Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2502.19699v1
• Date: Thu, 27 Feb 2025 02:34:23 GMT
• Title: Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification
• Authors: Yimin Zhu, Linlin Xu,
• Abstract summary: This paper presents a Spatial-Spectral Diffusion Contrastive Representation Network (DiffCRN)<n>DiffCRN is based on denoising diffusion probabilistic model (DDPM) combined with contrastive learning (CL) for hyperspectral images classification.<n> Experiments conducted on widely used four HSI datasets demonstrate the improved performance of the proposed DiffCRN.
• Score: 8.600534616819333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although efficient extraction of discriminative spatial-spectral features is critical for hyperspectral images classification (HSIC), it is difficult to achieve these features due to factors such as the spatial-spectral heterogeneity and noise effect. This paper presents a Spatial-Spectral Diffusion Contrastive Representation Network (DiffCRN), based on denoising diffusion probabilistic model (DDPM) combined with contrastive learning (CL) for HSIC, with the following characteristics. First,to improve spatial-spectral feature representation, instead of adopting the UNets-like structure which is widely used for DDPM, we design a novel staged architecture with spatial self-attention denoising module (SSAD) and spectral group self-attention denoising module (SGSAD) in DiffCRN with improved efficiency for spectral-spatial feature learning. Second, to improve unsupervised feature learning efficiency, we design new DDPM model with logarithmic absolute error (LAE) loss and CL that improve the loss function effectiveness and increase the instance-level and inter-class discriminability. Third, to improve feature selection, we design a learnable approach based on pixel-level spectral angle mapping (SAM) for the selection of time steps in the proposed DDPM model in an adaptive and automatic manner. Last, to improve feature integration and classification, we design an Adaptive weighted addition modul (AWAM) and Cross time step Spectral-Spatial Fusion Module (CTSSFM) to fuse time-step-wise features and perform classification. Experiments conducted on widely used four HSI datasets demonstrate the improved performance of the proposed DiffCRN over the classical backbone models and state-of-the-art GAN, transformer models and other pretrained methods. The source code and pre-trained model will be made available publicly.

Related papers

• DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification [3.271106943956333]
  Hyperspectral image classification (HSIC) has gained significant attention because of its potential in analyzing high-dimensional data with rich spectral and spatial information.<n>We propose the Differential Spatial-Spectral Transformer (DiffFormer) to address the inherent challenges of HSIC, such as spectral redundancy and spatial discontinuity.<n>Experiments on benchmark hyperspectral datasets demonstrate the superiority of DiffFormer in terms of classification accuracy, computational efficiency, and generalizability.
  arXiv  Detail & Related papers  (2024-12-23T07:21:41Z)
• Hyperspectral Images Efficient Spatial and Spectral non-Linear Model with Bidirectional Feature Learning [7.06787067270941]
  We propose a novel framework that significantly reduces data volume while enhancing classification accuracy.<n>Our model employs a bidirectional reversed convolutional neural network (CNN) to efficiently extract spectral features, complemented by a specialized block for spatial feature analysis.
  arXiv  Detail & Related papers  (2024-11-29T23:32:26Z)
• Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image Classification [3.446873355279676]
  classification of hyperspectral images (HSI) is a challenging task due to the high spectral dimensionality and limited labeled data.<n>We propose a novel multi-stage active transfer learning (ATL) framework that integrates a Spatial-Spectral Transformer (SST) with an active learning process for efficient HSI classification.<n>Experiments on benchmark HSI datasets demonstrate that the SST-ATL framework significantly outperforms existing CNN and SST-based methods.
  arXiv  Detail & Related papers  (2024-11-27T07:53:39Z)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising [54.110544509099526]
  Hyperspectral image (HSI) denoising is critical for the effective analysis and interpretation of hyperspectral data. We propose a hybrid convolution and attention network (HCANet) to enhance HSI denoising. Experimental results on mainstream HSI datasets demonstrate the rationality and effectiveness of the proposed HCANet.
  arXiv  Detail & Related papers  (2024-03-15T07:18:43Z)
• DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification [6.521187080027966]
  We propose a new network called DiffSpectralNet, which combines diffusion and transformer techniques. First, we use an unsupervised learning framework based on the diffusion model to extract both high-level and low-level spectral-spatial features. The diffusion method is capable of extracting diverse and meaningful spectral-spatial features, leading to improvement in HSI classification.
  arXiv  Detail & Related papers  (2023-10-29T15:26:37Z)
• You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality Assessment [45.62136459502005]
  We propose a network to perform full reference (FR) and no reference (NR) IQA. We first employ an encoder to extract multi-level features from input images. A Hierarchical Attention (HA) module is proposed as a universal adapter for both FR and NR inputs. A Semantic Distortion Aware (SDA) module is proposed to examine feature correlations between shallow and deep layers of the encoder.
  arXiv  Detail & Related papers  (2023-10-14T11:03:04Z)
• ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution [76.7408734079706]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. We propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure.
  arXiv  Detail & Related papers  (2023-07-26T07:45:14Z)
• Exploring Multi-Timestep Multi-Stage Diffusion Features for Hyperspectral Image Classification [16.724299091453844]
  Diffusion-based HSI classification methods only utilize manually selected single-timestep single-stage features. We propose a novel diffusion-based feature learning framework that explores Multi-Timestep Multi-Stage Diffusion features for HSI classification for the first time, called MTMSD. Our method outperforms state-of-the-art methods for HSI classification, especially on the challenging Houston 2018 dataset.
  arXiv  Detail & Related papers  (2023-06-15T08:56:58Z)
• SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models [18.391049303136715]
  We propose a generative framework for HSI classification with diffusion models (SpectralDiff) SpectralDiff effectively mines the distribution information of high-dimensional and highly redundant data. Experiments on three public HSI datasets demonstrate that the proposed method can achieve better performance than state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-04-12T16:32:34Z)
• DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration [103.79030498369319]
  Self-supervised diffusion model for hyperspectral image restoration is proposed. textttDDS2M enjoys stronger ability to generalization compared to existing diffusion-based methods. Experiments on HSI denoising, noisy HSI completion and super-resolution on a variety of HSIs demonstrate textttDDS2M's superiority over the existing task-specific state-of-the-arts.
  arXiv  Detail & Related papers  (2023-03-12T14:57:04Z)
• HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging [138.04956118993934]
  We propose a high-resolution dual-domain learning network (HDNet) for HSI reconstruction. On the one hand, the proposed HR spatial-spectral attention module with its efficient feature fusion provides continuous and fine pixel-level features. On the other hand, frequency domain learning (FDL) is introduced for HSI reconstruction to narrow the frequency domain discrepancy.
  arXiv  Detail & Related papers  (2022-03-04T06:37:45Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)

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.