Improvement of Panchromatic IKONOS Image Classification Based on Structural Neural Network

摘要

Remote sensing image classification plays an important role in urban studies. In this paper, a method based on structural neural network for panchromatic image classification in urban area with adaptive processing of data structures is presented. Backpropagation Through Structure (BPTS) algorithm is adopted in the neural network that enables the classification more reliable. 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. 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 99.91%. In order to prove the efficiency of the proposed method, experiments based on conventional method, maximum likelihood classification, are implemented as well. Experimental results show the proposed approach is much more effective and reliable.