Satellite Video Motion Detection Supported by Decision Tree Weak Classification

For the satellite video motion detection, two issues are harmful to the subsequent application. One is the local parallax"pseudo-motion", and the other is"Ghost pseudo-motion", which may lead to serious error detection. For these two issues, a decision tree-supported local differentiated updating background subtraction method is proposed. Firstly, the initial background model is established. One other parameter, the updating factor, is added based on the ViBe model. Each pixel in the model has a unique dynamic updating factor. In the next place, we perform the pixel-wise motion detection based on the modified model. The detection results are segmented into targets by using the connected component analysis-labeling method. Afterwards, the decision tree model is established based on the target characteristics. And the targets can be weekly classified into different categories. In the end, the background model samples and the factor parameters are differentiated updated according to the categories results. Using SkySat and Jilin No. 1 video data to do experiments. The experimental results show that our proposed method has favourable effect and performance, with the recall rate of results superior to 0.909. Comparing with the classical ViBe method, the error detection removal rate is superior to 90%.