A Gift From the Integration of Discriminative and Diffusion-Based Generative Learning: Boundary Refinement Remote...

Area of Science:

• Computer Vision
• Geospatial Analysis
• Machine Learning

Background:

• Remote sensing semantic segmentation requires accurate identification and localization of ground objects.
• Existing discriminative models struggle with high-frequency boundary details, while generative models lack low-frequency feature inference.
• Bridging this gap is crucial for precise geospatial analysis.

Purpose of the Study:

• To develop a novel framework that integrates discriminative and generative learning for improved remote sensing semantic segmentation.
• To enhance the localization accuracy of object boundaries in segmentation maps.
• To address the limitations of existing models in capturing both low-frequency semantic information and high-frequency boundary details.

Main Methods:

• Proposed the Integration of Discriminative and diffusion-based Generative learning for Boundary Refinement (IDGBR) framework.
• Utilized a discriminative backbone for initial coarse segmentation map generation.
• Employed a conditioning guidance network and iterative denoising diffusion process for boundary refinement.

Main Results:

• The IDGBR framework demonstrated consistent boundary refinement capabilities across diverse discriminative architectures.
• Experiments on five remote sensing datasets (binary and multi-class) validated the framework's effectiveness.
• Achieved significant improvements in the precision of boundary localization in semantic segmentation maps.

Conclusions:

• The proposed IDGBR framework effectively combines the strengths of discriminative and generative learning for superior remote sensing semantic segmentation.
• The integration approach successfully enhances the learning of high-frequency features crucial for accurate boundary delineation.
• This method offers a robust solution for precise geospatial object segmentation, applicable to various datasets and models.