Mine flood perception method based on spatiotemporal generalization modeling in Gabor domai

Compared with coal and rock images, water inrush texture in mine water inrush images has strong variability characteristics in spatiotemporal domain. Existing mine flood identification methods based on image texture features have limited extraction ability and low recognition rate for complex water inrush texture features. For the above problems, a mine flood perception method based on spatiotemporal generalization modeling in Gabor domain is proposed. In the method, Gabor decompositions of training sample images and tested sample images under different receptive fields and directions are carried out separately, and expectation and standard deviation of each sub-band are combined to form learning feature vector and the tested one in a direction. Spatiotemporal generalization modeling of the feature vectors is carried out according to the minimum entropy principle, so as to remove spatiotemporal sensitivity. Angles between each component of the feature vector are taken as the similarity measure of similarity comparison between the learning feature vector and the tested one, so as to realize water inrush recognition. The experimental results show that recognition rate of the method is 89.4% and recognition time is 136 ms, which basically meets real-time perception demand of mine flood.