scholarly journals Using geochemical imaging data to map nickel sulfide deposits in Daxinganling, China

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Xiaoyan Chen ◽  
Jiang Chen ◽  
Jun Pan
Keyword(s):  
Nickel Sulfide ◽  
Spectral Feature ◽  
Geochemical Data ◽  
Ultramafic Rocks ◽  
Imaging Data ◽  
Spectral Angle Mapper ◽  
Sulfide Deposits ◽  
Feature Fitting ◽  
Prospectivity Mapping ◽  
Spectral Angle

AbstractNickel sulfide deposits occur in ultramafic rocks in the Daxinganling area, China; however, the prospectivity of these deposits has received little attention. This study transformed rasterized regional 1:200,000 geochemical data into spectral-like data and then used hyperspectral tools of the spectral angle mapper (SAM) to classify possible ultramafic lithologies and the multirange spectral feature fitting (MRSFF) method to classify prospective areas that are similar to a typical Gaxian Ni deposit. The prospective area map generated by the MRSFF implies the possible occurrence of ultramafic rocks classified by the SAM method. These results confirm the suitability of this innovative approach for prospectivity mapping of Ni sulfide deposits.

scholarly journals Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs)

2020 ◽  
Vol 12 (3) ◽  
pp. 408
Author(s):  
Małgorzata Krówczyńska ◽  
Edwin Raczko ◽  
Natalia Staniszewska ◽  
Ewa Wilk
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Spectral Feature ◽  
Hyperspectral Data ◽  
Aerial Photographs ◽  
Support Vector ◽  
Spectral Angle Mapper ◽  
Asbestos Cement ◽  
Feature Fitting

Due to the pathogenic nature of asbestos, a statutory ban on asbestos-containing products has been in place in Poland since 1997. In order to protect human health and the environment, it is crucial to estimate the quantity of asbestos–cement products in use. It has been evaluated that about 90% of them are roof coverings. Different methods are used to estimate the amount of asbestos–cement products, such as the use of indicators, field inventory, remote sensing data, and multi- and hyperspectral images; the latter are used for relatively small areas. Other methods are sought for the reliable estimation of the quantity of asbestos-containing products, as well as their spatial distribution. The objective of this paper is to present the use of convolutional neural networks for the identification of asbestos–cement roofing on aerial photographs in natural color (RGB) and color infrared (CIR) compositions. The study was conducted for the Chęciny commune. Aerial photographs, each with the spatial resolution of 25 cm in RGB and CIR compositions, were used, and field studies were conducted to verify data and to develop a database for Convolutional Neural Networks (CNNs) training. Network training was carried out using the TensorFlow and R-Keras libraries in the R programming environment. The classification was carried out using a convolutional neural network consisting of two convolutional blocks, a spatial dropout layer, and two blocks of fully connected perceptrons. Asbestos–cement roofing products were classified with the producer’s accuracy of 89% and overall accuracy of 87% and 89%, depending on the image composition used. Attempts have been made at the identification of asbestos–cement roofing. They focus primarily on the use of hyperspectral data and multispectral imagery. The following classification algorithms were usually employed: Spectral Angle Mapper, Support Vector Machine, object classification, Spectral Feature Fitting, and decision trees. Previous studies undertaken by other researchers showed that low spectral resolution only allowed for a rough classification of roofing materials. The use of one coherent method would allow data comparison between regions. Determining the amount of asbestos–cement products in use is important for assessing environmental exposure to asbestos fibres, determining patterns of disease, and ultimately modelling potential solutions to counteract threats.

scholarly journals Improved Spectral Angle Mapper applications for mangrove classification using SPOT5 imagery

2019 ◽  
Author(s):  
Xiu Su ◽  
Xiang Wang ◽  
Jianhua Zhao ◽  
Ke Cao ◽  
Jianchao Fan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Spectral Feature ◽  
Mangrove Ecosystem ◽  
Training Sample ◽  
Mangrove Species ◽  
Spectral Angle Mapper ◽  
Structure Information ◽  
Mangrove Ecosystems ◽  
Spectral Angle

Abstract. The traditional Spectral Angle Mapper (SAM) is an image classification method that uses image endmember spectra. Image spatial structure information may be neglected, especially in mangrove classification research where there is greater spectral similarity between species. This study combined object-oriented classification to improve the accuracy of the method in mangrove ecosystems. A mangrove area in Guangxi's coastal zone was chosen as the study site, and spectral feature analysis and ground investigations were carried out, combining pixel purification, training sample set optimization, and watershed image segmentation algorithm to improve the SAM. The improved SAM was used to classify SPOT5 remote sensing image data for a mangrove ecosystem and then classification accuracy was assessed. The results showed that the improved SAM had better classification accuracy for SPOT5 imagery. Accuracy for each mangrove species was greater than 80 % and overall accuracy was greater than 90 %, which showed that SAM was applicable for mangrove remote sensing. This application potential for classification and information extraction lays the foundation for commercialized remote sensing monitoring of mangrove ecosystems.

scholarly journals Spectral Reflectance-Based Mangrove Species Mapping from WorldView-2 Imagery of Karimunjawa and Kemujan Island, Central Java Province, Indonesia

2022 ◽  
Vol 14 (1) ◽  
pp. 183
Author(s):  
Arie Dwika Rahmandhana ◽  
Muhammad Kamal ◽  
Pramaditya Wicaksono
Keyword(s):  
Spectral Reflectance ◽  
Spectral Feature ◽  
Individual Species ◽  
Mangrove Forests ◽  
Mangrove Species ◽  
Spectral Angle Mapper ◽  
Central Java ◽  
Feature Fitting ◽  
Species Mapping ◽  
Central Java Province

Mangrove mapping at the species level enables the creation of a detailed inventory of mangrove forest biodiversity and supports coastal ecosystem management. The Karimunjawa National Park in Central Java Province is one of Indonesia’s mangrove habitats with high biodiversity, namely, 44 species representing 25 true mangroves and 19 mangrove associates. This study aims to (1) classify and group mangrove species by their spectral reflectance characteristics, (2) map mangrove species by applying their spectral reflectance to WorldView-2 satellite imagery with the spectral angle mapper (SAM), spectral information divergence (SID), and spectral feature fitting (SFF) algorithms, and (3) assess the accuracy of the produced mangrove species mapping of the Karimunjawa and Kemujan Islands. The collected field data included (1) mangrove species identification, (2) coordinate locations of targeted mangrove species, and (3) the spectral reflectance of mangrove species measured with a field spectrometer. Dendrogram analysis was conducted with the Ward linkage method to classify mangrove species based on the distance between the closest clusters of spectral reflectance patterns. The dendrogram showed that the 24 mangrove species found in the field could be grouped into four levels. They consisted of two, four, and five species groups for Levels 1 to 3, respectively, and individual species for Level 4. The mapping results indicated that the SID algorithm had the highest overall accuracy (OA) at 49.72%, 22.60%, and 15.20% for Levels 1 to 3, respectively, while SFF produced the most accurate results for individual species mapping (Level 4) with an OA of 5.08%. The results suggest that the greater the number of classes to be mapped, the lower the mapping accuracy. The results can be used to model the spatial distribution of mangrove species or the composition of mangrove forests and update databases related to coastal management.

scholarly journals Characterization and mapping of hematite ore mineral classes using hyperspectral remote sensing technique: a case study from Bailadila iron ore mining region

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Ibrahim Shaik ◽  
S. K. Begum ◽  
P. V. Nagamani ◽  
Narayan Kayet
Keyword(s):  
Infrared Absorption ◽  
Near Infrared ◽  
Spectral Feature ◽  
Absorption Feature ◽  
X Ray ◽  
Remote Sensing Technique ◽  
Ore Minerals ◽  
Feature Fitting ◽  
Hematite Ore

AbstractThe study demonstrates a methodology for mapping various hematite ore classes based on their reflectance and absorption spectra, using Hyperion satellite imagery. Substantial validation is carried out, using the spectral feature fitting technique, with the field spectra measured over the Bailadila hill range in Chhattisgarh State in India. The results of the study showed a good correlation between the concentration of iron oxide with the depth of the near-infrared absorption feature (R2 = 0.843) and the width of the near-infrared absorption feature (R2 = 0.812) through different empirical models, with a root-mean-square error (RMSE) between < 0.317 and < 0.409. The overall accuracy of the study is 88.2% with a Kappa coefficient value of 0.81. Geochemical analysis and X-ray fluorescence (XRF) of field ore samples are performed to ensure different classes of hematite ore minerals. Results showed a high content of Fe > 60 wt% in most of the hematite ore samples, except banded hematite quartzite (BHQ) (< 47 wt%).

scholarly journals Airborne Hyperspectral Data Acquisition and Processing in the Arctic: A Pilot Study Using the Hyspex Imaging Spectrometer for Wetland Mapping

2021 ◽  
Vol 13 (6) ◽  
pp. 1178
Author(s):  
Jordi Cristóbal ◽  
Patrick Graham ◽  
Anupma Prakash ◽  
Marcel Buchhorn ◽  
Rudi Gens ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Pilot Study ◽  
Data Processing ◽  
High Latitude ◽  
Hyperspectral Images ◽  
The Arctic ◽  
Wetland Mapping ◽  
Kappa Index ◽  
Spectral Angle Mapper ◽  
Spectral Angle

A pilot study for mapping the Arctic wetlands was conducted in the Yukon Flats National Wildlife Refuge (Refuge), Alaska. It included commissioning the HySpex VNIR-1800 and the HySpex SWIR-384 imaging spectrometers in a single-engine Found Bush Hawk aircraft, planning the flight times, direction, and speed to minimize the strong bidirectional reflectance distribution function (BRDF) effects present at high latitudes and establishing improved data processing workflows for the high-latitude environments. Hyperspectral images were acquired on two clear-sky days in early September, 2018, over three pilot study areas that together represented a wide variety of vegetation and wetland environments. Steps to further minimize BRDF effects and achieve a higher geometric accuracy were added to adapt and improve the Hyspex data processing workflow, developed by the German Aerospace Center (DLR), for high-latitude environments. One-meter spatial resolution hyperspectral images, that included a subset of only 120 selected spectral bands, were used for wetland mapping. A six-category legend was established based on previous U.S. Geological Survey (USGS) and U.S. Fish and Wildlife Service (USFWS) information and maps, and three different classification methods—hybrid classification, spectral angle mapper, and maximum likelihood—were used at two selected sites. The best classification performance occurred when using the maximum likelihood classifier with an averaged Kappa index of 0.95; followed by the spectral angle mapper (SAM) classifier with a Kappa index of 0.62; and, lastly, by the hybrid classifier showing lower performance with a Kappa index of 0.51. Recommendations for improvements of future work include the concurrent acquisition of LiDAR or RGB photo-derived digital surface models as well as detailed spectra collection for Alaska wetland cover to improve classification efforts.

Yield Estimation from Hyperspectral Imagery Using Spectral Angle Mapper (SAM)

2008 ◽  
Vol 51 (2) ◽  
pp. 729-737 ◽  
Author(s):  
C. Yang ◽  
J. H. Everitt ◽  
J. M. Bradford
Keyword(s):  
Hyperspectral Imagery ◽  
Yield Estimation ◽  
Spectral Angle Mapper ◽  
Spectral Angle

Cross‐borehole delineation of a conductive ore deposit in a resistive host—experimental design

Geophysics ◽  
2001 ◽  
Vol 66 (3) ◽  
pp. 824-835 ◽  
Author(s):  
David Johnson ◽  
Elena Cherkaev ◽  
Cynthia Furse ◽  
Alan C. Tripp
Keyword(s):  
Nickel Sulfide ◽  
Sulfide Deposit ◽  
Wave Analysis ◽  
Geological Structures ◽  
Ore Deposit ◽  
Sulfide Deposits ◽  
Full Wave ◽  
Full Wave Analysis ◽  
Optimal Weighting ◽  
Difference Time

The finite‐difference time‐domain method is used for high‐resolution full‐wave analysis of cross‐borehole electromagnetic surveys of buried nickel sulfide deposits. The method is validated against analytical methods for simple cases, but is shown to be a valuable tool for analysis of complicated geological structures such as faulted or layered regions. The magnetic fields generated by a wire loop in a borehole near a nickel sulfide deposit are presented for several cases. The full‐wave solution is obtained up to 200 MHz, where quasi‐static methods would have failed. The dielectric response is included in the solution, and the diffractive nature of the field is observed. The sensitivity of each receiver in a vertical line in the cross borehole is presented and analyzed to provide an optimal weighting for receivers that can be applied to an experimental study.

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