Multi-Source Image Classification

Hillary Tribby; James Kroll; Daniel Unger; I-Kuai Hung; Hans Michael Williams

Multi-Source Image Classification

Hillary Tribby, James Kroll, Daniel Unger, I-Kuai Hung, Hans Michael Williams

Forestry

Research output: Contribution to conference › Paper

Abstract

Since multi-source image classifications have the ability to exceed single source processes, such as traditional unsupervised classification methods, this paper will present the integration of four types of data: Lidar, elevation, multispectral and thermal. Using multi-source data and maximum likelihood classification methodology, as well as all possible permutations of data types, this paper will discuss ways to increase accuracy assessments of forested areas in east Texas and find the best combination of data sources.

Original language	Undefined/Unknown
State	Published - Oct 1 2005

Access to Document

Multi-Source Image Classification

http://scholarworks.sfasu.edu/spatialsci_facultypres/19

Cite this

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title = "Multi-Source Image Classification",

abstract = "Since multi-source image classifications have the ability to exceed single source processes, such as traditional unsupervised classification methods, this paper will present the integration of four types of data: Lidar, elevation, multispectral and thermal. Using multi-source data and maximum likelihood classification methodology, as well as all possible permutations of data types, this paper will discuss ways to increase accuracy assessments of forested areas in east Texas and find the best combination of data sources.",

author = "Hillary Tribby and James Kroll and Daniel Unger and I-Kuai Hung and Williams, {Hans Michael}",

year = "2005",

month = oct,

day = "1",

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TY - CONF

T1 - Multi-Source Image Classification

AU - Tribby, Hillary

AU - Kroll, James

AU - Unger, Daniel

AU - Hung, I-Kuai

AU - Williams, Hans Michael

PY - 2005/10/1

Y1 - 2005/10/1

N2 - Since multi-source image classifications have the ability to exceed single source processes, such as traditional unsupervised classification methods, this paper will present the integration of four types of data: Lidar, elevation, multispectral and thermal. Using multi-source data and maximum likelihood classification methodology, as well as all possible permutations of data types, this paper will discuss ways to increase accuracy assessments of forested areas in east Texas and find the best combination of data sources.

AB - Since multi-source image classifications have the ability to exceed single source processes, such as traditional unsupervised classification methods, this paper will present the integration of four types of data: Lidar, elevation, multispectral and thermal. Using multi-source data and maximum likelihood classification methodology, as well as all possible permutations of data types, this paper will discuss ways to increase accuracy assessments of forested areas in east Texas and find the best combination of data sources.

M3 - Paper

ER -