scholarly journals An Efficient Spectral Feature Extraction Framework for Hyperspectral Images

2020 ◽  
Vol 12 (23) ◽  
pp. 3967
Author(s):  
Zhen Li ◽  
Baojun Zhao ◽  
Wenzheng Wang
Keyword(s):  
Feature Extraction ◽  
Spectral Characteristics ◽  
Spectral Feature ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Spectral Information ◽  
Testing Time ◽  
Discriminative Ability ◽  
Feature Extracting ◽  
Learning Machine

Extracting diverse spectral features from hyperspectral images has become a hot topic in recent years. However, these models are time consuming for training and test and suffer from a poor discriminative ability, resulting in low classification accuracy. In this paper, we design an effective feature extracting framework for the spectra of hyperspectral data. We construct a structured dictionary to encode spectral information and apply learning machine to map coding coefficients. To reduce training and testing time, the sparsity constraint is replaced by a block-diagonal constraint to accelerate the iteration, and an efficient extreme learning machine is employed to fit the spectral characteristics. To optimize the discriminative ability of our model, we first add spectral convolution to extract abundant spectral information. Then, we design shared constraints for subdictionaries so that the common features of subdictionaries can be expressed more effectively, and the discriminative and reconstructive ability of dictionary will be improved. The experimental results on diverse databases show that the proposed feature extraction framework can not only greatly reduce the training and testing time, but also lead to very competitive accuracy performance compared with deep learning models.

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.

Feature Extraction Techniques for Airborne Hyperspectral Images – Implication for Mineral Exploration

2021 ◽  
Author(s):  
Rupsa Chakraborty ◽  
Gabor Kereszturi ◽  
Reddy Pullanagari ◽  
Patricia Durance ◽  
Salman Ashraf ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Total Variation ◽  
Spatial Resolution ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Total Variation Regularization ◽  
Elemental Distribution ◽  
Ground Truth Data ◽  
Resolution Data ◽  
Mineral Prospecting

<p>Geochemical mineral prospecting approaches are mostly point-based surveys which then rely on statistical spatial extrapolation methods to cover larger areas of interest. This leads to a trade-off between increasing sampling density and associated attributes (e.g., elemental distribution). Airborne hyperspectral data is typically high-resolution data, whilst being spatially continuous, and spectrally contiguous, providing a versatile baseline to complement ground-based prospecting approaches and monitoring. In this study, we benchmark various shallow and deep feature extraction algorithms, on airborne hyperspectral data at three different spatial resolutions, 0.8 m, 2 m and 3 m. Spatial resolution is a key factor to detailed scale-dependent mineral prospecting and geological mapping. Airborne hyperspectral data has potential to advance our understanding for delineating new mineral deposits. This approach can be further extended to large areas using forthcoming spaceborne hyperspectral platforms, where procuring finer spatial resolution data is highly challenging. The study area is located along the Rise and Shine Shear Zone (RSSZ) within the Otago schist, in the South Island (New Zealand). The RSSZ contains gold and associated hydrothermal sulphides and carbonate minerals that are disseminated through sheared upper green schist facies rocks on the 10-metre scale, as well as localized (metre-scale) quartz-rich zones. Soil and rock samples from 63 locations were collected, scattered around known mineralised and unmineralized zones, providing ground truth data for benchmarking. The separability between the mineralized and the non-mineralised samples through laboratory based spectral datasets was analysed by applying Partial least squares discriminant analysis (PLS-DA) on the XRF spectra and laboratory based hyperspectral data separately. The preliminary results indicate that even in partially vegetated zones mineralised regions can be mapped out relatively accurately from airborne hyperspectral images using orthogonal total variation component analysis (OTVCA). This focuses on feature extraction by optimising a cost function that best fits the hyperspectral data in a lower dimensional feature space while monitoring the spatial smoothness of the features by applying total variation regularization.</p>

scholarly journals An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images

2019 ◽  
Vol 11 (2) ◽  
pp. 136 ◽  
Author(s):  
Yuliang Wang ◽  
Huiyi Su ◽  
Mingshi Li
Keyword(s):  
Feature Extraction ◽  
Logistic Regression ◽  
Dimensionality Reduction ◽  
Spectral Feature ◽  
Hyperspectral Images ◽  
Nonlinear Dimensionality Reduction ◽  
Impervious Surfaces ◽  
Urban Impervious Surfaces ◽  
Improved Model ◽  
Logistic Regression Classifier

Hyperspectral images (HSIs) provide unique capabilities for urban impervious surfaces (UIS) extraction. This paper proposes a multi-feature extraction model (MFEM) for UIS detection from HSIs. The model is based on a nonlinear dimensionality reduction technique, t-distributed stochastic neighbor embedding (t-SNE), and the deep learning method convolutional deep belief networks (CDBNs). We improved the two methods to create a novel MFEM consisting of improved t-SNE, deep compression CDBNs (d-CDBNs), and a logistic regression classifier. The improved t-SNE method provides dimensionality reduction and spectral feature extraction from the original HSIs and the d-CDBNs algorithm extracts spatial feature and edges using the reduced dimensional datasets. Finally, the extracted features are combined into multi-feature for the impervious surface detection using the logistic regression classifier. After comparing with the commonly used methods, the current experimental results demonstrate that the proposed MFEM model provides better performance for UIS extraction and detection from HSIs.

Dimensionality Reduction of Hyperspectral Data via Spectral Feature Extraction

2009 ◽  
Vol 47 (7) ◽  
pp. 2091-2105 ◽  
Author(s):  
B. Mojaradi ◽  
H. Abrishami-Moghaddam ◽  
M.J.V. Zoej ◽  
R.P.W. Duin
Keyword(s):  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Spectral Feature ◽  
Hyperspectral Data

scholarly journals DETERMINING SPECTRAL REFLECTANCE COEFFICIENTS FROM HYPERSPECTRAL IMAGES OBTAINED FROM LOW ALTITUDES

2016 ◽  
Vol XLI-B7 ◽  
pp. 107-110
Author(s):  
P. Walczykowski ◽  
A. Jenerowicz ◽  
A. Orych ◽  
K. Siok
Keyword(s):  
Remote Sensing ◽  
Spectral Reflectance ◽  
Spectral Characteristics ◽  
Research Work ◽  
Object Identification ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Post Processing ◽  
Ecosystem Studies ◽  
Processing Techniques

Remote Sensing plays very important role in many different study fields, like hydrology, crop management, environmental and ecosystem studies. For all mentioned areas of interest different remote sensing and image processing techniques, such as: image classification (object and pixel- based), object identification, change detection, etc. can be applied. Most of this techniques use spectral reflectance coefficients as the basis for the identification and distinction of different objects and materials, e.g. monitoring of vegetation stress, identification of water pollutants, yield identification, etc. Spectral characteristics are usually acquired using discrete methods such as spectrometric measurements in both laboratory and field conditions. Such measurements however can be very time consuming, which has led many international researchers to investigate the reliability and accuracy of using image-based methods. According to published and ongoing studies, in order to acquire these spectral characteristics from images, it is necessary to have hyperspectral data. The presented article describes a series of experiments conducted using the push-broom Headwall MicroHyperspec A-series VNIR. This hyperspectral scanner allows for registration of images with more than 300 spectral channels with a 1.9 nm spectral bandwidth in the 380- 1000 nm range. The aim of these experiments was to establish a methodology for acquiring spectral reflectance characteristics of different forms of land cover using such sensor. All research work was conducted in controlled conditions from low altitudes. Hyperspectral images obtained with this specific type of sensor requires a unique approach in terms of post-processing, especially radiometric correction. Large amounts of acquired imagery data allowed the authors to establish a new post- processing approach. The developed methodology allowed the authors to obtain spectral reflectance coefficients from a hyperspectral sensor mounted on an unmanned aerial vehicle, ensuring a high accuracy of obtained data.

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