Analysis of the Impact of Different Feature Extraction Strategies on Scene Change Detection Performance
With the rapid development of remote sensing technology,change detection based on remote sensing images is widely used in many fields,such as land and resource management,ground object change,ecological monitoring,etc. Feature extraction is a key link in change detection,and how to obtain the optimal feature extraction strategy is a difficult point in current research. To solve this problem,the influence of commonly used texture features,color features,spectral features,shape features and convolutional neural network (CNN) features on the performance of high-resolution remote sensing image scene change detection is analyzed. The experi-mental results based on the MtS-WH standard data set show that the overall performance of CNN feature change detection is the high-est,and the color feature has good performance on farmland and other vegetation change detection,texture features and shape features have better detection performance for building changes,while spectral features have better detection performance for ground changes.
万方数据
维普
