Supervised classification of RESURS MSY-E data for recognizing predominant cone-bearing trees

Victor I. Khamarin; Konstantin T. Protasov; Aleksandr P. Serykh

doi:10.1117/12.370491

19 November 1999 Supervised classification of RESURS MSY-E data for recognizing predominant cone-bearing trees

Victor I. Khamarin, Konstantin T. Protasov, Aleksandr P. Serykh

Proceedings Volume 3983, Sixth International Symposium on Atmospheric and Ocean Optics; (1999) https://doi.org/10.1117/12.370491
Event: Sixth International Symposium on Atmospheric and Ocean Optics, 1999, Tomsk, Russian Federation

Abstract

Digital classifications of RESURS MSY-E data of forest Tomsk region were used. One scene acquired on 22 October 1992 was selected for classification. A supervised approach was used to generate training signatures for input to ERDAS classification. The classes (forest predominate composition) for classification were: (1) 80% cedar (pinus sibirica) + 20% spruce (picea); (2) 80 - 100% cedar without spruce; (3) moorland; (4) recent wood-cutting area; (5) old wood-cutting area; (6) 90 - 100% pine. Parametric and non-parametric decision rule were used. The GIS-compatible classifications provided a comprehensive view and evaluation of the results. Comparison of the results of classification with the forest management inventory data shows a satisfactory agreement.

Citation Download Citation

Victor I. Khamarin, Konstantin T. Protasov, and Aleksandr P. Serykh "Supervised classification of RESURS MSY-E data for recognizing predominant cone-bearing trees", Proc. SPIE 3983, Sixth International Symposium on Atmospheric and Ocean Optics, (19 November 1999); https://doi.org/10.1117/12.370491

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KEYWORDS

Image classification

Satellites

Geographic information systems

Earth observing sensors

Lab on a chip

Mahalanobis distance

Satellite imaging

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