AVIRIS-NG hyperspectral data analysis for pre- and post-MNF transformation using per-pixel classification algorithms

Sharma, Laxmi Kant; Verma, Rajani Kant

doi:10.1080/10106049.2020.1801857

Cited by 8 publications

(5 citation statements)

References 20 publications

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“…AVIRIS-NG surface reflectance data for February 8, 2016, were made available via the VEDAS geoportal. This information was collected from a height of around 6 km, with a pixel resolution of 4 m. The image was constructed using data collected in the visible, near-infrared, and short-wave infrared (SWIR) ranges, with 425 continuous narrow spectral bands with a spectral resolution of 5 nm 29 . Very high signal-to-noise ratio (>2000 at 600 nm and >1000 at 2200 nm) and a high degree of accuracy (95%).…”

Section: Methodsmentioning

confidence: 99%

“…This information was collected from a height of around 6 km, with a pixel resolution of 4 m. The image was constructed using data collected in the visible, near-infrared, and short-wave infrared (SWIR) ranges, with 425 continuous narrow spectral bands with a spectral resolution of 5 nm. 29 Very high signal-tonoise ratio (>2000 at 600 nm and >1000 at 2200 nm) and a high degree of accuracy (95%). The field of view (FOV) is 34 mrad, and the instantaneous field of view (IFOV) is 1 mrad.…”

Section: Data Description and Study Sitementioning

confidence: 99%

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AVIRIS-NG hyperspectral data for biomass modeling: from ground plot selection to forest species recognition

Verma

Sharma

Lele

2023

J. Appl. Rem. Sens.

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Forest biomass is an important biophysical parameter, which delivers vital and valuable information about forest health, growth, productivity, carbon cycle monitoring, forest degradation, and its ecosystem. It is an important inconstant for ecological modeling, carbon stock assessment, and climate change. Forest biomass estimation has been progressively investigated, in which the accuracy of results is good enough to estimate accuracy of biomass; therefore, more accurate estimation of biomass is important for refining the precision and its applicability of these techniques. Hyperspectral remote sensing provides more accurate information about vegetation, so with the combination of advanced hyperspectral datasets it may be a better technique to enhance the results and accuracy of spatial biomass. Airborne hyperspectral data of airborne visible infrared imaging spectrometer-next generation data were demonstrated to estimate above ground biomass (AGB) of a tropical dry deciduous forest. Atmospherically resistant vegetation index, simple ratio index (SRI), normalized difference vegetation index, and enhanced vegetation index (EVI) were used to estimate AGB, in which EVI performs better than other vegetation indices with 0.55 R square value. Plant senescence reflectance index was used to estimate the dry and senescence condition of the forest and its correlations were performed with ground biomass and other vegetation indices.

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Section: Methodsmentioning

confidence: 99%

Section: Data Description and Study Sitementioning

confidence: 99%

AVIRIS-NG hyperspectral data for biomass modeling: from ground plot selection to forest species recognition

Verma

Sharma

Lele

2023

J. Appl. Rem. Sens.

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“…PCA selects the principal components according to the skew variance size, and the principal components are arranged in order from large to small, according to the eigenvectors corresponding to the eigenvalues to retain the larger eigenvectors, so as to achieve dimensionality reduction of the data. The difference between MNF and PCA is that the components obtained after MNF transformation [20] are arranged according to the signal-to-noise ratio (SNR) rather than variance.…”

Section: W W = Imentioning

confidence: 99%

The Spatial Spectral K-Nearest Neighbor Minimax Label Projection Semi-supervised Classification For Hyperspectral Remote Sensing Image

Luo¹,

Liu²,

Xiong

2023

Preprint

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Aiming at the issues of classification performance degradation of the algorithm due to too much redundant information of hyperspectral remote sensing images and incomplete classification of MMLP algorithm due to sparse graph, the Spatial Spectral K-Nearest Neighbor Minimax Label Projection (SSKMMLP) semi-supervised hyperspectral image classification algorithm was proposed, which is a semi-supervised hyperspectral image classification algorithm. SSKMMLP algorithm firstly uses Minimum noise fraction (MNF) and Principal component analysis (PCA) algorithm to reduce the dimension of image data. Secondly, MMLP algorithm is used to preliminarily classify the data after dimensionality reduction, and a new MMLP second algorithm is proposed to reclassify the unlabeled samples, to solve the problem that MMLP algorithm may not be able to completely classify. Finally, combined with spatial neighborhood information and classification results, all sample categories are judged again, so as to improve the classification accuracy further. Experimental results on data sets of Pavia University, Salinas, WHU-Hi-Longkou, WHU-Hi-Hanchuan, WHU-Hi-Honghu show that compared with the comparison method used, SSKMMLP algorithm only needs less class label samples to obtain higher classification accuracy.

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“…MNF transformation was used to determine the inherent dimensions of image data (i.e., the number of bands) [ 30 , 31 ], separate the noise in the data, and reduce the computational demand in subsequent processing. MNF transformation essentially involves two cascading principal component transformations.…”

Section: Experiments and Data Processingmentioning

confidence: 99%

Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology

Zhao¹,

Jing²,

Wang³

et al. 2021

Entropy

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A porcelain insulator is an important part to ensure that the insulation requirements of power equipment can be met. Under the influence of their structure, porcelain insulators are prone to mechanical damage and cracks, which will reduce their insulation performance. After a long-term operation, crack expansion will eventually lead to breakdown and safety hazards. Therefore, it is of great significance to detect insulator cracks to ensure the safe and reliable operation of a power grid. However, most traditional methods of insulator crack detection involve offline detection or contact measurement, which is not conducive to the online monitoring of equipment. Hyperspectral imaging technology is a noncontact detection technology containing three-dimensional (3D) spatial spectral information, whereby the data provide more information and the measuring method has a higher safety than electric detection methods. Therefore, a model of positioning and state classification of porcelain insulators based on hyperspectral technology is proposed. In this model, image data were used to extract edges to locate cracks, and spectral information was used to classify the surface states of porcelain insulators with EfficientNet. Lastly, crack extraction was realized, and the recognition accuracy of cracks and normal states was 96.9%. Through an analysis of the results, it is proven that the crack detection method of a porcelain insulator based on hyperspectral technology is an effective non-contact online monitoring approach, which has broad application prospects in the era of the Internet of Things with the rapid development of electric power.

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AVIRIS-NG hyperspectral data analysis for pre- and post-MNF transformation using per-pixel classification algorithms

Cited by 8 publications

References 20 publications

AVIRIS-NG hyperspectral data for biomass modeling: from ground plot selection to forest species recognition

AVIRIS-NG hyperspectral data for biomass modeling: from ground plot selection to forest species recognition

The Spatial Spectral K-Nearest Neighbor Minimax Label Projection Semi-supervised Classification For Hyperspectral Remote Sensing Image

Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology

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