Geometric correction of airborne imaging spectrometer data

Mäkisara, Kai; Karna, J.-P.; Lohi, Anssi

doi:10.1109/igarss.1994.399282

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…The AISA Eagle preprocessing software 4 provides for automatic geometric correction, rectification, mosaicking, and calculation of at-platform radiance by applying calibration coefficients referenced to well-characterized spectroradiometric targets. The algorithm uses the DGPS and attitude information from the inertial navigation system to perform geometric, georeferencing, and mosaicking operations (Mäkisara et al 1994).…”

Section: Methodsmentioning

confidence: 99%

Detecting and Mapping Four Invasive Species along the Floodplain of North Platte River, Nebraska

Narumalani

Mishra

Wilson³

et al. 2009

Weed technol.

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Geospatial technologies are increasingly important tools used to assess the spatial distributions and predict the spread of invasive species. The objective of our research was to quantify and map four dominant invasive plant species, including saltcedar, Russian olive, Canada thistle, and musk thistle, along the flood plain of the North Platte River corridor within a 1-mile (1.6-km) buffer. Using the Airborne Imaging Spectroradiometer for Applications (AISA) hyperspectral imager (from visible to near infrared), we evaluated an image processing technique known as spectral angle mapping for mapping the invasive species distribution. A minimum noise fraction algorithm was used to remove the inherent noise and redundancy within the dataset during the classification. The classification algorithm applied on the AISA image revealed five categories of invasive species distribution including (1) saltcedar; (2) Russian olive; and a mix of (3) Canada and musk thistle, (4) Canada/musk thistle and reed canary grass, or (5) Canada/musk thistle, saltcedar, and reed canary grass. Validation procedures confirmed an overall map accuracy of 74%. Saltcedar and Russian olive classes showed producer and user accuracies of greater than 90%, whereas the mixed categories revealed accuracy values of between 35 and 74%. The immediate benefit of this research has been to provide information on the spatial distribution of invasive species to land managers for implementation of management programs. In addition, these data can be used to establish a baseline of the species distributions for future monitoring and control efforts.

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Section: Methodsmentioning

confidence: 99%

Detecting and Mapping Four Invasive Species along the Floodplain of North Platte River, Nebraska

Narumalani

Mishra

Wilson³

et al. 2009

Weed technol.

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show abstract

“…The AISA pre-processing software provides for the automatic geometric correction, rectification, mosaicking, and calculation of radiance or apparent atplatform reflectance. The algorithm uses the DGPS and attitude information from the INS to perform geometric, georeferencing and mosaicking operations (Mäkisara et al, 1994).…”

Section: Initial Investigation and Data Acquisitionmentioning

confidence: 99%

A Comparative Evaluation of ISODATA and Spectral Angle Mapping for the Detection of Saltcedar Using Airborne Hyperspectral Imagery

Narumalani

Mishra

Burkholder

et al. 2006

Geocarto International

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Nonnative plant species often cause adverse ecological and environmental impacts on the indigenous species of an area. Remote sensing methods have had mixed successes in providing spatial information on the distribution characteristics of specific vegetation species. Such research has been limited to broad-band satellite based sensor systems whose spatial and spectral capabilities may not be adequate. Our research focuses on using hyperspectral data and innovative image processing techniques for mapping specific invasive species based on their spectral characteristics. Using the Airborne Imaging Spectroradiometer for Applications (AISA) hyperspectral imager (from Visible to Near Infrared (VNIR)). This research evaluated two methods of processing hyperspectral imagery including the Iterative Self-Organizing Data (ISODATA) algorithm and Spectral Angle Mapping (SAM) for detecting saltcedar (Tamarix sp.) in Lake Meredith Recreational Area, Texas. A Minimum Noise Fraction (MNF) algorithm was used to remove the inherent noise and redundancy within the dataset during the SAM classification. Validation procedures revealed higher accuracies for the SAM method (83%) when compared to ISODATA (76%) in identifying saItcedar. The immediate benefit of this research has been to provide improved information on the spatial extent and density of saltcedar to land managers for the effective implementation of management programs to control this invasive plant.

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Best Practices for Urban Hyperspectral Remote Sensing Data Acquisition and Processing

Lulla

Jensen

2016

Urban Sustainability: Policy and Praxis

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Geometric correction of airborne imaging spectrometer data

Cited by 6 publications

References 3 publications

Detecting and Mapping Four Invasive Species along the Floodplain of North Platte River, Nebraska

Detecting and Mapping Four Invasive Species along the Floodplain of North Platte River, Nebraska

A Comparative Evaluation of ISODATA and Spectral Angle Mapping for the Detection of Saltcedar Using Airborne Hyperspectral Imagery

Best Practices for Urban Hyperspectral Remote Sensing Data Acquisition and Processing

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