An improved semantic segmentation algorithm for high-resolution remote sensing images based on DeepLabv3+

High-precision and high-efficiency Semantic segmentation of high-resolution remote sensing images is a challenge. Existing models typically require a significant amount of training data to achieve good classification results and have numerous training parameters. A novel model called MST-DeepLabv3+ was suggested in this paper for remote sensing image classification. It's based on the DeepLabv3+ and can produce better results with fewer train parameters. MST-DeepLabv3+ made three improvements: (1) Reducing the number of model parameters by substituting MobileNetV2 for the Xception in the DeepLabv3+'s backbone network. (2) Adding the attention mechanism module SENet to increase the precision of semantic segmentation. (3) Increasing Transfer Learning to enhance the model's capacity to recognize features, and raise the segmentation accuracy. MST-DeepLabv3+ was tested on international…