KAO: Kernel-Adaptive Optimization in Diffusion for Satellite Image

2025-11-04

IEEE Transactions on Geoscience and Remote Sensing

AI Summary

Existing preconditioned models for very-high-resolution (VHR) satellite image inpainting suffer from poor generalization across diverse missing patterns, while postconditioned models incur prohibitive computational overhead. To address these limitations, this paper proposes the Kernel-Adaptive Optimization (KAO) framework, which synergistically integrates diffusion modeling with latent-space conditioning (LSC) and explicit propagation (EP) mechanisms to enable lightweight, stable, and transferable conditional guidance in the latent space. KAO requires no retraining to adapt to multiple mask types, drastically reducing inference complexity while improving both reconstruction accuracy and structural consistency. Evaluated on DeepGlobe and Massachusetts Roads datasets, KAO achieves state-of-the-art performance with fewer parameters and lower FLOPs than existing methods. It establishes a new benchmark for VHR remote sensing image inpainting, demonstrating superior reconstruction quality, cross-pattern generalization, and computational efficiency.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Satellite image inpainting is a crucial task in remote sensing, where accurately restoring missing or occluded regions is essential for robust image analysis. In this article, we propose kernel-adaptive optimization (KAO), a novel framework that utilizes KAO within diffusion models (DMs) for satellite image inpainting. KAO is specifically designed to address the challenges posed by very high-resolution (VHR) satellite datasets, such as DeepGlobe and Massachusetts Roads datasets. Unlike existing methods that rely on preconditioned models requiring extensive retraining or postconditioned models with significant computational overhead, KAO introduces a latent-space conditioning (LSC) approach, optimizing a compact latent space to achieve efficient and accurate inpainting. Furthermore, we incorporate explicit propagation (EP) into the diffusion process, facilitating forward–backward fusion, which improves the stability and precision of the method. The experimental results demonstrate that KAO sets a new benchmark for VHR satellite image restoration, providing a scalable, high-performance solution that balances the efficiency of preconditioned models with the flexibility of postconditioned models.

Problem

• Restoring missing regions in high-resolution satellite imagery
• Optimizing latent space for efficient image inpainting
• Improving diffusion model stability for precise restoration

Innovation

• Kernel-Adaptive Optimization in diffusion models
• Latent Space Conditioning for efficient inpainting
• Explicit Propagation enabling forward-backward fusion