Multisensor Fusion With Hyperspectral Imaging Data: Detection and Classification

Hsu, Su May; Burke, Hsiao-hua K.

doi:10.1142/9789812775320_0019

Cited by 31 publications

(32 citation statements)

References 9 publications

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“…Fusing the information from these two imaging devices using their individual benefits is shown to improve the overall results compared to using either imaging device individually (Hsu and Burke 2003).…”

Section: Proposed Plant Detection and Classification Methodsmentioning

confidence: 99%

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Plant Classification Combining Colour and Spectral Cameras for Weed Control Purposes

Komi

Jackson

Parkin

2007

2007 IEEE International Symposium on Industrial Electronics

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Section: Proposed Plant Detection and Classification Methodsmentioning

confidence: 99%

“…A simulation of fusing hyperspectral and relatively spatially more accurate colour data is presented by Hsu and Burke (2003). They give examples of current methods in registration and spectral sharpening using the data from both sensors.…”

Section: Hyperspectral and High Resolution Colour Fusion In Remote Sementioning

confidence: 99%

Plant Classification Combining Colour and Spectral Cameras for Weed Control Purposes

Komi

Jackson

Parkin

2007

2007 IEEE International Symposium on Industrial Electronics

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“…In this spectral imaging format, the cross trackline of spatial pixels are decomposed into three spectral bands via diffraction grating and wedge filter mechanisms (Seery et al, 1996). On the other hand, Principal Component Analysis (PCA) was utilized in reduction ofdata dimensionality while preserving essential information in the spectral data cube (Hsu and Burke, 2003). For MODIS L1B data pre-processing procedures i.e.…”

Section: Spectral Imagingmentioning

confidence: 99%

Retrieval of Aerosol Optical Depth Over Ecologically Stressed East African Land Surfaces from Top of Atmosphere Radiance Utilizing the Aerosol Free Vegetation Index

Makokha¹,

Angeyo²,

Muthama³

2016

Ethiop. J. Env Stud & Manag

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“…In the research on hyperspectral image analysis, Salgado and Ponomaryov [51] proposed feature selection based on coefficients obtained via discrete Fourier transform (DFT). Furthermore, two-dimensional (2D) Fourier transform is applied for sharpening HSIdata [52].…”

Section: Introductionmentioning

confidence: 99%

Application of the Frequency Spectrum to Spectral Similarity Measures

Wang

Yong

2016

Remote Sensing

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Several frequency-based spectral similarity measures, derived from commonly-used ones, are developed for hyperspectral image classification based on the frequency domain. Since the frequency spectrum (magnitude spectrum) of the original signature for each pixel from hyperspectral data can clearly reflect the spectral features of different types of land covers, we replace the original spectral signature with its frequency spectrum for calculating the existing spectral similarity measure. The frequency spectrum is symmetrical around the direct current (DC) component; thus, we take one-half of the frequency spectrum from the DC component to the highest frequency component as the input signature. Furthermore, considering the fact that the low frequencies include most of the frequency energy, we can optimize the classification result by choosing the ratio of the frequency spectrum (from the DC component to the highest frequency component) involved in the calculation. In our paper, the frequency-based measures based on the spectral gradient angle (SAM), spectral information divergence (SID), spectral correlation mapper (SCM), Euclidean distance (ED), normalized Euclidean distance (NED) and SID × sin(SAM) (SsS) measures are called the F-SAM, F-SID, F-SCM, F-ED, F-NED and F-SsS, respectively. In the experiment, three commonly-used hyperspectral remote sensing images are employed as test data. The frequency-based measures proposed here are compared to the corresponding existing ones in terms of classification accuracy. The classification results by parameter optimization are also analyzed. The results show that, although not all frequency-based spectral similarity measures are better than the original ones, some frequency-based measures, such as the F-SsS and F-SID, exhibit a relatively better performance and have more robust applications than the other spectral similarity measures.

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Multisensor Fusion With Hyperspectral Imaging Data: Detection and Classification

Cited by 31 publications

References 9 publications

Plant Classification Combining Colour and Spectral Cameras for Weed Control Purposes

Plant Classification Combining Colour and Spectral Cameras for Weed Control Purposes

Retrieval of Aerosol Optical Depth Over Ecologically Stressed East African Land Surfaces from Top of Atmosphere Radiance Utilizing the Aerosol Free Vegetation Index

Application of the Frequency Spectrum to Spectral Similarity Measures

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