The invention provides an optimization method based on depth Q-learning
remote sensing data
discretization, and the method comprises the steps: carrying out the
feature extraction of a
remote sensing image, obtaining a feature subset, carrying out the preprocessing of the feature subset, obtaining a candidate
breakpoint set, carrying out the binary genetic coding of the candidate
breakpoint set, and constructing a to-be-optimized
discretization scheme; designing a state set of a crossing stage and a state set of a variation stage through the global search capability and the local search capability of a
genetic operator; constructing a
fitness function with a
control variable, and constructing a self-adaptive reward function; a pair of depth Q-learning with the same structure is introduced to calculate the Q value in real time in the transition between the cross state and the variation state, and the cross fragment and the variation point of the
discretization scheme to be optimized are determined. According to the method, a pair of depth Q-learning with the same structure is introduced into the discretization scheme to be optimized, so that the search efficiency of the feature discretization method based on the evolution model is improved, and the number of breakpoints can be further reduced while relatively high classification precision is obtained.