Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders, Belgium - Van Coillie, Frieke M.B.; Verbeke, Lieven P.C.; De Wulf, Robert R. (Remote sensing of environment, 2007)
Obtaining detailed information about the amount of forest cover is an important issue for governmental policy and forest management. This paper presents a new approach to update the Flemish Forest Map using IKONOS imagery. The proposed method is a three-step object-oriented classification routine that involves the integration of 1) image segmentation, 2) feature selection by Genetic Algorithms (GAs) and 3) joint Neural Network (NN) based object-classification. The added value of feature selection and neural network combination is investigated. Results show that, with GA-feature selection, the mean classification accuracy (in terms of Kappa Index of Agreement) is significantly higher ( p < 0.01) than without feature selection. On average, the summed output of 50 networks provided a significantly higher ( p < 0.01) classification accuracy than the mean output of 50 individual networks. Finally, the proposed classification routine yields a significantly higher ( p < 0.01) classification accuracy as compared with a strategy without feature selection and joint network output. In addition, the proposed method showed its potential when few training data were available.