Improvement of panchromatic IKONOS image classification based on structural neural network

Remote sensing image classification plays an important role in urban studies. This work presents a method for panchromatic image classification for urban land-use mapping with neural network. A structural neural network with backpropagation through structure algorithm is conducted for image classification. With wavelet decomposition, an object´s features in wavelet domain can be extracted. Therefore, the pixel´s spectral intensity and its wavelet features are combined as feature sets that are used as attributes for the neural network. Then, an object´s content can be represented by a tree structure and the nodes of the tree can be represented by the attributes. In order to prove the efficacy of the proposed method, experiments based on proposed method and maximum likelihood classification are carried out respectively. 2510 pixels for four classes, road, building, grass and water body, are selected for training a neural network. 19498 pixels are selected for testing. The four categories can be perfectly classified using the training data. The classification rate based on testing data reaches 99.68%. Experimental results show the proposed approach is much more effective and reliable.