Combined Spatial-Spectral Schroedinger Eigenmaps with Multiple Kernel Learning for Hyperspectral Image Classification Using a Low Number of Training Samples

Hassanzadeh, Shirin; Danyali, Habibollah; Helfroush, Mohammad Sadegh

doi:10.1080/07038992.2021.1978840

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…However, many irrelevant and redundant spectral bands lead to the Hughes phenomenon. 47 Consequently, HSI dimensionality reduction has a pivotal role in reducing the processing time and computational complexity and improving classification performance. The schematic representation in Fig.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…HSI contains hundreds of adjacent spectral bands along the electromagnetic spectrum. However, many irrelevant and redundant spectral bands lead to the Hughes phenomenon 47 . Consequently, HSI dimensionality reduction has a pivotal role in reducing the processing time and computational complexity and improving classification performance.…”

Section: Proposed Methodologymentioning

confidence: 99%

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Spatial–spectral hyperspectral images classification based on Krill Herd band selection and edge-preserving transform domain recursive filter

Moharram

Sundaram

2022

J. Appl. Rem. Sens.

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.Hyperspectral images (HSIs) have recently been exploited in several aspects as HSIs contain many contiguous and narrow discriminative spectral bands. The problem of dimensionality is a significant dilemma for HSIs due to there being plenty of irrelevant and redundant spectral bands and highly correlated bands that lead to Hughes phenomenon. To this end, we present an approach to selecting the most informative and relevant spectral bands for HSI dimensionality reduction using the Krill Herd (KH) algorithm. Moreover, KH is a heuristic search method that seeks to reach the optimum global solution within the search space and effectively evade falling into the local optima. Then an edge-preserving filter was employed to extract the spatial features while reducing noise and obtaining a suitable smoothing that improves the classification performance. Finally, the support vector machine classifier was performed at the pixel level for HSI classification. Furthermore, the proposed work was compared with the harmony search, genetic algorithm, bat algorithm, particle swarm optimization, and firefly algorithm. The experimental results demonstrated outstanding performance with an overall accuracy equal to 96.54%, 98.93%, 99.78%, and 98.66% on four hyperspectral datasets: Indian Pines scene, Pavia University scene, Salinas scene, and Botswana scene, respectively.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Proposed Methodologymentioning

confidence: 99%

Spatial–spectral hyperspectral images classification based on Krill Herd band selection and edge-preserving transform domain recursive filter

Moharram

Sundaram

2022

J. Appl. Rem. Sens.

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“…By studying effective fine-grained image-recognition methods for fruit flies, we can help farmers to identify fruit fly hazards earlier and guide them to take scientific control measures in a timely manner and, therefore, improve their agricultural production. However, in general, fine-grained image recognition is a challenging task in the field of computer vision [4], as it requires classification and recognition of images with small differences. In the case of the fruit fly, there are small differences between the species of Drosophila, which increase the difficulty in distinguishing them by traditional fine-grained image-recognition methods.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fine-Grained Image Recognition with Multi-Channel Self-Attention Mechanisms: A Focus on Fruit Fly Species Classification

Lu,

Yi,

2024

Applied Sciences

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Fruit fly species classification is a fine-grained task as there is a small gap between species. In order to effectively identify and improve the recognition of fruit flies, a fine-grained image-recognition method based on a multi-channel self-attention mechanism was studied and a network framework for fine-grained image recognition based on deep learning was designed in this paper. In this framework, long-term and short-term memory networks are used to extract the underlying features in fruit fly fine-grained images. By inputting the underlying features in the multi-channel self-attention mechanism module, the global and local attention feature maps can be obtained.The weighted attention feature map can also be obtained by multiplying the weight of each channel and the attention feature map. The fine-grained image features of fruit flies were obtained by summing the weighted attention feature map. A softmax classifier was used to process the features and complete the recognition of the fruit fly fine-grained images. Two fine-grained image datasets of fruit flies were applied as experimental objects. Dataset 1 and Dataset 2 contain 11,778 images and 20,580 images from 46 different categories of fruit flies, respectively. The Kappa coefficient was used as the evaluation index to identify fruit fly images with different targets using the method proposed herein. The experimental results showed that, as the number of attention channels increased, the Kappa coefficient gradually increased, suggesting an improvement in the accuracy of fine-grained image recognition. The fine-grained image features extracted by introducing a multi-channel self-attention mechanism exhibited more distinct boundaries with a small amount of overlap, demonstrating strong feature extraction capabilities. When dealing with fine-grained images with either simple or complex backgrounds, the method proposed in this paper has good performance and generalization ability. Even if the target is small and varied in shape, it can still achieve highly accurate recognition.

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A novel graph-based multiple kernel learning framework for hyperspectral image classification

Hassanzadeh,

Danyali,

Karami

et al. 2024

International Journal of Remote Sensing

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Combined Spatial-Spectral Schroedinger Eigenmaps with Multiple Kernel Learning for Hyperspectral Image Classification Using a Low Number of Training Samples

Cited by 3 publications

References 31 publications

Spatial–spectral hyperspectral images classification based on Krill Herd band selection and edge-preserving transform domain recursive filter

Spatial–spectral hyperspectral images classification based on Krill Herd band selection and edge-preserving transform domain recursive filter

Enhancing Fine-Grained Image Recognition with Multi-Channel Self-Attention Mechanisms: A Focus on Fruit Fly Species Classification

A novel graph-based multiple kernel learning framework for hyperspectral image classification

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