Scene classification of high-resolution remote sensing image is one of the important tasks in interpreting remote sensing image information. In order to extract the target information accurately, we propose a high-resolution remote sensing image scene classification method based on salient features combined with deep convolutional neural network (DCNN) to solve the problems of complex background, diverse targets, and difficult to distinguish between target information and background information in the classification of high-scoring remote sensing image scenes. First, K-means clustering algorithm and super-pixel segmentation algorithm are used to generate the color spatial distribution map and color contrast map of the image, and the different contrast maps are fused to get the saliency map. Then, the features in the saliency map are enhanced through logarithmic transformation, and the adaptive threshold segmentation method is used to improve the discrimination of the target and divide the target area and the background area, and extract the area of interest. Finally, a DCNN model is constructed to extract deep semantic features and classification, and the obtained features are input into the network model for training and classification. Experimental results show that the method can effectively distinguish the main target information from the background information and reduce the interference of irrelevant information. The classification accuracy of the method on the UC-Merced data set and WHU-RS data set are 96.10% and 95.84%, respectively.