Intuitionistic Fuzzy Hamacher Generalized Shapley Choquet Integral Operators Based Decision-Making Model for Feature Extraction and Automatic Material Classification in Mining Area Using Satellite Data
Algorithm 1
Authenticity of material detection/classification using vanadium/other metal image corpus.
Input: image signals of LANDSAT/LC08/C02/T1 dataset.
Output: authenticity of material detection/classification.
Start
(1)
Input noisy image signals
(2)
Noise is removed using the spectral subtraction method
(3)
image signal is divided into frames
(4)
Frames are windowed with Hamming window
(5)
Feature vectors are extracted from the speech signal using the MFCC algorithm.
(6)
Clusters are created by the intuitionistic fuzzy Hamacher generalized Shapley Choquet integral operators fuzzy c-mean (HGSCIO-FCM) clustering method, arranged in proper format to feed into the artificial neural network for classification.
(7)
Training stage: weights of the feed forward neural network were given by some arbitrary values and then tuned for optimal during the iterative learning procedure with the help of the back propagation algorithm.
(8)
Testing stage: the neural network is tested against a variety of test samples of image to ensure whether the acquired system correctly categorizes the metal into vanadium and other metal parts.
(9)
Authenticity of image classification is done using vanadium/other metal image corpus.
