Improved neural network algorithm for classification of UAV imagery related to Wenchuan earthquake

Abstract

When Wenchuan earthquake struck, the terrain of the region changed violently. Unmanned aerial vehicles (UAV) remote sensing is effective in extracting first hand information. The high resolution images are of great importance in disaster management and relief operations. Back propagation (BP) neural network is an artificial neural network which combines multi-layer feed-forward network and error back-propagation algorithm. It has a strong input-output mapping capability, and does not require the object to be identified obeying certain distribution law. It has strong non-linear features and error-tolerant capabilities. Remotely-sensed image classification can achieve high accuracy and satisfactory error-tolerant capabilities. But it also has drawbacks such as slow convergence speed and can probably be trapped by local minimum points. In order to solve these problems, we have improved this algorithm through setting up self-adaptive training rate and adding momentum factor. UAV high-resolution aerial image in Taoguan District of Wenchuan County is used as data source. First, we preprocess UAV aerial images and rectify geometric distortion in images. Training samples were selected and purified. The image is then classified using the improved BP neural network algorithm. Finally, we compare such classification result with the maximum likelihood classification (MLC) result. Numerical comparison shows that the overall accuracy of maximum likelihood classification is 83.8%, while the improved BP neural network classification is 89.7%. The testing results indicate that the latter is better.