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Abstracting GIS Layers from Hyperspectral Imagery

Name: Abstracting GIS Layers from Hyperspectral Imagery
Brand: Biblioscholar
SKU: 9781288316441
Price: 18.69 EUR
Availability: InStock

Автор: Torsten E Howard

Език:

Корици: С меки корици

Издател: Biblioscholar

Наличност: Външен склад

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18.69 € 36.55 лв

Modern warfare methods in the urban environment necessitates the use of multiple layers of sensors t...

Информация за книгата

Автор

Torsten E Howard

Език

Английски език

Корици

Книга - С меки корици

Издадена

2012

страници

158

EAN

9781288316441

ISBN

9781288316441

Enbook ID

08243263

Издател

Biblioscholar

Теглоt

295

Размери

189 x 246 x 9

Пълно описание

Modern warfare methods in the urban environment necessitates the use of multiple layers of sensors to manage the battle space. Hyperspectral imagers are one possible sensor modality to provide remotely sensed images that can be converted into Geographic Information Systems (GIS) layers. GIS layers abstract knowledge of roads, buildings, and scene content and contain shape files that outline and highlight scene features. Creating shape files is a labor-intensive and time-consuming process. The availability of shape files that reflect the current configuration of the area of interest significantly enhances Intelligence Preparation of the Battlespace (IPB). The solution presented in this thesis is a novel process to automate the creation of shape files by exploiting the spectral-spatial relationship of a hyperspectral image cube. It is assumed that "a-priori" endmember spectra, a spectral database, or specific scene knowledge is not available. The topological neighborhood of a Self Organizing Map (SOM) is segmented and used as a spectral filter to produce six initial object maps that are spatially processed with logical and morphological operations. A novel road finding algorithm connects road segments under significantly tree-occluded roadways into a contiguous road network. The manual abstraction of GIS shape files is improved into a semi-automated process. The resulting shape files are not susceptible to deviation from orthorectified imagery as they are produced directly from the hyperspectral imagery. The results are eight separate high-quality GIS layers (Vegetation, Non-Tree Vegetation, Trees, Fields, Buildings, Major Buildings, Roadways, and Parking Areas) that follow the terrain of the hyperspectral image and are separately and automatically labeled.

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Abstracting GIS Layers from Hyperspectral Imagery

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