Fast semantic segmentation for remote sensing images with an improved Short-Term Dense-Connection (STDC) network

Abstract

It is hard to accomplish fast semantic segmentation on large remote sensing images, since current neural networks with numerous parameters often rely on significant computational resources. Our team proposes an improved fast semantic segmentation model based on short-term dense-connection network (RepSTDC). We introduce a structure reparameterization and coordinate attention into STDC networks. By structure reparameterization, we transform the multi-branch structure into a comparable single-branch configuration during the inference process. By replacing the traditional channel attention with a coordinate attention mechanism, we enhance the attention mechanism with considering channel relationships and long-distance position information, and then it saves the memory usages. We conducted thorough experiments to assess the efficacy of network components of RepSTDC on the several benchmark datasets. Additionally, we compared our proposed approach with state-of-the-art methods. Our RepSTDC model can well balance the accuracy performances, computing speed, and memory usage in most cases. It achieves fast segmentation by significantly reducing parameters but without obviously compromising performances compared to other methods.

Keywords

• Convolutional neural network (CNN)
• structural reparameterization
• remote sensing image
• semantic segmentation