Three methods for estimating subpixel cover fractions in coarse resolution imagery

by Damon, Diane L.

Abstract (Summary)
Coarse resolution imagery, such as that produced by the MODIS instrument, poses the

challenge of estimating sub-pixel proportions of di erent land cover types. This problem is

di cult because of the variety and variability of vegetation within individual pixels. This

thesis describes and compares two existing algorithms for estimating sub-pixel fractional

land cover and introduces a new algorithm that estimates sub-pixel cover fractions more

accurately than those currently used. The two existing algorithms are the linear spectral

mixture model, which has previously been applied to coarse-resolution imagery, and arti-

cial neural networks, which have been very successful across a wide range of tasks. The

paper introduces a new regression tree algorithm that directly estimates land cover fractions.

Our implementations of these methods enforce the requirement that the predicted

sub-pixel fractions sum to 1. In addition, we employed internal cross-validation methods to

calibrate the design parameters of the algorithms to optimize their performance separately

on each data set. The methods were tested on real and simulated data from three di erent

studies. The results show that the linear mixture model is signi cantly less accurate in terms of mean squared error compared to the non-linear neural network and regression tree

methods. In addition, we applied an ensemble learning method, bagging, to construct multiple

classi ers and combine their predictions by voting. The experiments demonstrate that

the regression tree algorithm combined with bagging gives the most accurate predictions.

This method proved efficient and easy to implement.

Bibliographical Information:

Advisor:Dietterich, Thomas G.

School:Oregon State University

School Location:USA - Oregon

Source Type:Master's Thesis

Keywords:vegetation mapping remote sensing mathematical models image processing


Date of Publication:05/28/2003

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