Spectrally segmented principal component analysis of hyperspectral imagery for mapping invasive plant species

Tsai, Fuan; Lin, E. K.; Yoshino, Katsumi

doi:10.1080/01431160600887706

Cited by 102 publications

(56 citation statements)

References 25 publications

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“…Indeed the ties to major airborne campaigns have been loosened and researchers can increasingly focus on specific areas and targets, perhaps using basic imagery acquired by basic camera systems from an airborne platform. This may be of value to a range of applications including studies with very focused interest, such as those concerned with studies of introduced and invasive species (Ramsey et al, 2002;Underwood et al, 2003;Ellis and Wang, 2006;D"Iorio et al, 2007;Kakembo et al, 2007;Tsai et al, 2007;Auda et al, 2008;Singh and Glenn, 2009). Such studies may also benefit from a more tailored approach to mapping, in which effort and resources are focused on the thematic information of interest rather than the entire land cover mosaic of the region (Sanchez-Hernandez et al, 2007a).…”

mentioning

confidence: 99%

An overview of recent remote sensing and GIS based research in ecological informatics

Boyd

Foody

2011

Ecological Informatics

114

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This article provides an overview of some of the recent research in ecological informatics involving remote sensing and GIS. Attention focuses on a selected range of issues including topics such as the nature of remote sensing data sets, issues of accuracy and uncertainty, data visualization and sharing activities as well as developments in aspects of ecological modeling research. It is shown that considerable advances have been made over recent years and foundations for future research established. BackgroundGeotechnology has been couched as one of the three "mega-technologies" of the 21 st century, along with nanotechnology and biotechnology (Gewin, 2004). On the cusp of the second decade of the 21 st century it is evident that recent developments in the geotechnologies of Geographic Information Science (GIS) and remote sensing have had a substantial impact on ecological research, providing spatial data and associated information to enable the further understanding of ecological systems (Rundell et al., 2009). In a digital society there is sometimes an expectation that information is just a click away and this may apply to spatial information in the domain of ecological informatics. Indeed, it has been in the last five years that developments in and relating to the fields of GIS and remote sensing, such as those seen in internet technologies (e.g., Web 2.0; high performance communication networks; Brunt et al., 2007), sensing technology (e.g., quantum well infrared photodetectors; Krabach, 2000), data standards and interoperability (e.g., OGC® KML 2.2) and spatially explicit modeling (e.g., Osborne et al., 2007), have resulted in the revelation of previously unobservable phenomena and posing of what might be termed second generation of ecological questions. During this time we have also seen more support for the open exchange of data and software and infrastructure for location based services. This paper serves as a review of some of the recent developments in GIS and remote sensing that have been or promises to be of benefit to ecological informatics. The subjects addressed are necessarily selective and limited in scope, drawing on topics of interest to us and, we hope, to you.

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confidence: 99%

An overview of recent remote sensing and GIS based research in ecological informatics

Boyd

Foody

2011

Ecological Informatics

114

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“…Nevertheless, hyperspectral sensors when compared to multispectral sensors may cause new difficulties in data processing and analysis that could hinder the success of certain research endeavours. One of the challenges in hyperspectral data handling and analysis that requires more attention is the amount of data, as well as high data dimensionality (Khurshid et al, 2006;Tsai et al, 2007). These concerns alone may result in low accuracies when classifying land features.…”

Section: Data Compression Using Principal Components (Pc)mentioning

confidence: 99%

“…Since vegetation contains diverse structure types, the characteristics, including absorption and reflectance across different portions of wavelengths, also differ (Bell & Baranoskie, 2004). If PCA were to be generated for each range of the spectrum-visible and near-infrared (VNIR), shortwave infrared (SW1 and SW2), the significant information is better maintained for each separate portion rather than CPCA which is generated across an entire spectrum (Tsai et al, 2007). Ideally, a SPCA should produce greater accuracy results for classification purposes.…”

Section: Data Compression Using Principal Components (Pc)mentioning

confidence: 99%

“…Use of hyperspectral imaging potentially gives researchers (Datt et al, 2003;Khurshid et al, 2006;Tsai et al, 2007) the opportunity to accomplish complex analyses that are often difficult with multispectral imaging. One advantage is maintaining a greater amount of spectral bands to define land cover in dense urban regions.…”

Section: Data Compression Using Principal Components (Pc)mentioning

confidence: 99%

“…Ultimately, hyperspectral analysis involves crucial attention to data compression. By not compressing hyperspectral data and only selecting a few bands for analysis, the continuity of the spectral data and the sensors full capacity would not be considered (Tsai et al, 2007).…”

Section: Data Compression Using Principal Components (Pc)mentioning

confidence: 99%

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Comparing Hyperspectral and Multispectral Imagery for Land Classification of the Lower Don River, Toronto

Ferrato¹,

Forsythe²

2013

JGG

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Urban greenspace is important for the health of cities. Up-to-date databases and information are vital to maintain and monitor growth in cities. During the last decade, advances in spaceborne hyperspectral sensors have resulted in some advantages being gained over multispectral sensors for land cover monitoring (due to increased spectral resolution). The objective of this research was to compare Earth Observing-1 (EO-1) Hyperion hyperspectral data to Landsat 5 Thematic Mapper (TM) and Satellite Probatoire d'Observation de la Terre (SPOT) 5 multispectral data for land cover classification in a dense urban landscape. For comparative analysis, orthorectified aerial imagery provided by the Toronto and Region Conservation Authority (TRCA) was used as ground truth data for accuracy assessment. This study utilized conventional and segmented principal components (CPCA and SPCA) for data compression on the Hyperion imagery, and used principal components analysis (PCA) as a visual enhancement technique for multispectral imagery. Image processing including the generation of the normalized difference vegetation index (NDVI), and mean texture was also performed for both Landsat and SPOT sensors. Unsupervised iterative self-organizing data analysis (ISODATA) classification procedures were performed on all images to produce land cover classification maps for a portion of the Lower Don River in Toronto, Ontario, Canada. Experiments conducted in this research demonstrated that hyperspectral imagery produced a higher overall accuracy (5-6% better) than multispectral data with the same resolution for defining vegetation cover. In addition, SPOT generated greater accuracy results than Landsat or Hyperion for vegetation classes. It was found that conventional Hyperion and segmented Hyperion methods outperformed the Landsat 5 TM sensor for vegetation differences (for tree canopy and open green spaces).

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Segmented-Truncated-SVD for Effective Feature Extraction in Hyperspectral Image Classification

Rahman

Ahmed

Haque

et al. 2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Spectrally segmented principal component analysis of hyperspectral imagery for mapping invasive plant species

Cited by 102 publications

References 25 publications

An overview of recent remote sensing and GIS based research in ecological informatics

An overview of recent remote sensing and GIS based research in ecological informatics

Comparing Hyperspectral and Multispectral Imagery for Land Classification of the Lower Don River, Toronto

Segmented-Truncated-SVD for Effective Feature Extraction in Hyperspectral Image Classification

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