Comparison and Evaluation of Dimensionality Reduction Techniques for Hyperspectral Data Analysis

Priyadarshini, K. Nivedita; Sivashankari, V.; Shekhar, Sulochana; Balasubramani, K.

doi:10.3390/iecg2019-06209

Cited by 12 publications

(9 citation statements)

References 6 publications

(7 reference statements)

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“…Each of these dimensional reduction techniques works on a unique principle and has its own advantages and disadvantages. Researchers use single transformation techniques (PCA, MNF, ICA) or hybrid combinations of these techniques (PCA and ICA transforms performed on the MNF components) before classification, then compare the results [60], emphasizing that the transformation e.g., MNF allowed for the extraction of the most informative data, with high signal-to-noise ratio (SNR), especially in the first MNF bands [60,61]. In the case of PCA, bands are delivered as optimal data due to its maximized total variance from the original images [62].…”

Section: Acronymmentioning

confidence: 99%

“…In the case of PCA, bands are delivered as optimal data due to its maximized total variance from the original images [62]. According to researchers, MNF and PCA bands have great impact for an improvement of classification results [61,63] because PCA techniques work on the data variance, and MNF sorts the information based on the signal-to-noise ratio [60,[64][65][66]. In the case of the AISA data, the first 15 MNF bands were highly decorrelated and used for classification, and in the case of MNF, there were the first four bands from the Sentinel-2 images (Table 4) and, respectively, four AISA's PCA and three Sentinel-2's PCA bands (Table 4).…”

Section: Acronymmentioning

confidence: 99%

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Sentinel-2 and AISA Airborne Hyperspectral Images for Mediterranean Shrubland Mapping in Catalonia

et al. 2022

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The Mediterranean ecosystem exhibits a particular geology and climate, which is characterized by mild, rainy winters and long, very hot summers with low precipitation; it has led to the emergence of resilient plant species. Such habitats contain a preponderance of shrubs, and collectively harbor 10% of the Earth's species, thus containing some of the most unique shrubby formations protecting against environmental natural degradation. Due to shrub species diversity, initial phases of forestland, heterogenous grasses, bare ground and stones, the monitoring of such areas is difficult. For this reason, the aim of this paper is to assess semi-automatic classifications of the shrubby formations based on multispectral Sentinel-2 and visible and near infrared (VINR) AISA-EAGLE II hyperspectral airborne images with a support of Canopy High Model (CHM) as a three-dimensional information and field-verified patterns, based on Match-T/DSM and aerial photos. Support Vector Machine (SVM) and Random Forest (RF) classifiers have been tested on a few scenarios featuring different combinations of spectral and Minimum Noise Fraction (MNF) transformed bands and vegetation indices. Referring to the results, the average overall accuracy for the SVM and AISA images (all tested data sets) was 78.23%, and for the RF: 79.85%. In the case of Sentinel-2, the SVM classifier obtained an average value of 83.63%, while RF: 85.32%; however, in the case of the shrubland, we would like to recommend the RF classifier, because the highest mean value of F1-score achieved was 91.86% (SVM offered few-percent-point worse results), and the required training time was quicker than SVM. Commonly available Sentinel-2 data offered higher accuracies for shrubland monitoring than did the airborne VNIR data.

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Section: Acronymmentioning

confidence: 99%

Section: Acronymmentioning

confidence: 99%

Sentinel-2 and AISA Airborne Hyperspectral Images for Mediterranean Shrubland Mapping in Catalonia

et al. 2022

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“…Dimensionality reduction was performed on the normalised dataset using the 't-Distributed Stochastic Neighbor Embedding (t-SNE)' (Van Der Maaten et al, 2014;Van Der Maaten and Hinton, 2008) algorithm which, like Principal Component Analysis (PCA) (Jolliffe, 2010), allows for dimensionality reduction of high-dimensional objects into low-dimensional points using feature extraction. This simplified the dimensionality by reducing the 'noise' (Priyadarshini et al, 2019) or the redundant similarities within the dataset, allowing the clustering algorithm to reveal the 'essence' therein and to cluster all observations on the basis of their significant differences. The t-SNE projected the high-dimensional data (the 24-hour features) into low-dimensional space, creating a two-dimensional output, by modelling the probability distribution of neighbouring data points in the dataset while preserving "as much of the significant structure of the high-dimensional data as possible in the low-dimensional map" (Van Der Maaten and Hinton, 2008).…”

Section: Dimensionality Reductionmentioning

confidence: 99%

An empirical water consumer segmentation and the characterisation of consumption patterns underpinning demand peaks

Abu-Bakar

Williams

Hallett

2021

Resources, Conservation and Recycling

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“…Dimensionality reduction techniques allow to handle the multi-dimensionality nature of hyperspectral data by removing the redundancy of noisy data without losing the original information content and in some cases increasing final classification accuracy [74]. Many studies can be found in literature for applying such techniques to HS data and to the simultaneous exploitation of HS and LiDAR, also integrated with the use of Machine Learning (ML), in particular Random Forest (RF) and/or Support Vector Machines (SVM) [62,63].…”

Section: Feature Selection and The Recursive Feature Elimination-randmentioning

confidence: 99%

Recursive Feature Elimination and Random Forest Classification of Meadows and Dry Grasslands in Lowland River Valleys of Poland Based on Airborne Hyperspectral and LiDAR Data Fusion

Demarchi¹,

Kania²,

Ciężkowski³

et al. 2020

Preprint

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The use of Hyperspectral (HS) and LiDAR acquisitions has a great potential to enhance mapping and monitoring practices of endangered grasslands habitats, beyond conventional botanical field surveys. In this study we assess the potentiality of Recursive Feature Elimination (RFE) in combination with Random Forest (RF) classification in extracting the main HS and LiDAR features needed to map selected Natura 2000 grasslands along Polish lowland river valleys, in particular alluvial meadows 6440, lowland hay meadows 6510 and xeric and calcareous grasslands 6120. We developed an automated RFE-RF system capable to combine the potentials of both techniques and applied it to multiple acquisitions. Several LiDAR-based products and different Spectral Indexes (SI) were computed and used as input in the system, with the aim of shedding light on the best-to-use features. Results showed a remarkable increase in classification accuracy when LiDAR and SI products are added to the HS dataset, strengthening in particular the importance of employing LiDAR in combination with HS. Using only the 24 optimal features selection generalized over the three study areas, strongly linked to the highly heterogeneous characteristics of the habitats and landscapes investigated, it was possible to achieve rather high classification results (K around 0.7-0.77 and habitats F1 accuracy around 0.8-0.85), indicating that the selected Natura 2000 meadows and dry grasslands habitats can be automatically mapped by airborne HS and LiDAR data. Similar approaches might be considered for future monitoring activities in the context of habitats protection and conservation

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Comparison and Evaluation of Dimensionality Reduction Techniques for Hyperspectral Data Analysis

Cited by 12 publications

References 6 publications

Sentinel-2 and AISA Airborne Hyperspectral Images for Mediterranean Shrubland Mapping in Catalonia

Sentinel-2 and AISA Airborne Hyperspectral Images for Mediterranean Shrubland Mapping in Catalonia

An empirical water consumer segmentation and the characterisation of consumption patterns underpinning demand peaks

Recursive Feature Elimination and Random Forest Classification of Meadows and Dry Grasslands in Lowland River Valleys of Poland Based on Airborne Hyperspectral and LiDAR Data Fusion

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