Noise reduction and best band selection techniques for improving classification results using hyperspectral data: application to lithological mapping in Canada's Arctic

2006 ◽  
Vol 32 (5) ◽  
pp. 341-354 ◽  
Author(s):  
J R Harris ◽  
P. Ponomarev ◽  
J. Shang ◽  
D. Rogge
Keyword(s):  
Noise Reduction ◽  
Hyperspectral Data ◽  
Band Selection ◽  
Lithological Mapping ◽  
Data Application

Dimensionality Reduction and Classification of Hyperspectral Images using Genetic Algorithm

2016 ◽  
Vol 3 (3) ◽  
pp. 503 ◽  
Author(s):  
R. Kiran Kumar ◽  
B. Saichandana ◽  
K. Srinivas
Keyword(s):  
Genetic Algorithm ◽  
Dimensionality Reduction ◽  
Hyperspectral Image ◽  
Image Data ◽  
Unsupervised Classification ◽  
Hyperspectral Data ◽  
Band Selection ◽  
Data Set ◽  
Mode Decomposition ◽  
Fused Image

<p>This paper presents genetic algorithm based band selection and classification on hyperspectral image data set. Hyperspectral remote sensors collect image data for a large number of narrow, adjacent spectral bands. Every pixel in hyperspectral image involves a continuous spectrum that is used to classify the objects with great detail and precision. In this paper, first filtering based on 2-D Empirical mode decomposition method is used to remove any noisy components in each band of the hyperspectral data. After filtering, band selection is done using genetic algorithm in-order to remove bands that convey less information. This dimensionality reduction minimizes many requirements such as storage space, computational load, communication bandwidth etc which is imposed on the unsupervised classification algorithms. Next image fusion is performed on the selected hyperspectral bands to selectively merge the maximum possible features from the selected images to form a single image. This fused image is classified using genetic algorithm. Three different indices, such as K-means Index (KMI) and Jm measure are used as objective functions. This method increases classification accuracy and performance of hyperspectral image than without dimensionality reduction.</p>

scholarly journals Noise reduction of time domain electromagnetic data: Application of a combined wavelet denoising method

Radio Science ◽  
2016 ◽  
Vol 51 (6) ◽  
pp. 680-689 ◽  
Author(s):  
Yanju Ji ◽  
Dongsheng Li ◽  
Guiyang Yuan ◽  
Jun Lin ◽  
Shangyu Du ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Time Domain ◽  
Wavelet Denoising ◽  
Denoising Method ◽  
Electromagnetic Data ◽  
Data Application ◽  
Time Domain Electromagnetic

Band Selection Using Hyperspectral Data of Subtropical Tree Species

2003 ◽  
Vol 18 (4) ◽  
pp. 3-11 ◽  
Author(s):  
Tung Fung ◽  
Hester Fung Yan Ma ◽  
Wai Lok Siu
Keyword(s):  
Tree Species ◽  
Hyperspectral Data ◽  
Band Selection

scholarly journals Towards Multiscale and Multisource Remote Sensing Mineral Exploration Using RPAS: A Case study in the Lofdal Carbonatite-Hosted REE Deposit, Namibia

2019 ◽  
Vol 11 (21) ◽  
pp. 2500 ◽  
Author(s):  
Booysen ◽  
Zimmermann ◽  
Lorenz ◽  
Gloaguen ◽  
Nex ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mineral Exploration ◽  
Field Work ◽  
Relevant Information ◽  
Hyperspectral Data ◽  
Multiscale Approach ◽  
Carbonatite Complex ◽  
Lithological Mapping ◽  
Aircraft System ◽  
Difficult Terrain

Traditional exploration techniques usually rely on extensive field work supported by geophysical ground surveying. However, this approach can be limited by several factors such as field accessibility, financial cost, area size, climate, and public disapproval. We recommend the use of multiscale hyperspectral remote sensing to mitigate the disadvantages of traditional exploration techniques. The proposed workflow analyzes a possible target at different levels of spatial detail. This method is particularly beneficial in inaccessible and remote areas with little infrastructure, because it allows for a systematic, dense and generally noninvasive surveying. After a satellite regional reconnaissance, a target is characterized in more detail by plane-based hyperspectral mapping. Subsequently, Remotely Piloted Aircraft System (RPAS)-mounted hyperspectral sensors are deployed on selected regions of interest to provide a higher level of spatial detail. All hyperspectral data are corrected for radiometric and geometric distortions. End-member modeling and classification techniques are used for rapid and accurate lithological mapping. Validation is performed via field spectroscopy and portable XRF as well as laboratory geochemical and spectral analyses. The resulting spectral data products quickly provide relevant information on outcropping lithologies for the field teams. We show that the multiscale approach allows defining the promising areas that are further refined using RPAS-based hyperspectral imaging. We further argue that the addition of RPAS-based hyperspectral data can improve the detail of field mapping in mineral exploration, by bridging the resolution gap between airplane- and ground-based data. RPAS-based measurements can supplement and direct geological observation rapidly in the field and therefore allow better integration with in situ ground investigations. We demonstrate the efficiency of the proposed approach at the Lofdal Carbonatite Complex in Namibia, which has been previously subjected to rare earth elements exploration. The deposit is located in a remote environment and characterized by difficult terrain which limits ground surveys.

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