Dropout Concrete Autoencoder for Band Selection on HSI Scenes

• URL: http://arxiv.org/abs/2401.16522v1
• Date: Mon, 29 Jan 2024 19:53:17 GMT
• Title: Dropout Concrete Autoencoder for Band Selection on HSI Scenes
• Authors: Lei Xu, Mete Ahishali, and Moncef Gabbouj
• Abstract summary: This work proposes a novel end-to-end network for informative band selection on hyperspectral images. The proposed network is inspired by the advances in concrete autoencoder (CAE) and dropout feature ranking strategy. Experimental results on four HSI scenes show that the proposed dropout CAE achieves substantial and effective performance levels.
• Score: 17.318853428846847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning-based informative band selection methods on hyperspectral images (HSI) recently have gained intense attention to eliminate spectral correlation and redundancies. However, the existing deep learning-based methods either need additional post-processing strategies to select the descriptive bands or optimize the model indirectly, due to the parameterization inability of discrete variables for the selection procedure. To overcome these limitations, this work proposes a novel end-to-end network for informative band selection. The proposed network is inspired by the advances in concrete autoencoder (CAE) and dropout feature ranking strategy. Different from the traditional deep learning-based methods, the proposed network is trained directly given the required band subset eliminating the need for further post-processing. Experimental results on four HSI scenes show that the proposed dropout CAE achieves substantial and effective performance levels outperforming the competing methods.

Related papers

• Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting [37.721042095518044]
  Cross-Domain Few-Shot Learning has witnessed great stride with the development of meta-learning. We propose a Frequency-Aware Prompting method with mutual attention for Cross-Domain Few-Shot classification.
  arXiv  Detail & Related papers  (2024-06-24T08:14:09Z)
• Embedded Hyperspectral Band Selection with Adaptive Optimization for Image Semantic Segmentation [0.0]
  We introduce a pioneering approach for hyperspectral band selection that offers an embedded solution. Our proposed method, embedded Hyperspectral Band Selection (EHBS), excels in selecting the best bands without the need for prior processing. The adaptability of our approach to other tasks opens up promising avenues for broader applications.
  arXiv  Detail & Related papers  (2024-01-21T07:48:39Z)
• Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters [69.24377241408851]
  Overfitting to the source domain is a common issue in gradient-based training of deep neural networks. We propose to base the selection on gradient-signal-to-noise ratio (GSNR) of network's parameters.
  arXiv  Detail & Related papers  (2023-10-11T10:21:34Z)
• Online Network Source Optimization with Graph-Kernel MAB [62.6067511147939]
  We propose Grab-UCB, a graph- kernel multi-arms bandit algorithm to learn online the optimal source placement in large scale networks. We describe the network processes with an adaptive graph dictionary model, which typically leads to sparse spectral representations. We derive the performance guarantees that depend on network parameters, which further influence the learning curve of the sequential decision strategy.
  arXiv  Detail & Related papers  (2023-07-07T15:03:42Z)
• An Unsupervised Learning Approach for Spectrum Allocation in Terahertz Communication Systems [31.263991262752498]
  We propose a new spectrum allocation strategy, aided by unsupervised learning, for terahertz communication systems. We first formulate an optimization problem to determine the optimal sub-band bandwidth and transmit power. We then propose the unsupervised learning-based approach to obtaining the near-optimal solution to this problem.
  arXiv  Detail & Related papers  (2022-08-07T02:14:13Z)
• End-to-end learnable EEG channel selection with deep neural networks [72.21556656008156]
  We propose a framework to embed the EEG channel selection in the neural network itself. We deal with the discrete nature of this new optimization problem by employing continuous relaxations of the discrete channel selection parameters. This generic approach is evaluated on two different EEG tasks.
  arXiv  Detail & Related papers  (2021-02-11T13:44:07Z)
• Ensemble Wrapper Subsampling for Deep Modulation Classification [70.91089216571035]
  Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms. We propose a subsampling technique to facilitate the use of deep learning for automatic modulation classification in wireless communication systems.
  arXiv  Detail & Related papers  (2020-05-10T06:11:13Z)
• Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification [58.20132466198622]
  We propose Attentive CutMix, a naturally enhanced augmentation strategy based on CutMix. In each training iteration, we choose the most descriptive regions based on the intermediate attention maps from a feature extractor. Our proposed method is simple yet effective, easy to implement and can boost the baseline significantly.
  arXiv  Detail & Related papers  (2020-03-29T15:01:05Z)
• ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising [52.01041506447195]
  We propose an attention-based deep residual network to learn a mapping from noisy HSI to the clean one. Experimental results demonstrate that our proposed ADRN scheme outperforms the state-of-the-art methods both in quantitative and visual evaluations.
  arXiv  Detail & Related papers  (2020-03-04T08:36:27Z)

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.