Abstract

We compare features and classification methods to locate deciduous trees in images. From this comparison we conclude that a back-propagation neural network achieves better classification results than the other classifiers we tested. Our analysis of the relevance of 51 features from seven feature extraction methods based on the graylevel co-occurrence matrix, Gabor filters, fractal dimension, steerable filters, the Fourier transform, entropy, and color shows that each feature contributes important information. We show how we obtain a 13-feature subset that significantly reduces the feature extraction time while retaining most of the complete feature set's power and robustness. The best subsets of features were found to be combinations of features of each of the extraction methods. Methods for classification and feature relevance determination that are based on the covariance or correlation matrix of the features (such as eigenanalyses or linear or quadratic classifiers) generally cannot be used, since even small sets of features are usually highly linearly redundant, rendering their covariance or correlation matrices too singular to be invertible. We argue that representing deciduous trees and many other objects by rich image descriptions can significantly aid their classification. We make no assumptions about the shape, location, viewpoint, viewing distance, lighting conditions, and camera parameters, and we only expect scanning methods and compression schemes to retain a “reasonable” image quality.