The invention relates to the technical field of image detection and image classification, and provides a remote sensing image segmentation method, which comprises the following steps: obtaining a target image, and extracting hierarchical characteristics of the target image through each convolutional layer of a deep convolutional neural network; determining target features of the target image basedon the hierarchical features extracted from the convolution layers of the two different scales in sequence; and determining the object category of each object in the target image according to the target feature, and segmenting the target image according to the object category. According to the invention, the convolutional neural network is adopted to extract multi-scale features from shallow to deep; and deep, middle and shallow features are fused in a jumping mode to obtain high-level semantic information and low-level geometric information, and then spatial output is realized through up-sampling, so that the purpose of pixel level segmentation is achieved, and the segmentation precision of the remote sensing image under cloud, strong illumination and other interference factors is improved.