Unsupervised feature extraction techniques for hyperspectral data and its effects on unsupervised classification

2003 ◽  
Author(s):  
Luis O. Jimenez-Rodriguez ◽  
Emmanuel Arzuaga-Cruz ◽  
Miguel Velez-Reyes
Keyword(s):  
Feature Extraction ◽  
Unsupervised Classification ◽  
Hyperspectral Data ◽  
Extraction Techniques ◽  
Unsupervised Feature Extraction

scholarly journals Classification of hyperspectral data using extended attribute profiles based on supervised and unsupervised feature extraction techniques

2012 ◽  
Vol 3 (3) ◽  
pp. 269-298 ◽  
Author(s):  
Prashanth Reddy Marpu ◽  
Mattia Pedergnana ◽  
Mauro Dalla Mura ◽  
Stijn Peeters ◽  
Jon Atli Benediktsson ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Hyperspectral Data ◽  
Extraction Techniques ◽  
Unsupervised Feature Extraction

Wavelet Based Feature Extraction Techniques of Hyperspectral Data

2016 ◽  
Vol 44 (3) ◽  
pp. 373-384 ◽  
Author(s):  
N. Prabhu ◽  
Manoj K. Arora ◽  
R. Balasubramanian
Keyword(s):  
Feature Extraction ◽  
Hyperspectral Data ◽  
Extraction Techniques

scholarly journals The Novel Gravitational Mass Weighted PCA Technique for Feature Extraction in Hyperspectral Data Classification

2019 ◽  
Vol 8 (5S3) ◽  
pp. 250-255
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Gravitational Mass ◽  
Spectral Information ◽  
The Novel ◽  
Extraction Techniques ◽  
Imaging Spectrometer ◽  
Weighted Principal Component ◽  
Minimum Number

Recent advancements in the imaging spectrometer collect both spatial and spectral information which creates a huge dimensionality. The heavy spectral information creates to build a classifier for discerning between the materials in the scene. The minimum number of training labels always in an exchange between the spectral information and the performance is called the Hughes effect. Also the redundant of spectral information and noisy data presents in the hyperspectral scene. The above issues are overcome using feature extraction and feature selection methods which play a major role in the reduction of dimensionality. This paper proposes the novel fusion Gravitational Mass Weighted Principal Component Analysis (GMWPCA) techniques for hyperspectral data dimensionality. Also, this paper presents the deep insight about the feature extraction techniques in hyperspectral data of both supervised and unsupervised learning methods and experimental analysis in AVIRIS Indian Pines hyperspectral dataset by employing PCA, Probability PCA, LDA, and proposed techniques. The 93.63 % high accuracy achieved by using a novel proposed method

scholarly journals AN EXTENDED SPECTRAL–SPATIAL CLASSIFICATION APPROACH FOR HYPERSPECTRAL DATA

2017 ◽  
Vol IV-4/W4 ◽  
pp. 37-41
Author(s):  
D. Akbari
Keyword(s):  
Feature Extraction ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Extraction Methods ◽  
Hyperspectral Data ◽  
Classification Approach ◽  
Supervised Feature Extraction ◽  
Noise Fraction ◽  
Unsupervised Feature Extraction

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

scholarly journals Hyperspectral Image Classification Using Unsupervised Learning Algorithms

2021 ◽  
Author(s):  
Wafa Fatima ◽  
Iqra Ejaz
Keyword(s):  
Feature Extraction ◽  
Hyperspectral Image ◽  
Spectral Feature ◽  
Unsupervised Classification ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Classification Algorithms ◽  
Classification Methods ◽  
Hyperspectral Image Classification ◽  
Different Types

Hyperspectral image (HSI) classification is a mechanism of analyzing differentiated land cover in remotely sensed hyperspectral images. In the last two decades, a number of different types of classification algorithms have been proposed for classifying hyperspectral data. These algorithms include supervised as well as unsupervised methods. Each of these algorithms has its own limitations. In this research, three different types of unsupervised classification methods are used to classify different datasets i-e Pavia Center, Pavia University, Cuprite, Moffett Field. The main objective is to assess the performance of all three classifiers K-Means, Spectral Matching, and Abundance Mapping, and observing their applicability on different datasets. This research also includes spectral feature extraction for hyperspectral datasets.

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