UCSL: Toward Unsupervised Common Subspace Learning for Cross-Modal Image Classification

Hong, Danfeng; Wang, Haipeng; Líu, Hao; Chanussot, Jocelyn

doi:10.1109/tgrs.2023.3282951

Cited by 15 publications

(2 citation statements)

References 44 publications

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“…However, the classification and identification of the vitality of sweet corn seeds using hyperspectral imaging technology were rarely reported. Apart from the commonly employed machine learning algorithms, several renowned deep learning models have been devised to analyze hyperspectral data ( Yao et al., 2023a ). These models are recognized for their notable advantages in extracting profound insights from images ( Saha and Manickavasagan, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Detection of sweet corn seed viability based on hyperspectral imaging combined with firefly algorithm optimized deep learning

Wang,

Song

2024

Front. Plant Sci.

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The identification of sweet corn seed vitality is an essential criterion for selecting high-quality varieties. In this research, a combination of hyperspectral imaging technique and diverse deep learning algorithms has been utilized to identify different vitality grades of sweet corn seeds. First, the hyperspectral data of 496 seeds, including four viability-grade seeds, are extracted and preprocessed. Then, support vector machine (SVM) and extreme learning machine (ELM) are used to construct the classification models. Finally, the one-dimensional convolutional neural networks (1DCNN), one-dimensional long short-term memory (1DLSTM), the CNN combined with the LSTM (CNN-LSTM), and the proposed firefly algorithm (FA) optimized CNN-LSTM (FA-CNN-LSTM) are utilized to distinguish spectral images of sweet corn seeds viability grade. The findings from the experimental analysis indicate that the deep learning models exhibit a significant advantage over traditional machine learning approaches in the discrimination of seed vitality levels, boasting a classification accuracy exceeding 94.26% in test datasets and achieving an accuracy improvement of at least 3% compared to the best-performing machine learning model. Moreover, the performance of the FA-CNN-LSTM model proposed in this study demonstrated a slight superiority over the other three models. Besides, the FA-CNN-LSTM achieved a classification accuracy of 97.23%, representing a significant improvement of 2.97% compared to the lowest-performing CNN and a 1.49% enhancement over the CNN-LSTM. In summary, this study reveals the potential of integrating deep learning with hyperspectral imaging as a promising alternative for discriminating sweet corn seed vitality grade, showcasing its value in agricultural research and cultivar breeding.

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Section: Introductionmentioning

confidence: 99%

Detection of sweet corn seed viability based on hyperspectral imaging combined with firefly algorithm optimized deep learning

Wang,

Song

2024

Front. Plant Sci.

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“…[19] presents a framework with Generative Adversarial Networks (GANs). Among the three conventional computer vision tasks, from the view of remote sensing, the image-level classification is more fundamental [20]- [23]. However, few of the mentioned image-level classification schemes, dominated by deep learning though, are coupled with the well-known evidence theory to achieve a higher accuracy.…”

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confidence: 99%

Deep Evidential Remote Sensing Landslide Image Classification With a New Divergence, Multiscale Saliency and an Improved Three-Branched Fusion

Zhang,

Cui,

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hitherto, image-level classification on remote sensing landslide images has been paid attention to, but the accuracy of traditional deep learning-based methods still have room for improvement. The evidence theory is found efficient to boost the accuracy of neural networks, however, the present study argues three challenges that hinder the lead-in of this theory in deep landslide image classification. Aiming at the three problems, this study makes three improvements. For the interpretability and decision-invariance losses of three previous divergences, we propose a Belief Jensen-Renyi divergence with properties proven. To couple the evidence theory with deep remote sensing landslide image classification, a channel-wise multi-scale visual saliency fusion is developed. We additionally find that the channel-wise fusion is capable to reduce false recognition of networks as compared with original RGB images. To avoid decision failures in evidence-theoretic fusion process, we design an interpretability improved three-branched fusion. Experiments on Bijie Landslide dataset corroborate the synergistic benefits of the three improvements, where the proposal is compared with state-ofthe-art image classification backbone networks, remote sensing image scene classifiers, evidence fusion algorithms and versatile evidence-theoretic deep learning classifiers. We also evaluated the new method with two sort of image degradation, as well as an actual scenario in Luding County, China whose data is publicly available. The source code is at https://github.com/defzhangaa/ deeplandslideDS.

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Discriminant analysis-based attention network for hyperspectral target detection

Imani

2025

Optics & Laser Technology

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UCSL: Toward Unsupervised Common Subspace Learning for Cross-Modal Image Classification

Cited by 15 publications

References 44 publications

Detection of sweet corn seed viability based on hyperspectral imaging combined with firefly algorithm optimized deep learning

Detection of sweet corn seed viability based on hyperspectral imaging combined with firefly algorithm optimized deep learning

Deep Evidential Remote Sensing Landslide Image Classification With a New Divergence, Multiscale Saliency and an Improved Three-Branched Fusion

Discriminant analysis-based attention network for hyperspectral target detection

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