A remote sensing image semantic segmentation method based on self-supervised comparative learning is provided and includes: building a semantic segmentation network model; pre-training an encoder of the semantic segmentation network model by using unlabeled data; performing a supervised semantic segmentation training on labeled samples to obtain a trained network model after the pre-training; performing a semantic segmentation on a remote sensing image by using the trained network model after the supervised semantic segmentation training; and performing a contrastive learning by using a method of combining global style contrast and local matching contrast in a process of the pre-training. The invention applies contrastive self supervised learning to remote sensing semantic segmentation dataset, proposes a framework of global style and local matching contrastive learning, and forms a remote sensing image semantic segmentation method based on contrastive self-supervised learning, which makes the semantic segmentation method have a wide application range and better segmentation effect. 21005ZCM-AUP2 Building a DeepLab V3+ network model Pre-training an encoder of the DeepLab V3+ network model by using unlabeled data Performing a supervised semantic segmentation training on labeled samples to obtain a trained network model after the pre-training Performing a semantic segmentation on a remote sensing image by using the trained network model after the supervised semantic segmentation training Contrast of matched local region patch patch2 Select local feature t g() 1 ~ Center style correspondence _ Contrast of global style Maximize agreement Maximize agreement S g(•) (•(x t - --- - - avg avg Select local feature * patch1 patch2 21005ZCM-AUP2
REMOTE SENSING IMAGE SEMANTIC SEGMENTATION METHOD BASED ON CONTRASTIVE SELF-SUPERVISED LEARNING