The invention relates to a target detection method for an SAR (
Synthetic Aperture Radar)
image based on deep
reinforcement learning, which comprises the steps of S1, setting the number of iterations,and sequentially
processing images in a
training set in each
iteration process; S2, inputting an image from the
training set, and generating training samples by using the Markov
decision process; S3,randomly selecting a certain number of samples, training the Q-network by adopting a
gradient descent method, obtaining the state of a reduced observation area, generating a next sample until a presettermination condition is met, and terminating the
image processing process; S4, returning back to the step S2, continuing to input the next image from the
training set until all images are completelyprocessed, and terminating the current
iteration process; S5, continuing the next
iteration process until the set number of iterations is met, and determining network parameters of the Q-network; andS6, performing target detection on an image in a
test set through the trained Q-network, and outputting a detection result. The target detection method obtains good detection accuracy in target detection for the SAR image.