Spectral texture classification of high-resolution satellite images for the state forest inventory in Russia

Egor V. Dmitriev; Anton A. Sokolov; Vladimir V. Kozoderov; Hervé Delbarre; Petr G. Melnik; Sergey A. Donskoi

doi:10.1117/12.2532965

21 October 2019 Spectral texture classification of high-resolution satellite images for the state forest inventory in Russia

Egor V. Dmitriev, Anton A. Sokolov, Vladimir V. Kozoderov, Hervé Delbarre, Petr G. Melnik, Sergey A. Donskoi

Author Affiliations +

Proceedings Volume 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI; 111491J (2019) https://doi.org/10.1117/12.2532965
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

State forest inventory (SFI) program has been adopted for obtaining and updating forest inventory data in the territory of the Russian Federation. In the course of SFI, circular permanent sample plots (PSP) of constant radii are laid out. The number of PSP depends on the representation of plantations in the work object. Currently, high resolution satellite images are used within the framework of SFI mainly for updating the cartographic basis of forest inventory. We propose a method for joint processing of multispectral and panchromatic satellite images of high spatial resolution in order to retrieve the species composition and age classes of mixed forest stands. The method consists of several steps. The preprocessing step includes calibration, correction, matching satellite and ground data. The next step is obtaining regional specific training data from PSP measurements. For the retrieval of forest parameters, we propose the recognition method based on the modified ECOC (Error Correcting Output Codes) classifier and regularized stepwise forward feature selection. This allows us to combine spectral and texture features more effectively. The last postprocessing step is the correction of classification results using the methods of mathematical morphology. The proposed method contributes to the automation of updating data on species composition and age classes of forest stands and allows improving the efficiency of SFI works. The accuracy of the retrieval of the species composition of mixed forests using high-resolution satellite images is comparable with the accuracy of the standard archival ground inventory data.

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Egor V. Dmitriev, Anton A. Sokolov, Vladimir V. Kozoderov, Hervé Delbarre, Petr G. Melnik, and Sergey A. Donskoi "Spectral texture classification of high-resolution satellite images for the state forest inventory in Russia", Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 111491J (21 October 2019); https://doi.org/10.1117/12.2532965

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KEYWORDS

Satellite imaging

Satellites

Binary data

Earth observing sensors

Feature selection

High resolution satellite images

Image classification

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