Automatic Mapping of Linearwoody Vegetation Features in Agricultural Landscapes

Development of automatic methods for agricultural mapping and monitoring using remotely sensed imagery has been an important research problem. We describe algorithms that exploit the spectral, textural and object shape information using hierarchical feature extraction and decision making steps for automatic mapping of linear strips of woody vegetation in very high-resolution imagery. First, combinations of multispectral values and multi-scale Gabor and entropy texture features are used for training pixel level statistical classifiers for characterizing individual trees and tree groups with respect to their surroundings. Then, decisions based on object level texture features and morphological shape analysis provide the final detection of woody vegetation having a linear structure. Experiments on QuickBird imagery from different sites show that the proposed algorithms provide good localization of linear strips of woody vegetation in different landscapes.