavelength should be considered when the LIF LiDAR was utilized in the field of remote sensing based on the LIF technology.

scholarly journals The Potential of LiDAR and UAV-Photogrammetric Data Analysis to Interpret Archaeological Sites: A Case Study of Chun Castle in South-West England

2021 ◽  
Vol 10 (1) ◽  
pp. 41
Author(s):  
Israa Kadhim ◽  
Fanar M. Abed
Keyword(s):  
Remote Sensing ◽  
Remotely Sensed ◽  
Airborne Lidar ◽  
Support Vector ◽  
Archaeological Remains ◽  
Remotely Sensed Data ◽  
Spatial Attributes ◽  
Aerial Vehicle ◽  
Remote Sensing Methods ◽  
Non Destructive

With the increasing demands to use remote sensing approaches, such as aerial photography, satellite imagery, and LiDAR in archaeological applications, there is still a limited number of studies assessing the differences between remote sensing methods in extracting new archaeological finds. Therefore, this work aims to critically compare two types of fine-scale remotely sensed data: LiDAR and an Unmanned Aerial Vehicle (UAV) derived Structure from Motion (SfM) photogrammetry. To achieve this, aerial imagery and airborne LiDAR datasets of Chun Castle were acquired, processed, analyzed, and interpreted. Chun Castle is one of the most remarkable ancient sites in Cornwall County (Southwest England) that had not been surveyed and explored by non-destructive techniques. The work outlines the approaches that were applied to the remotely sensed data to reveal potential remains: Visualization methods (e.g., hillshade and slope raster images), ISODATA clustering, and Support Vector Machine (SVM) algorithms. The results display various archaeological remains within the study site that have been successfully identified. Applying multiple methods and algorithms have successfully improved our understanding of spatial attributes within the landscape. The outcomes demonstrate how raster derivable from inexpensive approaches can be used to identify archaeological remains and hidden monuments, which have the possibility to revolutionize archaeological understanding.

scholarly journals EXPLORATION OF IOCG MINERALIZATIONS USING INTEGRATION OF SPACE-BORNE REMOTE SENSING DATA WITH AIRBORNE GEOPHYSICAL DATA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 9-16
Author(s):  
M. Abdolmaleki ◽  
T. M. Rasmussen ◽  
M. K. Pal
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Support Vector ◽  
Mineral Potential Mapping ◽  
Band Ratio ◽  
Sensing Data ◽  
Mineral Potential ◽  
Potential Mapping ◽  
Sentinel 2

Abstract. Nowadays, remote sensing technologies are playing a significant role in mineral potential mapping. To optimize the exploration approach along with a cost-effective way, narrow down the target areas for a more detailed study for mineral exploration using suitable data selection and accurate data processing approaches are crucial. To establish optimum procedures by integrating space-borne remote sensing data with other earth sciences data (e.g., airborne magnetic and electromagnetic) for exploration of Iron Oxide Copper Gold (IOCG) mineralization is the objective of this study. Further, the project focus is to test the effectiveness of Copernicus Sentinel-2 data in mineral potential mapping from the high Arctic region. Thus, Inglefield Land from northwest Greenland has been chosen as a study area to evaluate the developed approach. The altered minerals, including irons and clays, were mapped utilizing Sentinel-2 data through band ratio and principal component analysis (PCA) methods. Lineaments of the study area were extracted from Sentinel-2 data using directional filters. Self-Organizing Maps (SOM) and Support Vector Machines (SVM) were used for classification and analysing the available data. Further, various thematic maps (e.g., geological, geophysical, geochemical) were prepared from the study area. Finally, a mineral prospectively map was generated by integrating the above mentioned information using the Fuzzy Analytic Hierarchy Process (FAHP). The prepared potential map for IOCG mineralization using the above approach of Inglefield Land shows a good agreement with the previous geological field studies.

scholarly journals Remote Sensing Exploration of Fullerene Bearing Shungite in Mangampet Baryte Mines, India

2021 ◽  
Author(s):  
R. G. Rejith ◽  
M. Sundararajan ◽  
L. Gnanappazham ◽  
M. A. Mohammed‑Aslam ◽  
Sarika Verma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Photoelectron Spectroscopy ◽  
High Performance ◽  
Materials Science ◽  
Value Added ◽  
Principal Component ◽  
Maldi Mass Spectrometry ◽  
Support Vector ◽  
Carbon Allotrope ◽  
X Ray

Abstract The fullerene (C60) is economically significant due to its significant applications in diverse areas like nanochemistry, superconductivity, materials science, etc. The carbon allotrope fullerene C60 and C70 are identified within the thick sequence of black carbonaceous units belonging to the shungite suite of rock at the baryte mine, Mangampet, Andhra Pradesh. Since this is the first of its kind from India's geological material, the exploration of fullerenes enriched barytes from the mines at Mangampet in the Kadapa district is essential. In the present study, remote sensing techniques such as hyperspectral analysis followed by Mixture Tuned Matched Filtering (MTMF) and Support Vector Machine (SVM), and Principal Component Analysis (PCA) were used for exploring the presence of fullerene bearing baryte deposits in the Mangampet mine. The spectra measured for baryte samples were used as reference spectra for deriving true endmember from Landsat OLI and ASTER satellite data. The detailed characterisation of structure and chemistry of the baryte samples were carried out using techniques like Energy Dispersive X-ray Fluorescence (EDXRF), X-ray Powder Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), and Fourier-Transform Infrared Spectroscopy (FTIR) techniques. The High-Performance Liquid Chromatography (HPLC) and Matrix-Assisted Laser Desorption/Ionization (MALDI) mass spectrometry analyses confirm fullerene presence in baryte deposits. The retention time at 11.783–11.822 min obtained from HPLC and high-intensity rate m/z at 720.178 obtained from MALDI spectra suggest fullerene in baryte mine deposits. The remote sensing exploration of fullerene bearing shungite suite of rocks in baryte mineral deposits of Mangampet mine has opened up new research areas for converting this material into value-added products.

scholarly journals Improved Classification of Mangroves Health Status Using Hyperspectral Remote Sensing Data

2014 ◽  
Vol XL-8 ◽  
pp. 667-670 ◽  
Author(s):  
R. Vidhya ◽  
D. Vijayasekaran ◽  
M. Ahamed Farook ◽  
S. Jai ◽  
M. Rohini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Health Status ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Spectral Angle Mapper ◽  
Svm Classification ◽  
Sensing Data

Mangrove ecosystem plays a crucial role in costal conservation and provides livelihood supports to humans. It is seriously affected by the various climatic and anthropogenic induced changes. The continuous monitoring is imperative to protect this fragile ecosystem. In this study, the mangrove area and health status has been extracted from Hyperspectral remote sensing data (EO- 1Hyperion) using support vector machine classification (SVM). The principal component transformation (PCT) technique is used to perform the band reduction in Hyperspectral data. The soil adjusted vegetation Indices (SAVI) were used as additional parameters. The mangroves are classified into three classes degraded, healthy and sparse. The SVM classification is generated overall accuracy of 73 % and kappa of 0.62. The classification results were compared with the results of spectral angle mapper classification (SAM). The SAVI also included in SVM classification and the accuracy found to be improved to 82 %. The sparse and degraded mangrove classes were well separated. The results indicate that the mapping of mangrove health is accurate when the machine learning classifier like SVM combined with different indices derived from hyperspectral remote sensing data.

scholarly journals THE EFFECTIVE OF DIFFERENT EXCITATION WAVELENGTHS ON THE IDENTIFICATION OF PLANT SPECIES BASED ON FLUORESCENCE LIDAR

2016 ◽  
Vol XLI-B1 ◽  
pp. 147-150 ◽  
Author(s):  
Jian Yang ◽  
Wei Gong ◽  
Shuo Shi ◽  
Lin Du ◽  
Jia Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Fluorescence Spectra ◽  
Principal Component ◽  
Laser Induced Fluorescence ◽  
Support Vector ◽  
Excitation Wavelength ◽  
Lidar System ◽  
Excitation Light

Laser-induced fluorescence (LIF) served as an active technology has been widely used in many field, and it is closely related to excitation wavelength (EW). The objective of this investigation is to discuss the performance of different EWs of LIF LiDAR in identifying plant species. In this study, the 355, 460 and 556 nm lasers were utilized to excite the leaf fluorescence and the fluorescence spectra were measured by using the LIF LiDAR system built in the laboratory. Subsequently, the principal component analysis (PCA) with the help of support vector machine (SVM) was utilized to analyse fluorescence spectra. For the three EWs, the overall identification rates of the six plant species were 80 %, 83.3 % and 90 %. Experimental results demonstrated that 556 nm excitation light source is superior to 355 and 460 nm for the classification of the plant species for the same genus in this study. Thus, an appropriate excitation wavelength should be considered when the LIF LiDAR was utilized in the field of remote sensing based on the LIF technology.

scholarly journals Lithological mapping using remote sensing techniques: A case study of Alagbayan area, Dornogobi province, Mongolia

2021 ◽  
Vol 26 (53) ◽  
pp. 37-54
Author(s):  
Badrakh Munkhsuren ◽  
Batkhuyag Enkhdalai ◽  
Tserendash Narantsetseg ◽  
Khurelchuluun Udaanjargal ◽  
Demberel Orolmaa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Principal Component ◽  
Granite Gneiss ◽  
Support Vector ◽  
Landsat 8 ◽  
Landsat Images ◽  
Band Ratio ◽  
Lithological Mapping ◽  
Remote Sensing Techniques ◽  
Thin Section Analysis

This study investigated the multispectral remote sensing techniques including ASTER, Landsat 8 OLI, and Sentinel 2A data in order to distinguish different lithological units in the Alagbayan area of Dornogobi province, Mongolia. Therefore, Principal component analysis (PCA), Band ratio (BR), and Support Vector Machine (SVM), which are widely used image enhancement methods, have been applied to the satellite images for lithological mapping. The result of supervised classification shows that Landsat data gives a better classification with an overall accuracy of 93.43% and a kappa coefficient of 0.92 when the former geologic map and thin section analysis were chosen as a reference for training samples. Moreover, band ratios of ((band 7 + band 9)/band 8) obtained from ASTER corresponds well with carbonate rocks. According to PCs, PC4, PC3 and PC2 in the RGB of Landsat, PC3, PC2, PC6 for ASTER data are chosen as a good indicator for different lithological units where Silurian, Carboniferous, Jurassic, and Cretaceous formations are easily distinguished. In terms of Landsat images, the most efficient BR was a ratio where BRs of 5/4 for alluvium, 4/7 for schist and 7/6 to discriminate granite. In addition, as a result of BR as well as PCA, Precambrian Khutag-Uul metamorphic complex and Norovzeeg formation can be identified but granite-gneiss and schist have not given satisfactory results.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

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