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

被引:129
作者
Carvalho Junior, Osmar A. [1 ]
Guimaraes, Renato F. [1 ]
Gillespie, Alan R. [2 ]
Silva, Nilton C. [3 ]
Gomes, Roberto A. T. [1 ]
机构
[1] Univ Brasilia UnB, Dept Geog, BR-70910900 Brasilia, DF, Brazil
[2] Univ Washington, Dept Earth & Space Sci, Seattle, WA 98195 USA
[3] Ctr Univ Anapolis Unievangelica, BR-75083515 Anapolis, Go, Brazil
关键词
change-detection; Spectral Correlation Mapper; Spectral Angle Mapper; Mahalanobis distance; Euclidean distance; bi-temporal; LAND-COVER CLASSIFICATION; MISREGISTRATION; REFLECTANCE; ACCURACY; IMAGES;
D O I
10.3390/rs3112473
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页码:2473 / 2493
页数:21
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