Texture-Integrated Classification of Urban Treed Areas in High-Resolution Color-Infrared Imagery

Abstract

lkaditional multispectral classification methods have not provided satisfying results for treed area extraction from highresolution digital imagery because trees are characterized not only by their spectral but also by their textural properties. Treed areas in urban regions are especially dificult to extract due to their small area. Many other urban objects, such as lawn and playgrounds, cause confusion because they display similar, even identical, spectral properties. In this study a texture integrated classification method is proposed. To effectively extract tree textural features and eliminate noise, an algorithm of conditional variance detection is developed, which consists of a directional variance detection and a local variance detection. This algorithm detects tree features with higher accuracy than common texture algorithms. By integrating the new algorithm with traditional multispectral classification, treed areas in urban regions can be extracted with sufficiently high accuracy. Application of the new approach in different urban areas indicates that the average accuracy of treed area extraction was increased from 67 percent, using a multispectral classification, to 96 percent, using the texture integrated classification.