A Novel Perspective Invariant Dissociaton Hash Method for Remotely Sensed Imagery Plusieurs Target Classification

Plusieurs target classification based on remotely sensed imagery is a hot and difficult-ridden topic in recent years. Specifically, the time and space complexity of the classical pattern recognition model in the classification task based on remote sensing image is usually high, and the remotely sensed imagery is usually not
normally
captured. For the former, we introduce the low delay and low storage hash method into the plusieurs target classification of remotely sensed imagery. Aiming at the latter, in order to improve the effectiveness of the proposed model in monitoring perspective transformation data, a dissociation perspective invariant model is constructed. By fusing these two solutions, a perspective invariant dissociaton hash model for remotely sensed imagery plusieurs target classification is obtained. By adding perspective invariant constraint to the supervised dissociaton hash method, our method forces the same type of target to share the same binary code, increases the similarity of the hash code of the same type of target, and thus improves the performance. In order to verify the validity and universality of our method, two different data sets were used for experiments, in which different hash methods and different classification methods are compared. The experimental results show that, compared with the comparison methods, the proposed method improves the accuracy of plusieurs target classes with a small number of samples, thus achieving a higher overall classification accuracy. In addition, the advantages of hash method in low storage make the speed of proposed method improved compared with the classical pattern recognition method.