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Spectral signature generalization and expansion can improve the accuracy of satellite image classification.

Laborte AG, Maunahan AA, Hijmans RJ - PLoS ONE (2010)

Bottom Line: When applying signatures to the images they were derived from, signature expansion improved accuracy relative to the conventional method, and variability in accuracy declined markedly.In contrast, signature generalization did not improve classification.When applying signatures to images of other years (temporal extension), the conventional method, using a signature derived from a single image, resulted in very low classification accuracy.

View Article: PubMed Central - PubMed

Affiliation: International Rice Research Institute, Los Baños, Laguna, Philippines. aglaborte@gmail.com

ABSTRACT
Conventional supervised classification of satellite images uses a single multi-band image and coincident ground observations to construct spectral signatures of land cover classes. We compared this approach with three alternatives that derive signatures from multiple images and time periods: (1) signature generalization: spectral signatures are derived from multiple images within one season, but perhaps from different years; (2) signature expansion: spectral signatures are created with data from images acquired during different seasons of the same year; and (3) combinations of expansion and generalization. Using data for northern Laos, we assessed the quality of these different signatures to (a) classify the images used to derive the signature, and (b) for use in temporal signature extension, i.e., applying a signature obtained from data of one or several years to images from other years. When applying signatures to the images they were derived from, signature expansion improved accuracy relative to the conventional method, and variability in accuracy declined markedly. In contrast, signature generalization did not improve classification. When applying signatures to images of other years (temporal extension), the conventional method, using a signature derived from a single image, resulted in very low classification accuracy. Signature expansion also performed poorly but multi-year signature generalization performed much better and this appears to be a promising approach in the temporal extension of spectral signatures for satellite image classification.

Show MeSH
Change in accuracy resulting from signature expansion.Dense and secondary forests or old fallow trees (A), shrubs and grasses, recently fallowed (B) and agricultural land (C) are the three most common land cover classes considered in this study.
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pone-0010516-g004: Change in accuracy resulting from signature expansion.Dense and secondary forests or old fallow trees (A), shrubs and grasses, recently fallowed (B) and agricultural land (C) are the three most common land cover classes considered in this study.

Mentions: The change in accuracy when comparing classification using signature expansion with classification with single-date signatures depended on the land cover class (Figure 4). For the forest and old fallow class, 60% of the cases had a higher accuracy, whereas for shrubs and grasses 80% of the cases have a higher accuracy with signature expansion. For agricultural land, however, only about half of the cases have a higher accuracy with signature expansion.


Spectral signature generalization and expansion can improve the accuracy of satellite image classification.

Laborte AG, Maunahan AA, Hijmans RJ - PLoS ONE (2010)

Change in accuracy resulting from signature expansion.Dense and secondary forests or old fallow trees (A), shrubs and grasses, recently fallowed (B) and agricultural land (C) are the three most common land cover classes considered in this study.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2865537&req=5

pone-0010516-g004: Change in accuracy resulting from signature expansion.Dense and secondary forests or old fallow trees (A), shrubs and grasses, recently fallowed (B) and agricultural land (C) are the three most common land cover classes considered in this study.
Mentions: The change in accuracy when comparing classification using signature expansion with classification with single-date signatures depended on the land cover class (Figure 4). For the forest and old fallow class, 60% of the cases had a higher accuracy, whereas for shrubs and grasses 80% of the cases have a higher accuracy with signature expansion. For agricultural land, however, only about half of the cases have a higher accuracy with signature expansion.

Bottom Line: When applying signatures to the images they were derived from, signature expansion improved accuracy relative to the conventional method, and variability in accuracy declined markedly.In contrast, signature generalization did not improve classification.When applying signatures to images of other years (temporal extension), the conventional method, using a signature derived from a single image, resulted in very low classification accuracy.

View Article: PubMed Central - PubMed

Affiliation: International Rice Research Institute, Los Baños, Laguna, Philippines. aglaborte@gmail.com

ABSTRACT
Conventional supervised classification of satellite images uses a single multi-band image and coincident ground observations to construct spectral signatures of land cover classes. We compared this approach with three alternatives that derive signatures from multiple images and time periods: (1) signature generalization: spectral signatures are derived from multiple images within one season, but perhaps from different years; (2) signature expansion: spectral signatures are created with data from images acquired during different seasons of the same year; and (3) combinations of expansion and generalization. Using data for northern Laos, we assessed the quality of these different signatures to (a) classify the images used to derive the signature, and (b) for use in temporal signature extension, i.e., applying a signature obtained from data of one or several years to images from other years. When applying signatures to the images they were derived from, signature expansion improved accuracy relative to the conventional method, and variability in accuracy declined markedly. In contrast, signature generalization did not improve classification. When applying signatures to images of other years (temporal extension), the conventional method, using a signature derived from a single image, resulted in very low classification accuracy. Signature expansion also performed poorly but multi-year signature generalization performed much better and this appears to be a promising approach in the temporal extension of spectral signatures for satellite image classification.

Show MeSH