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.

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.

scholarly journals Target detection algorithm for airborne thermal hyperspectral data

2014 ◽  
Vol XL-8 ◽  
pp. 827-832 ◽  
Author(s):  
R. Marwaha ◽  
A. Kumar ◽  
P. L. N. Raju ◽  
Y. V. N. Krishna Murthy
Keyword(s):  
Spectral Resolution ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Hyperspectral Data ◽  
High Spectral Resolution ◽  
Infrared Range ◽  
Digital Photograph ◽  
Minimum Noise Fraction ◽  
Noise Fraction ◽  
Minimum Noise

Airborne hyperspectral imaging is constantly being used for classification purpose. But airborne thermal hyperspectral image usually is a challenge for conventional classification approaches. The Telops Hyper-Cam sensor is an interferometer-based imaging system that helps in the spatial and spectral analysis of targets utilizing a single sensor. It is based on the technology of Fourier-transform which yields high spectral resolution and enables high accuracy radiometric calibration. The Hypercam instrument has 84 spectral bands in the 868 cm<sup>&minus;1</sup> to 1280 cm<sup>&minus;1</sup> region (7.8 μm to 11.5 μm), at a spectral resolution of 6 cm<sup>&minus;1</sup> (full-width-half-maximum) for LWIR (long wave infrared) range. Due to the Hughes effect, only a few classifiers are able to handle high dimensional classification task. MNF (Minimum Noise Fraction) rotation is a data dimensionality reducing approach to segregate noise in the data. In this, the component selection of minimum noise fraction (MNF) rotation transformation was analyzed in terms of classification accuracy using constrained energy minimization (CEM) algorithm as a classifier for Airborne thermal hyperspectral image and for the combination of airborne LWIR hyperspectral image and color digital photograph. On comparing the accuracy of all the classified images for airborne LWIR hyperspectral image and combination of Airborne LWIR hyperspectral image with colored digital photograph, it was found that accuracy was highest for MNF component equal to twenty. The accuracy increased by using the combination of airborne LWIR hyperspectral image with colored digital photograph instead of using LWIR data alone.

scholarly journals Superpixel-Based Minimum Noise Fraction Feature Extraction for Classification of Hyperspectral Images

2020 ◽  
Vol 37 (5) ◽  
pp. 812-822
Author(s):  
Behnam Asghari Beirami ◽  
Mehdi Mokhtarzade
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Minimum Noise Fraction ◽  
Vector Machines ◽  
Noise Covariance ◽  
Noise Fraction ◽  
Minimum Noise

In this paper, a novel feature extraction technique called SuperMNF is proposed, which is an extension of the minimum noise fraction (MNF) transformation. In SuperMNF, each superpixel has its own transformation matrix and MNF transformation is performed on each superpixel individually. The basic idea behind the SuperMNF is that each superpixel contains its specific signal and noise covariance matrices which are different from the adjacent superpixels. The extracted features, owning spatial-spectral content and provided in the lower dimension, are classified by maximum likelihood classifier and support vector machines. Experiments that are conducted on two real hyperspectral images, named Indian Pines and Pavia University, demonstrate the efficiency of SuperMNF since it yielded more promising results than some other feature extraction methods (MNF, PCA, SuperPCA, KPCA, and MMP).

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.

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