scholarly journals Coupling Relationship Analysis of Gold Content Using Gaofen-5 (GF-5) Satellite Hyperspectral Remote Sensing Data: A Potential Method in Chahuazhai Gold Mining Area, Qiubei County, SW China

2021 ◽  
Vol 14 (1) ◽  
pp. 109
Author(s):  
Yuehan Qin ◽  
Xinle Zhang ◽  
Zhifang Zhao ◽  
Ziyang Li ◽  
Changbi Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mining Area ◽  
Potential Method ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Matched Filtering ◽  
Geochemical Anomaly ◽  
Sw China ◽  
Coupling Relationship ◽  
Geochemical Anomalies

The gold (Au) geochemical anomaly is an important indicator of gold mineralization. While the traditional field geochemical exploration method is time-consuming and expensive, the hyperspectral remote sensing technique serves as a robust technique for the delineation and mapping of hydrothermally altered and weathered mineral deposits. Nonetheless, mineralization element anomaly detection was still seldomly used in previous hyperspectral remote sensing applications in mineralization. This study explored the coupling relationship between Gaofen-5 (GF-5) hyperspectral data and Au geochemical anomalies through several models. The Au geochemical anomalies in the Chahuazhai mining area, Qiubei County, Yunnan Province, SW China, was studied in detail. First, several noise reduction methods including radiometric calibration, Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Savitzky–Golay filter, and endmember choosing methods including Minimum Noise Fraction (MNF) transformation, matched filtering, and Fast Fourier Transform (FFT) transformation were applied to the Gaofen-5 (GF-5) hyperspectral data processing. The Spectrum-Area (S-A) method was introduced to build an FFT filter to highlight the spectral abnormal characteristics associated with Au geochemical anomaly information. Specifically, the Matched Filtering (MF) technique was applied to the dataset to find the Au geochemical anomaly abundances of endmembers with innovative large-sample learning. Then, Multiple Linear Regression (MLR), Partial Least Squares (PLS) regression, a Back Propagation (BP) network, and Geographically Weighted Regression (GWR) were used to reveal the coupling relationship between the spectra of the processed hyperspectral data and the Au geochemical anomalies. The results show that the GWR analysis has a much higher coefficient of determination, which implies that the Au geochemical anomalies and the spectral information are highly related to spatial locations. GWR works especially well for showing the regional Au geochemical anomaly trend and simulating the Au concentrated areas. The GWR model with application of the S-A method is applicable to the detection of Au geochemical anomalies, which could provide a potential method for Au deposit exploration using GF-5 hyperspectral data.

Mapping lithology in Canada's Arctic: application of hyperspectral data using the minimum noise fraction transformation and matched filtering

2005 ◽  
Vol 42 (12) ◽  
pp. 2173-2193 ◽  
Author(s):  
J R Harris ◽  
D Rogge ◽  
R Hitchcock ◽  
O Ijewliw ◽  
D Wright
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Matched Filtering ◽  
Lithological Mapping ◽  
Minimum Noise Fraction ◽  
Logical Mapping ◽  
End Members ◽  
Noise Fraction ◽  
Minimum Noise

A test site in southern Baffin Island, Canada has been established to study the applications of hyperspectral data to lithological mapping. Good bedrock exposure and minimal vegetation cover provide an ideal environment for the evaluation of hyperspectral remote sensing. Airborne PROBE hyperspectral data were collected over the study site during the summer of 2000. Processing methods involved (1) applying a minimum noise fraction (MNF) transformation to the data and visual interpretation of a ternary colour MNF image to produce a lithological–compositional map, and (2) selection of end members from the MNF image followed by matched filtering based on the selected end members to produce a lithological–compositional map. Both methods have produced a lithological map that compares favourably with the existing geological map. Although lichen imparts a similarity to the spectra throughout the visible and near infrared and short-wave infrared ranges, this study has shown that enough variability in the spectra as a function of different mineralogy was present to successfully discriminate one major lithological group (metatonalites) and three compositional units (psammites, quartzites, and monzogranites). Vegetation could be clearly distinguished, which in this area only is a good proxy for mapping metagabbroic rocks. Furthermore, discrimination of slightly different compositional units within the psammites and the metatonalites was also possible. The results from this study indicate that hyperspectral remotely sensed imagery holds promise for lithological mapping in Canada's North, although further analysis is required in different geologic environments in Canada's North to validate hyperspectral remote sensing as a useful aid to litho logical mapping.

Study on the Geochemical Anomaly of Copper Element Based on Hyperspectral Indices

2020 ◽  
Author(s):  
Wang Shanshan ◽  
Zhou Kefa
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Coefficient Of Determination ◽  
Geochemical Data ◽  
Geochemical Anomaly ◽  
Spectral Indices ◽  
Absorption Bands ◽  
Important Indicator ◽  
Cu Content ◽  
Geochemical Anomalies

<p>Geochemical anomalies are an important indicator in prospecting. In particular, geochemical anomalies of Cu play a very important role in geological prospecting of minerals. Geochemical anomalies of Cu are mainly related to mafic-ultramafic rocks and porphyry bodies, which are also associated with ore-forming elements of the Co-Zn-Cr-Ni-Cu combination. The conventional technique of geochemical prospecting involves superimposition of element symbols (Au, Fe, Cu, Al, Ca, etc.) on the geological map of an area by analysing geochemical anomalies using geochemical data. However, this technique is not suitable for regions where geochemical anomaly data are limited. The development of hyperspectral remote sensing has enabled the mapping of spectral features related to characteristic absorption bands of elements in minerals at high spatial resolution, providing a means for precise and detailed reconstructions of geochemical anomalies facies (surface). Compared to conventional techniques for identifying elements, reflectance spectroscopy offers a rapid, inexpensive, and non-destructive tool for determining the mineralogy of rock and soil samples. Hyperspectral remote sensing also provides data for prospecting in areas without sufficient geochemical data, and thus is of vital significance in prospecting for ores in such regions. However, approaches for remotely sensing elements are still lacking, particularly for element content. In this study, a level analysis of Cu content via spectral indices in the northwestern Junggar region, Xinjiang, was conducted. Based on four levels (0–100 ppm, 100–1000 ppm, 1000–10000 ppm, and >10000 ppm) of Cu content and corresponding spectral reflectance, simple and useful spectral indices for estimating Cu content at different levels were explored. The best wavelength domains for a given type of index were determined from four types of spectral indices by screening all combinations using correlation analysis. The coefficient of determination (R2) for Cu was calculated for all indices derived from the spectra of rock samples and was found to range from 0.02–0.75. With sensitive wavelengths and a significant correlation coefficient (R2 = 0.63, P < 0.005), the Normalized Difference (ND)-type index was the most sensitive to Cu content exceeding 10000 ppm. Although the ND-type index has a few limitations, it is a useful, simple, and robust indicator for determining Cu at high concentrations. With the advent of new platforms and satellites in the future, such relationships with other elements are required to enable the widespread use of this index in broad-scale surveys of mineral elements in the field.</p>

scholarly journals Utilizing Hyperspectral Remote Sensing for Soil Gradation

2020 ◽  
Vol 12 (20) ◽  
pp. 3312
Author(s):  
Jordan Ewing ◽  
Thomas Oommen ◽  
Paramsothy Jayakumar ◽  
Russell Alger
Keyword(s):  
Remote Sensing ◽  
Fine Particles ◽  
Soil Mechanics ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Controlled Environment ◽  
Sieve Analysis ◽  
Field Sample ◽  
Specific Objective ◽  
Soil Gradation

Soil gradation is an important characteristic for soil mechanics. Traditionally soil gradation is performed by sieve analysis using a sample from the field. In this research, we are interested in the application of hyperspectral remote sensing to characterize soil gradation. The specific objective of this work is to explore the application of hyperspectral remote sensing to be used as an alternative to traditional soil gradation estimation. The advantage of such an approach is that it would provide the soil gradation without having to obtain a field sample. This work will examine five different soil types from the Keweenaw Research Center within a laboratory-controlled environment for testing. Our study demonstrates a correlation between hyperspectral data, the percent gravel and sand composition of the soil. Using this correlation, one can predict the percent gravel and sand within a soil and, in turn, calculate the remaining percent of fine particles. This information can be vital to help identify the soil type, soil strength, permeability/hydraulic conductivity, and other properties that are correlated to the gradation of the soil.

Ensemble Learning on Hyperspectral Remote Sensing Image Classification

2012 ◽  
Vol 546-547 ◽  
pp. 508-513 ◽  
Author(s):  
Qiong Wu ◽  
Ling Wei Wang ◽  
Jia Wu
Keyword(s):  
Remote Sensing ◽  
Ensemble Learning ◽  
Classification Accuracy ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Remote Sensing Image Classification ◽  
Hyperspectral Remote Sensing Image

The characteristics of hyperspectral data with large number of bands, each bands have correlation, which has required a very high demand of solving the problem. In this paper, we take the features of hyperspectral remote sensing data and classification algorithms as the background, applying the ensemble learning to image classification.The experiment based on Weka. I compared the classification accuracy of Bagging, Boosting and Stacking on the base classifiers J48 and BP. The results show that ensemble learning on hyperspectral data can achieve higher classification accuracy. So that it provide a new method for the classification of hyperspectral remote sensing image.

Hyperspectral Remote Sensing and Radiative Transfer Simulation as a Tool for Monitoring Coral Reef Health

2002 ◽  
Vol 36 (1) ◽  
pp. 4-13 ◽  
Author(s):  
Hiroya Yamano ◽  
Masayuki Tamura ◽  
Yoshimitsu Kunii ◽  
Michio Hidaka
Keyword(s):  
Remote Sensing ◽  
Coral Reef ◽  
Radiative Transfer ◽  
Feasibility Studies ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Dead Coral ◽  
Hyperspectral Sensors ◽  
Reef Health ◽  
Radiative Transfer Modeling

Recent advances in the remote sensing of coral reefs include hyperspectral remote sensing and radiative transfer modeling. Hyperspectral data can be regarded as continuous and the derivative spectroscopy is effective for extracting coral reef components, including sand, macroalgae, and healthy, bleached, recently dead, and old dead coral. Radiative transfer models are effective for feasibility studies of satellite or airborne remote sensing. Using these techniques, we simulate and analyze the apparent reflectance of coral reef benthic features associated with bleaching events, obtained by hyperspectral sensors on various platforms (ROV, boat, airplane, and satellite), and suggest that the coral reef health on reef flats can be discriminated precisely. Remote sensing using hyperspectral sensors should significantly contribute to mapping and monitoring coral reef health.

scholarly journals An Overview of Hyperspectral Remote Sensing and its applications in various Disciplines

2016 ◽  
Vol 5 (2) ◽  
pp. 85 ◽  
Author(s):  
Alpana Shukla ◽  
Rajsi Kot
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Spatial Information ◽  
Coastal Zone Management ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Data Sets ◽  
Imaging Data ◽  
Advantages And Disadvantages

<div><p><em>Recent advances in remote sensing and geographic information has opened new directions for the development of hyperspectral sensors. Hyperspectral remote sensing, also known as imaging spectroscopy is a new technology. Hyperspectral imaging is currently being investigated by researchers and scientists for the detection and identification of vegetation, minerals, different objects and background.</em><em> Hyperspectral remote sensing combines imaging and spectroscopy in a single system which often includes large data sets and requires new processing methods. Hyperspectral data sets are generally made of about 100 to 200 spectral bands of relatively narrow bandwidths (5-10 nm), whereas, multispectral data sets are usually composed of about 5 to 10 bands of relatively large bandwidths (70-400 nm). Hyperspectral imagery is collected as a data cube with spatial information collected in the X-Y plane, and spectral information represented in the Z-direction. </em><em>Hyperspectral remote sensing is applicable in many different disciplines. It was originally developed for mining and geology; it has now spread into fields such as agriculture and forestry, ecology, coastal zone management, geology and mineral exploration. This paper presents an overview of hyperspectral imaging, data exploration and analysis, applications in various disciplines, advantages and disadvantages and future aspects of the technique.</em></p></div>

scholarly journals Agricultural Stress Monitoring using Remote Sensing Data

2020 ◽  
Vol 9 (3) ◽  
pp. 335-337
Keyword(s):  
Remote Sensing ◽  
Stress Level ◽  
Hyperspectral Image ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Stress Monitoring ◽  
Chlorophyll Contents ◽  
Sensing Data

This study consist of experiments on Hyperspectral remote sensing data for monitoring field stress using remote sensing tools. We have segmented Hyperspectral image and then calculated stress level using ENVI tool. EO-I hyperspectral remote sensing data from hyperion space born sensor has been used as the key input. QUACK (Quick Atmospheric Correction) algorithm has been used for atmospheric correction of hyperspectral data. EO-1, hyperion sensors data It has been observed that stress level depends on chlorophyll contents of a leaf. It has been observed that green field is with less stress and rock where no chlorophyll contents have most stress. We have also shown stress level in the scale of 1 to 9.

Method Study of Mineral Weight Information Extraction Based on Hyperion Hyperspectral Remote Sensing Data - The Region of Gannan as an Example

2013 ◽  
Vol 718-720 ◽  
pp. 2237-2241
Author(s):  
Chuan Zhang ◽  
Fa Wang Ye ◽  
Dong Hui Zhang ◽  
Ning Bo Zhao ◽  
Ding Wu
Keyword(s):  
Remote Sensing ◽  
Information Extraction ◽  
Clay Minerals ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Matched Filtering ◽  
Mineral Mapping ◽  
Sensing Data ◽  
Background Image ◽  
Spectral Angle

In this paper, the methods of extracting minerals weight information are studied based on hyperion hyperspectral remote sensing data, taking the region of Gannan area in Jiangxi as an example. After studying spectral angle mapping and matched filtering, the method has been developed which combines them to extract the weight information of minerals. The results show that this method can successfully apply and made spectral angle mapping integrate with matched filtering, combined their advantages and made up their shortcomings, and extract weight information of clay minerals accurately from the background image. Meanwhile, the location of all kinds of mineral and results of mineral mapping are consistent very well, reflecting the application feasibility of the method.

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