Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets

• URL: http://arxiv.org/abs/2101.07589v1
• Date: Tue, 19 Jan 2021 12:19:53 GMT
• Title: Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets
• Authors: Ke Li, Dengxin Dai, Ender Konukoglu, Luc Van Gool
• Abstract summary: This work studies Hyperspectral image (HSI) super-resolution (SR) HSI SR is characterized by high-dimensional data and a limited amount of training examples. This exacerbates the undesirable behaviors of neural networks such as memorization and sensitivity to out-of-distribution samples.
• Score: 99.92564298432387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work studies Hyperspectral image (HSI) super-resolution (SR). HSI SR is characterized by high-dimensional data and a limited amount of training examples. This exacerbates the undesirable behaviors of neural networks such as memorization and sensitivity to out-of-distribution samples. This work addresses these issues with three contributions. First, we propose a simple, yet effective data augmentation routine, termed Spectral Mixup, to construct effective virtual training samples. Second, we observe that HSI SR and RGB image SR are correlated and develop a novel multi-tasking network to train them jointly so that the auxiliary task RGB image SR can provide additional supervision. Finally, we extend the network to a semi-supervised setting so that it can learn from datasets containing low-resolution HSIs only. With these contributions, our method is able to learn from heterogeneous datasets and lift the requirement for having a large amount of HD HSI training samples. Extensive experiments on four datasets show that our method outperforms existing methods significantly and underpin the relevance of our contributions. The code of this work will be released soon.

Related papers

• Test-time Training for Hyperspectral Image Super-resolution [95.38382633281398]
  Hyperspectral image (HSI) super-resolution (SR) is still lagging behind the research of RGB image SR. In this work, we propose a new test-time training method to tackle this problem. Specifically, a novel self-training framework is developed, where more accurate pseudo-labels and more accurate LR-HR relationships are generated.
  arXiv  Detail & Related papers  (2024-09-13T09:30:19Z)
• Rethinking Image Super-Resolution from Training Data Perspectives [54.28824316574355]
  We investigate the understudied effect of the training data used for image super-resolution (SR) With this, we propose an automated image evaluation pipeline. We find that datasets with (i) low compression artifacts, (ii) high within-image diversity as judged by the number of different objects, and (iii) a large number of images from ImageNet or PASS all positively affect SR performance.
  arXiv  Detail & Related papers  (2024-09-01T16:25:04Z)
• UnmixingSR: Material-aware Network with Unsupervised Unmixing as Auxiliary Task for Hyperspectral Image Super-resolution [5.167168688234238]
  This paper proposes a component-aware hyperspectral image (HIS) super-resolution network called UnmixingSR. We use the bond between LR abundances and HR abundances to boost the stability of our method in solving SR problems. Experimental results show that unmixing process as an auxiliary task incorporated into the SR problem is feasible and rational.
  arXiv  Detail & Related papers  (2024-07-09T03:41:02Z)
• CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image Fusion [9.3350274016294]
  This paper introduces a novel knowledge distillation (KD) framework for HR-MSI/LR-HSI fusion to achieve SR of LR-HSI. To fully exploit the spatial and spectral feature representations of LR-HSI and HR-MSI, we propose a novel Cross Self-Attention (CSA) fusion module. Our experimental results demonstrate that the student model achieves comparable or superior LR-HSI SR performance.
  arXiv  Detail & Related papers  (2024-06-28T05:25:57Z)
• ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data Fusion [54.668445421149364]
  Deep learning-based hyperspectral image (HSI) super-resolution aims to generate high spatial resolution HSI (HR-HSI) by fusing hyperspectral image (HSI) and multispectral image (MSI) with deep neural networks (DNNs) In this letter, we propose a novel adversarial automatic data augmentation framework ADASR that automatically optimize and augments HSI-MSI sample pairs to enrich data diversity for HSI-MSI fusion.
  arXiv  Detail & Related papers  (2023-10-11T07:30:37Z)
• Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution [79.97180849505294]
  We propose a novel coupled unmixing network with a cross-attention mechanism, CUCaNet, to enhance the spatial resolution of HSI. Experiments are conducted on three widely-used HS-MS datasets in comparison with state-of-the-art HSI-SR models.
  arXiv  Detail & Related papers  (2020-07-10T08:08:20Z)
• Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery [79.69449412334188]
  In this paper, we investigate how to adapt state-of-the-art residual learning based single gray/RGB image super-resolution approaches. We introduce a spatial-spectral prior network (SSPN) to fully exploit the spatial information and the correlation between the spectra of the hyperspectral data. Experimental results on some hyperspectral images demonstrate that the proposed SSPSR method enhances the details of the recovered high-resolution hyperspectral images.
  arXiv  Detail & Related papers  (2020-05-18T14:25:50Z)

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.