  Diffusion models and multi-scale features are essential components in
semantic segmentation tasks that deal with remote-sensing images. They
contribute to improved segmentation boundaries and offer significant contextual
information. U-net-like architectures are frequently employed in diffusion
models for segmentation tasks. These architectural designs include dense skip
connections that may pose challenges for interpreting intermediate features.
Consequently, they might not efficiently convey semantic information throughout
various layers of the encoder-decoder architecture. To address these
challenges, we propose a new model for semantic segmentation known as the
diffusion model with parallel multi-scale branches. This model consists of
Parallel Multiscale Diffusion modules (P-MSDiff) and a Cross-Bridge Linear
Attention mechanism (CBLA). P-MSDiff enhances the understanding of semantic
information across multiple levels of granularity and detects repetitive
distribution data through the integration of recursive denoising branches. It
further facilitates the amalgamation of data by connecting relevant branches to
the primary framework to enable concurrent denoising. Furthermore, within the
interconnected transformer architecture, the LA module has been substituted
with the CBLA module. This module integrates a semidefinite matrix linked to
the query into the dot product computation of keys and values. This integration
enables the adaptation of queries within the LA framework. This adjustment
enhances the structure for multi-head attention computation, leading to
enhanced network performance and CBLA is a plug-and-play module. Our model
demonstrates superior performance based on the J1 metric on both the UAVid and
Vaihingen Building datasets, showing improvements of 1.60% and 1.40% over
strong baseline models, respectively.
