A remote sensing image segmentation method, relating to the technical field of image detection and image categorization. The method comprises: acquiring a target image, and by means of each layer of a deep convolutional neural network, extracting layered features of the target image (S100); determining target features of the target image sequentially on the basis of layered features extracted by two differently scaled convolutional layers (S200); determining an object category for each object in the target image on the basis of the target features, and segmenting the target image on the basis of the object categories (S300). In the present method, a convolutional neural network is used to extract features of multiple scales from shallow to deep, and in a skipping manner, deep layer, intermediate layer, and shallow layer features are fused so as to acquire high-level semantic information and low-level geometric information, and then by means of upsampling, spatial output is accomplished, in order to achieve the goal of pixel-level segmentation, improving segmentation precision in remote sensing in the presence of interfering factors such as clouds or strong light.
REMOTE SENSING IMAGE SEGMENTATION METHOD AND DEVICE, AND STORAGE MEDIUM AND SERVER