Methods of Land Cover Classification Using Worldview-3 Satellite Images in Land Management

Abstract

Modern geoinformation technologies, such as remote sensing satellite missions and classification methods, are becoming increasingly prominent in land cover classification. Due to the emergence of high spatial resolution missions with improved temporal and spectral resolutions, such as Worldview-3, this approach enabled new possibilities in land management. To provide an in-depth analysis of such possibilities, this study reviews methods of land cover classification using WorldView-3 satellite imagery. With 29 different spectral channels and a spatial resolution of 1.2 m, Worldview-3 multispectral satellite images represent the most modern currently publicly available commercial multispectral images. The classification of multispectral images is performed to facilitate the identification and recognition of objects in the images. Analyzed classification methods are: supervised (semi-automatic) classification methods, unsupervised (automatic) classification methods, and object-based classification methods. In order to increase the accuracy in land cover studies, it was determined as necessary to develop automatic methods that rely on a combination of controlled and uncontrolled classification methods. This approach enables the automatic determination of samples for conducting supervised classifications of interest for land management.

Keywords

• high-resolution imagery
• multispectral imagery
• OBIA
• remote sensing
• segmentation
• supervised classification