Knowledge-guided Complex Diffusion Model for PolSAR Image Classification in Contourlet Domain

• URL: http://arxiv.org/abs/2507.05666v1
• Date: Tue, 08 Jul 2025 04:50:28 GMT
• Title: Knowledge-guided Complex Diffusion Model for PolSAR Image Classification in Contourlet Domain
• Authors: Junfei Shi, Yu Cheng, Haiyan Jin, Junhuai Li, Zhaolin Xiao, Maoguo Gong, Weisi Lin,
• Abstract summary: We propose a knowledge-guided complex diffusion model for PolSAR image classification in the Contourlet domain.<n> Specifically, the Contourlet transform is first applied to decompose the data into low- and high-frequency subbands.<n>A knowledge-guided complex diffusion network is then designed to model the statistical properties of the low-frequency components.
• Score: 58.46450049579116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have demonstrated exceptional performance across various domains due to their ability to model and generate complicated data distributions. However, when applied to PolSAR data, traditional real-valued diffusion models face challenges in capturing complex-valued phase information.Moreover, these models often struggle to preserve fine structural details. To address these limitations, we leverage the Contourlet transform, which provides rich multiscale and multidirectional representations well-suited for PolSAR imagery. We propose a structural knowledge-guided complex diffusion model for PolSAR image classification in the Contourlet domain. Specifically, the complex Contourlet transform is first applied to decompose the data into low- and high-frequency subbands, enabling the extraction of statistical and boundary features. A knowledge-guided complex diffusion network is then designed to model the statistical properties of the low-frequency components. During the process, structural information from high-frequency coefficients is utilized to guide the diffusion process, improving edge preservation. Furthermore, multiscale and multidirectional high-frequency features are jointly learned to further boost classification accuracy. Experimental results on three real-world PolSAR datasets demonstrate that our approach surpasses state-of-the-art methods, particularly in preserving edge details and maintaining region homogeneity in complex terrain.

Related papers

• Controlled Latent Diffusion Models for 3D Porous Media Reconstruction [11.29275004613083]
  Three-dimensional digital reconstruction of porous media presents a fundamental challenge in geoscience.<n>We introduce a computational framework that addresses this challenge through latent diffusion models operating within the EDM framework.
  arXiv  Detail & Related papers  (2025-03-31T13:36:55Z)
• SeisRDT: Latent Diffusion Model Based On Representation Learning For Seismic Data Interpolation And Reconstruction [11.530476559185878]
  Due to limitations such as geographic, physical, or economic factors, collected seismic data often have missing traces.<n>Traditional seismic data reconstruction methods face the challenge of selecting numerous empirical parameters and struggle to handle large-scale continuous missing traces.<n>We propose a latent diffusion transformer utilizing representation learning for seismic data reconstruction.
  arXiv  Detail & Related papers  (2025-03-17T10:16:35Z)
• PolSAM: Polarimetric Scattering Mechanism Informed Segment Anything Model [76.95536611263356]
  PolSAR data presents unique challenges due to its rich and complex characteristics.<n>Existing data representations, such as complex-valued data, polarimetric features, and amplitude images, are widely used.<n>Most feature extraction networks for PolSAR are small, limiting their ability to capture features effectively.<n>We propose the Polarimetric Scattering Mechanism-Informed SAM (PolSAM), an enhanced Segment Anything Model (SAM) that integrates domain-specific scattering characteristics and a novel prompt generation strategy.
  arXiv  Detail & Related papers  (2024-12-17T09:59:53Z)
• Digging into contrastive learning for robust depth estimation with diffusion models [55.62276027922499]
  We propose a novel robust depth estimation method called D4RD. It features a custom contrastive learning mode tailored for diffusion models to mitigate performance degradation in complex environments. In experiments, D4RD surpasses existing state-of-the-art solutions on synthetic corruption datasets and real-world weather conditions.
  arXiv  Detail & Related papers  (2024-04-15T14:29:47Z)
• Enhancing Hyperspectral Images via Diffusion Model and Group-Autoencoder Super-resolution Network [29.6360974619655]
  Group-Autoencoder (GAE) framework encodes high-dimensional hyperspectral data into low-dimensional latent space. DMGASR construct highly effective HSI SR model (DMGASR) Experimental results on both natural and remote sensing hyperspectral datasets demonstrate that the proposed method is superior to other state-of-the-art methods both visually and metrically.
  arXiv  Detail & Related papers  (2024-02-27T07:57:28Z)
• A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data [51.03144354630136]
  Recent advancements show that diffusion models can generate high-quality images.<n>We study this phenomenon in a hierarchical generative model of data.<n>We find that the backward diffusion process acting after a time $t$ is governed by a phase transition.
  arXiv  Detail & Related papers  (2024-02-26T19:52:33Z)
• Diffusion Models Without Attention [110.5623058129782]
  Diffusion State Space Model (DiffuSSM) is an architecture that supplants attention mechanisms with a more scalable state space model backbone. Our focus on FLOP-efficient architectures in diffusion training marks a significant step forward.
  arXiv  Detail & Related papers  (2023-11-30T05:15:35Z)
• Learning transformer-based heterogeneously salient graph representation for multimodal remote sensing image classification [42.15709954199397]
  A transformer-based heterogeneously salient graph representation (THSGR) approach is proposed in this paper. First, a multimodal heterogeneous graph encoder is presented to encode distinctively non-Euclidean structural features from heterogeneous data. A self-attention-free multi-convolutional modulator is designed for effective and efficient long-term dependency modeling.
  arXiv  Detail & Related papers  (2023-11-17T04:06:20Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• A Feature Fusion-Net Using Deep Spatial Context Encoder and Nonstationary Joint Statistical Model for High Resolution SAR Image Classification [10.152675581771113]
  A novel end-to-end supervised classification method is proposed for HR SAR images. To extract more effective spatial features, a new deep spatial context encoder network (DSCEN) is proposed. To enhance the diversity of statistics, the nonstationary joint statistical model (NS-JSM) is adopted to form the global statistical features.
  arXiv  Detail & Related papers  (2021-05-11T06:20:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.