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Remote Sens. 2011, 3(11), 2473-2493; doi:10.3390/rs3112473

A New Approach to Change Vector Analysis Using Distance and Similarity Measures

1
Departamento de Geografia, Universidade de Brasília (UnB), Campus Universitário Darcy Ribeiro, Asa Norte, Brasília, DF CEP. 70910-900, Brazil
2
Department of Earth and Space Sciences, University of Washington, Seattle, WA 98195, USA
3
Centro Universitário de Anápolis (Unievangélica), Avenida Universitária km 3,5, Cidade Universitária, Anápolis, GO CEP. 75083-515, Brazil
*
Author to whom correspondence should be addressed.
Received: 30 September 2011 / Revised: 11 November 2011 / Accepted: 11 November 2011 / Published: 18 November 2011
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Abstract

The need to monitor the Earth’s surface over a range of spatial and temporal scales is fundamental in ecosystems planning and management. Change-Vector Analysis (CVA) is a bi-temporal method of change detection that considers the magnitude and direction of change vector. However, many multispectral applications do not make use of the direction component. The procedure most used to calculate the direction component using multiband data is the direction cosine, but the number of output direction cosine images is equal to the number of original bands and has a complex interpretation. This paper proposes a new approach to calculate the spectral direction of change, using the Spectral Angle Mapper and Spectral Correlation Mapper spectral-similarity measures. The chief advantage of this approach is that it generates a single image of change information insensitive to illumination variation. In this paper the magnitude component of the spectral similarity was calculated in two ways: as the standard Euclidean distance and as the Mahalanobis distance. In this test the best magnitude measure was the Euclidean distance and the best similarity measure was Spectral Angle Mapper. The results show that the distance and similarity measures are complementary and need to be applied together.
Keywords: change-detection; Spectral Correlation Mapper; Spectral Angle Mapper; Mahalanobis distance; Euclidean distance; bi-temporal change-detection; Spectral Correlation Mapper; Spectral Angle Mapper; Mahalanobis distance; Euclidean distance; bi-temporal
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Carvalho Júnior, O.A.; Guimarães, R.F.; Gillespie, A.R.; Silva, N.C.; Gomes, R.A.T. A New Approach to Change Vector Analysis Using Distance and Similarity Measures. Remote Sens. 2011, 3, 2473-2493.

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