Abstract:  In aerial images, there is significant difference between the scales of different objects in the same scene, single-scale segmentation often hardly achieves the best classification effect. In order to solve the problem, we proposes a multi-scale fusion model based on attention mechanism. Firstly, extract multi-scale features of the aerial image using dilated convolutions with different sampling rates; then utilize the attention mechanism in the multi-scale fusion stage, so that the model can automatically focus on the appropriate scale, and learn to put different weights on all scale and each pixel location; finally, the weighted sum of feature map is sampled to the original image size, and each pixel of aerial image is semantically labeled. The experiment demonstrates that compared with the traditional FCN and DeepLab method, and other aerial image segmentation model, the multi-scale fusion model based on attention mechanism not only has higher segmentation accuracy, but also can analyze the importance of different scales and pixel location by visualizing the weight map corresponding to each scale feature.

Key words: semantic segmentation, multi-scale fusion, attention mechanism, convolutional neural network