Application of the Frequency Spectrum to Spectral Similarity Measures

Wang, Ke; Yong, Bin

doi:10.3390/rs8040344

Cited by 15 publications

(7 citation statements)

References 55 publications

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“…SID indexes can be put in order using Equation (3). Hence, the larger the value of the SID index, the higher the similarity between pixels [19,20].…”

Section: Spectral Information Divergencementioning

confidence: 99%

Mineral Detection Using Sharpened VNIR and SWIR Bands of Worldview-3 Satellite Imagery

Park

Choi

2021

Sustainability

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Worldview-3 satellite imagery provides panchromatic images with a high spatial resolution and visible near infrared (VNIR) and shortwave infrared (SWIR) bands with a low spatial resolution. These images can be used for various applications such as environmental analysis, urban monitoring and surveying for sustainability. In this study, mineral detection was performed using Worldview-3 satellite imagery. A pansharpening technique was applied to the spatial resolution of the panchromatic image to effectively utilize the VNIR and SWIR bands of Worldview-3 satellite imagery. The following representative similarity analysis techniques were implemented for the mineral detection: the spectral angle mapper (SAM), spectral information divergence (SID) and the normalized spectral similarity score (NS3). In addition, pixels that could be estimated to indicate minerals were calculated by applying an empirical threshold to each similarity analysis result. A majority voting technique was applied to the results of each similarity analysis and pixels estimated to indicate minerals were finally selected. The results of each similarity analysis were compared to evaluate the accuracy of the proposed methods. From that comparison, it could be confirmed that false negative and false positive rates decreased when the methods proposed in the present study were applied.

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“…SID indexes can be put in order using Equation (3). Hence, the larger the value of the SID index, the higher the similarity between pixels [19,20].…”

Section: Spectral Information Divergencementioning

confidence: 99%

Mineral Detection Using Sharpened VNIR and SWIR Bands of Worldview-3 Satellite Imagery

Park

Choi

2021

Sustainability

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“…The frequency domain has been exploited to derive spectral analysis. A spectral similarity measure was suggested [31] using the magnitude values of the first few low-frequency components for spectral signature. Harmonic analysis was also used to describe the spectral reflectance and recognize objects [32].…”

Section: Hsi For General Applicationsmentioning

confidence: 99%

Pavement Crack Detection from Hyperspectral Images Using a Novel Asphalt Crack Index

et al. 2020

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Detection of road pavement cracks is important and needed at an early stage to repair the road and extend its lifetime for maintaining city roads. Cracks are hard to detect from images taken with visible spectrum cameras due to noise and ambiguity with background textures besides the lack of distinct features in cracks. Hyperspectral images are sensitive to surface material changes and their potential for road crack detection is explored here. The key observation is that road cracks reveal the interior material that is different from the worn surface material. A novel asphalt crack index is introduced here as an additional clue that is sensitive to the spectra in the range 450–550 nm. The crack index is computed and found to be strongly correlated with the appearance of fresh asphalt cracks. The new index is then used to differentiate cracks from road surfaces. Several experiments have been made, which confirmed that the proposed index is effective for crack detection. The recall-precision analysis showed an increase in the associated F1-score by an average of 21.37% compared to the VIS2 metric in the literature (a metric used to classify pavement condition from hyperspectral data).

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“…The frequency domain has been exploited to derive spectral metrics. A spectral similarity measure was suggested [22] using the magnitude values of the first few low-frequency components for spectral signature. Harmonic analysis was also used to describe the spectral reflectance and recognize objects [23].…”

Section: Physics-based Approachesmentioning

confidence: 99%

Hyperspectral Imaging for Autonomous Inspection of Road Pavement Defects

Abdellatif¹,

Peel²,

Cohn³

et al. 2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Autonomous inspection of roads is gaining interest to improve the efficiency of road repair and maintenance. In this paper we will be showing the potential for using Hyper Spectral Cameras, HSC, to identify road defects. The key idea of this paper is that cracks in the road show the interior material of road pavement which have different chemical composition from the surface materials due to surface wear. Material changes of the road surface give rise to a spectral signature that can be easily detected in HSC images. This condition facilitates the detection of cracks and potholes, which can be difficult if working in the visible spectrum domain only. We report on experiments with a HSC to identify the road material changes and their association to cracks and potholes. A new metric is devised to measure the amount of metal oxides and associate its absence to the appearance of cracks. The metric is shown to be more discriminative than previous indicators in the literature.

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Application of the Frequency Spectrum to Spectral Similarity Measures

Cited by 15 publications

References 55 publications

Mineral Detection Using Sharpened VNIR and SWIR Bands of Worldview-3 Satellite Imagery

Mineral Detection Using Sharpened VNIR and SWIR Bands of Worldview-3 Satellite Imagery

Pavement Crack Detection from Hyperspectral Images Using a Novel Asphalt Crack Index

Hyperspectral Imaging for Autonomous Inspection of Road Pavement Defects

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