Minimum noise fraction transform for improving the classification of airborne hyperspectral data: Two case studies

Frassy, Federico; Via, G. Dalla; Maianti, Pieralberto; Marchesi, Andrea; Nodari, Francesco Rota; Gianinetto, Marco

doi:10.1109/whispers.2013.8080626

Cited by 18 publications

(16 citation statements)

References 13 publications

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“…Inverse MNF converts 3 MNF bands into 4 image bands (blue, green, red, and near infrared). The image quality has been improved because of the eliminated noises, hence particular object feature processing and analysis bringing more accurate results (Frassy et al, 2013).…”

Section: Mnf Transformation Resultsmentioning

confidence: 99%

“…On the contrary of hyper spectral data dimensionality, multispectral image has limited bands and information that would be also reduced through the method to some extent, the MNF effectiveness would be declined greater. The research of MNF through hyper spectral airborne image data to classify land cover has been conducted (Frassy et al, 2013), however the effect of MNF analysis is done by comparing the accuracy of spatial modeling results of vegetation canopy density. The vegetation canopy mapping topics is interesting and challenging considered to the lack of research related to the influence of MNF usage analysis of vegetation canopy density model (quantitative stratified data) which is not included as categorical data.…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Minimum Noise Fraction on Multispectral Imagery Data for Vegetation Canopy Density Modelling

Syarif

Kumara

2018

Geoplanning: Journal of Geomatics and Planning

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Minimum Noise Fraction (MNF) is known as one of the method to minimize noise on hyperspectral imagery. In addition, there are not many studies have tried to show the effect of MNF transform on multispectral data. This study purposes to determine the effect of MNF transform on the accuracy level of vegetation density modeling using 10 meters Sentinel-2A spatial resolution (multispectral data) and to know the cause. The study area is located in parts of Sapporo City, Hokkaido, Japan. Vegetation density is modelled through vegetation index approach, Normalized Difference Vegetation Index (NDVI). The results show that the coefficient correlation of vegetation density data and vegetation index regression after MNF transformation (0.801623) has higher value than the same regression without the MNF (0.794481). However, better correlation does not represent the better accuracy on vegetation density modeling. Accuracy calculation through standard error of estimate shows the use of MNF in multispectral data for vegetation density modeling causes the decrease of model accuracy value. The accuracy of vegetation density model without involving MNF transformation reached 91.40 %, while the model accuracy through MNF transformation before vegetation density modeling reached 90.89 %. The insignificant increased accuracy is occurred due to the limited number of multispectral image information compared to hyperspectral image data.

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Section: Mnf Transformation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Effect of Minimum Noise Fraction on Multispectral Imagery Data for Vegetation Canopy Density Modelling

Syarif

Kumara

2018

Geoplanning: Journal of Geomatics and Planning

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“…The reader is referred to the ENVI's user guide [94] for a detailed listing of all 65 SI computed for this study. The Minimum Noise Fraction transformation (MNF), a well-known technique for hyperspectral dimensionality reduction and denoising, was also computed for each image [72]. From the ALS acquisition, 93 LiDAR-based indexes were computed after point-cloud pre-processing: vegetation structure layers, extracted from the OPALS software [95] and topographic indexes extracted from the SAGA software [96].…”

Section: Rs Data Pre-processingmentioning

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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“…Dimensionality reduction techniques like principle component analysis (PCA), minimum noise fraction (MNF) are pre-processing steps that enhances the image by reducing the redundancy in the original data. In general, multispectral information has a collection of mixed pixels representing ground features that are to be distinguished using selective representative samples [10][11][12]. A supervised classification algorithm like random forest (RF) classifier is applied to datasets in this study as it is popular among the remote sensing community for its accuracy.…”

Section: Related Workmentioning

confidence: 99%

Assessment on the Potential of Multispectral and Hyperspectral Datasets for Land Use/Land Cover Classification

Priyadarshini

Sivashankari

Shekhar

et al. 2019

The 2nd International Electronic Conference on Geosciences

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Land use/land cover (LULC) is a significant factor which plays a vital role in defining an urban ecosystem. Interpretations of LULC are eased in recent times by utilizing hyperspectral and multispectral datasets obtained from various platforms. An attempt is made to comparatively assess the potentiality of AVIRIS NG with Sentinel 2 data through applied classification techniques for Kalaburagi urban sphere. Spectral responses of both datasets were analyzed to derive reflectance spectra. A standard supervised classification algorithm associated with dimensionality reduction techniques is applied. For performance evaluation, results are validated to check which dataset outperforms well and provides better accuracy.

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Minimum noise fraction transform for improving the classification of airborne hyperspectral data: Two case studies

Cited by 18 publications

References 13 publications

The Effect of Minimum Noise Fraction on Multispectral Imagery Data for Vegetation Canopy Density Modelling

The Effect of Minimum Noise Fraction on Multispectral Imagery Data for Vegetation Canopy Density Modelling

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

Assessment on the Potential of Multispectral and Hyperspectral Datasets for Land Use/Land Cover Classification

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