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28 June 2016 Multivariate classification of landscape metrics in multispectral digital images
Jorge Lira, Sara Morales
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Journal of Applied Remote Sensing, Vol. 10, Issue 2, 026039 (June 2016). https://doi.org/10.1117/1.JRS.10.026039
Abstract
The use of landscape metrics to characterize the morphological behavior of a landscape has been extensive in the last few years. It is recognized that a single metric is insufficient to characterize a landscape. Such metrics are used individually to derive the morphological aspect of a landscape. No joint use of various metrics has been reported. Therefore, we considered the joint use of landscape metrics in a multivariate classification. We derived the value of a number of landscape metrics of patches from several case studies. A multivariate classification was applied using a hierarchical clustering algorithm. The multivariate classification was carried out using the least correlated landscape metrics. To consider the multivariate classification, a normalization of metrics range was used. The results provided the morphological structure of patches grouped into four or five classes. The classes depicted a morphological structure of patches that ranged from simple to very complex. An index was proposed to quantify the morphological structure of a class-patch. Such an index was defined as the average of the landscape metrics for a class-patch. The distance among the class-patch was given by means of the Jeffries–Matusita distance.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2016/$25.00 © 2016 SPIE
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Jorge Lira and Sara Morales "Multivariate classification of landscape metrics in multispectral digital images," Journal of Applied Remote Sensing 10(2), 026039 (28 June 2016). https://doi.org/10.1117/1.JRS.10.026039
Published: 28 June 2016
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KEYWORDS
Image classification
Multispectral imaging
Image segmentation
Agriculture
Fractal analysis
Binary data
Image filtering
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