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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.

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Study area and sources of training and test data.Small black insets are Quickbird images, black dots are approximate areas of field surveys.
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pone-0010516-g001: Study area and sources of training and test data.Small black insets are Quickbird images, black dots are approximate areas of field surveys.

Mentions: The study area, in northern Laos, is covered by the Landsat Worldwide Reference System (WRS 2) path 129 row 46 (Figure 1), and it comprises about 34000 km2. The area is mountainous, with elevations ranging from 274 to 1810 m. The rainy season is from May to October, with an average annual rainfall of about 1400 mm. A typical landscape in this area consists of patches cleared for cropping, recent and old fallow fields, and dense forests, which are usually located at higher elevations and on very steep slopes. There is land under permanent cultivation in the valleys. Rice is the dominant crop. It is usually planted in late May or early June and harvested in October to November. Other crops grown on the sloping fields include sesame and maize. On land used for shifting cultivation, the vegetation is usually cut in January or February and burned in March or April.


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

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

Study area and sources of training and test data.Small black insets are Quickbird images, black dots are approximate areas of field surveys.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0010516-g001: Study area and sources of training and test data.Small black insets are Quickbird images, black dots are approximate areas of field surveys.
Mentions: The study area, in northern Laos, is covered by the Landsat Worldwide Reference System (WRS 2) path 129 row 46 (Figure 1), and it comprises about 34000 km2. The area is mountainous, with elevations ranging from 274 to 1810 m. The rainy season is from May to October, with an average annual rainfall of about 1400 mm. A typical landscape in this area consists of patches cleared for cropping, recent and old fallow fields, and dense forests, which are usually located at higher elevations and on very steep slopes. There is land under permanent cultivation in the valleys. Rice is the dominant crop. It is usually planted in late May or early June and harvested in October to November. Other crops grown on the sloping fields include sesame and maize. On land used for shifting cultivation, the vegetation is usually cut in January or February and burned in March or April.

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