The topographic normalization of hyperspectral data: implications for the selection of spectral end members and lithologic mapping

2003 ◽  
Vol 85 (2) ◽  
pp. 221-231 ◽  
Author(s):  
J Feng
Keyword(s):  
Hyperspectral Data ◽  
End Members ◽  
Selection Of ◽  
Lithologic Mapping

scholarly journals MINERALOGICAL MAPPING IN THE PART OF A GOLD PROSPECT USING EO-1 HYPERION DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 991-993 ◽  
Author(s):  
V. K. Sengar ◽  
A. S. Venkatesh ◽  
P. K. Champaty Ray ◽  
S. L. Chattoraj ◽  
R. U. Sharma
Keyword(s):  
Hyperspectral Data ◽  
Reflectance Spectra ◽  
High Spectral Resolution ◽  
Mineral Potential Mapping ◽  
Spectral Angle Mapper ◽  
Potential Mapping ◽  
Hyperion Data ◽  
End Members ◽  
Resolution Of Imaging ◽  
Hyperion Image

The satellite data obtained from various airborne as well as space-borne Hyperspectral sensors, often termed as imaging spectrometers, have great potential to map the mineral abundant regions. Narrow contiguous bands with high spectral resolution of imaging spectrometers provide continuous reflectance spectra for different Earth surface materials. Detailed analysis of resultant reflectance spectra, derived through processing of hyperspectral data, helps in identification of minerals on the basis of their reflectance characteristics. EO-1 Hyperion sensor contains 196 unique channels out of 242 bands (L1R product) covering 0.4&amp;ndash;2.5&amp;thinsp;μm range has also been proved significant in the field of spaceborne mineral potential mapping. <br><br> Present study involves the processing of EO-1 Hyperion image to extract the mineral end members for a part of a gold prospect region. Mineral map has been generated using spectral angle mapper (SAM) method of image classification while spectral matching has been done using spectral analyst tool in ENVI. Resultant end members found in this study belong to the group of minerals constituting the rocks serving as host for the gold mineralisation in the study area.

A Study on Selecting Segmentation Object of Object-Oriented Inversion Based on Hyperspectral Chlorophyll-A in Chaohu Lake

2014 ◽  
Vol 675-677 ◽  
pp. 1153-1157
Author(s):  
Xue Jiao Hou ◽  
Jing Liu ◽  
Wei Cui
Keyword(s):  
Chlorophyll A ◽  
Object Oriented ◽  
Hyperspectral Data ◽  
Single Band ◽  
Quality Data ◽  
Chaohu Lake ◽  
Band Model ◽  
Water Quality Data ◽  
Combination Model ◽  
Selection Of

Beased on in Situ Water Quality Data and Hyperspectral Data from HJ-1A satellite in Chaohu Lake, through Contrasting the Object-Oriented Chlorophyll-a Inversion Precision Of single Band with Two-Band Model, the Results Show: (1) In Hyperspectral Object-Oriented Remote Sensing inversion, the Inversion Effect of Choosing Combination Model to Segment Is superior to that of Choosing the Single Band Directly, and Using Combination model to Segment can Certain Degree Solve the Problem that Commercial Softwares cannot Segment all Hyperspectral Data at the same Time.(2)When Inversing Chlorophyll-a Concentration with Hyperspectral Data, the Single Bands Constituting the Optimal Model Are not Always in the Traditional Characteristics Band Range of Chlorophyll-a. so All bands should be Comprehensively Analyzed to take Full Advantages Of hyperspectral Data when Inversing. these Conclusions Will provide Basis for the Future Segmentation Object Selection of Object-Orientedon Hyperspectral Lakes Chlorophyll-a Inversion and Certain Reference for Band Selection of Hyperspectral Inversion Model.

scholarly journals Modelling Site Index in Forest Stands Using Airborne Hyperspectral Imagery and Bi-Temporal Laser Scanner Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1020 ◽  
Author(s):  
Bollandsås ◽  
Ørka ◽  
Dalponte ◽  
Gobakken ◽  
Næsset
Keyword(s):  
Atmospheric Correction ◽  
Laser Scanner ◽  
Field Measurements ◽  
Site Index ◽  
Hyperspectral Data ◽  
Average Height ◽  
Single Time ◽  
Development Site ◽  
Corrected Data ◽  
Selection Of

In forest management, site index information is essential for planning silvicultural operations and forecasting forest development. Site index is most commonly expressed as the average height of the dominant trees at a certain index age, and can be determined either by photo interpretation, field measurements, or projection of age combined with height estimates from remote sensing. However, recently it has been shown that site index can be accurately predicted from bi-temporal airborne laser scanner (ALS) data. Furthermore, single-time hyperspectral data have also been shown to be correlated to site index. The aim of the current study was to compare the accuracy of modelling site index using (1) data from bi-temporal ALS; (2) single-time hyperspectral data with different types of preprocessing; and (3) combined bi-temporal ALS and single-time hyperspectral data. The period between the ALS acquisitions was 11 years. The preprocessing of the hyperspectral data included an atmospheric correction and/or a normalization of the reflectance. Furthermore, a selection of pixels was carried out based on NDVI and compared to using all pixels. The results showed that bi-temporal ALS data explained about 70% (R2) of the variation in the site index, and the RMSE values from a cross-validation were 3.0 m and 2.2 m for spruce- and pine-dominated plots, respectively. Corresponding values for the different single-time hyperspectral datasets were 54%, 3.9 m, and 2.5 m. With bi-temporal ALS data and hyperspectral data used in combination, the results indicated that the contribution from the hyperspectral data was marginal compared to just using bi-temporal ALS. We also found that models constructed with normalized hyperspectral data produced lower RMSE values compared to those constructed with atmospherically corrected data, and that a selection of pixels based on NDVI did not improve the results compared to using all pixels.

The Application of Petroleum Geochemical Methods to Production Allocation of Commingled Fluids

2021 ◽  
Author(s):  
Richard Patience ◽  
Mark Bastow ◽  
Martin Fowler ◽  
Julian Moore ◽  
Craig Barrie
Keyword(s):  
Best Practice ◽  
Statistical Treatment ◽  
Laboratory Analysis ◽  
Chemical Compositions ◽  
Production Allocation ◽  
Wide Range ◽  
Operational Activity ◽  
End Members ◽  
Selection Of

Abstract Production allocation from petroleum geochemistry is defined here as the quantitative determination of the amount or portion of a commingled fluid to be assigned to two or more individual fluid sources (e.g., a pipeline, field, reservoir, well) at a particular moment in time, based on the fluid chemistry. It requires: i) knowledge of the original chemical compositions of each of the fluids prior to mixing (referred to here as the "end members"), and ii) that statistically valid differences in their chemistries can be identified. Petroleum geochemical-based methods for production monitoring and allocation are much lower cost than using production logging tools, as there is no additional rig time or extra personnel required at the well site. Additionally, no intervention to the production of hydrocarbons from a well is required and, hence, there is none of the risk entailed in additional operational activity. Geochemical methods are applicable to a wide range of fields, irrespective of pressure, temperature, reservoir quality and reservoir fluid type. The method has been in existence for over 30 years, during which time a number of different analytical methods, data pre-processing and treatment approaches have been applied. This paper summarises these approaches, and provides examples, but also describes a "best practice" which is not a "one size fits all" approach, as is sometimes seen in the literature. A successful production allocation study consists of the following steps: i) Selection of end member samples that contribute to the commingled production fluid; ii) Determination of the differences in chemical composition of the end members through laboratory analysis of the end members (e.g. by WO-GC), replicate analyses of samples and statistical treatment of the data (e.g. PCA); iii) If statistically significant differences exist, laboratory analysis of the end members and commingled fluids with appropriate replicate analyses of samples; iv) Data selection, pre-processing (e.g. selection of ratios or concentrations of components); v) Determination of end member contributions by solving equations (e.g. least squares best fit) and uncertainty estimation (e.g. Monte Carlo or Bootstrap methods). The differences in approach for conventional versus unconventional plays are also discussed.

scholarly journals Analysis of Variable-Length Codes for Integer Encoding in Hyperspectral Data Compression with the k2-Raster Compact Data Structure

2020 ◽  
Vol 12 (12) ◽  
pp. 1983
Author(s):  
Kevin Chow ◽  
Dion Eustathios Olivier Tzamarias ◽  
Miguel Hernández-Cabronero ◽  
Ian Blanes ◽  
Joan Serra-Sagristà
Keyword(s):  
Data Structure ◽  
Compression Ratio ◽  
Hyperspectral Data ◽  
Variable Length ◽  
Direct Access ◽  
Access Time ◽  
Competitive Performance ◽  
K Value ◽  
Data Elements ◽  
Selection Of

This paper examines the various variable-length encoders that provide integer encoding to hyperspectral scene data within a k 2 -raster compact data structure. This compact data structure leads to a compression ratio similar to that produced by some of the classical compression techniques. This compact data structure also provides direct access for query to its data elements without requiring any decompression. The selection of the integer encoder is critical for obtaining a competitive performance considering both the compression ratio and access time. In this research, we show experimental results of different integer encoders such as Rice, Simple9, Simple16, PForDelta codes, and DACs. Further, a method to determine an appropriate k value for building a k 2 -raster compact data structure with competitive performance is discussed.

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.

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