Hyperspectral Classification Based on Siamese Neural Network Using Spectral-Spatial Feature

Zhao, Shizhi; Li, Wei; Du, Qian; Ran, Qiong

doi:10.1109/igarss.2018.8519286

Cited by 16 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yue et al [13] introduced the spatial and spectral information in the hyperspectral image into the CNN model after fusion, which further improved the classification effect. Zhao et al [14] extracted the spectral and spatial relationship information of neighboring pixel pairs by using the twin neural network and utilized the Softmax loss function to achieve classification. Wu et al [15] used 1D-CNN and 2D-CNN to extract spectral and spatial features, respectively; however, when using 1D-CNN for spectral feature learning, a large number of convolution kernels are required in the face of the high number of bands in HSI, which results in excessive computation and likely overfitting.…”

Section: Hyperspectral Image Classificationmentioning

confidence: 99%

Hyperspectral Image Classification Based on Two-Branch Multiscale Spatial Spectral Feature Fusion with Self-Attention Mechanisms

Ma,

Wang,

Wang

2024

Remote Sensing

View full text Add to dashboard Cite

In recent years, the use of deep neural network in effective network feature extraction and the design of efficient and high-precision hyperspectral image classification algorithms has gradually become a research hotspot for scholars. However, due to the difficulty of obtaining hyperspectral images and the high cost of annotation, the training samples are very limited. In order to cope with the small sample problem, researchers often deepen the network model and use the attention mechanism to extract features; however, as the network model continues to deepen, the gradient disappears, the feature extraction ability is insufficient, and the computational cost is high. Therefore, how to make full use of the spectral and spatial information in limited samples has gradually become a difficult problem. In order to cope with such problems, this paper proposes two-branch multiscale spatial–spectral feature aggregation with a self-attention mechanism for a hyperspectral image classification model (FHDANet); the model constructs a dense two-branch pyramid structure, which can achieve the high efficiency extraction of joint spatial–spectral feature information and spectral feature information, reduce feature loss to a large extent, and strengthen the model’s ability to extract contextual information. A channel–space attention module, ECBAM, is proposed, which greatly improves the extraction ability of the model for salient features, and a spatial information extraction module based on the deep feature fusion strategy HLDFF is proposed, which fully strengthens feature reusability and mitigates the feature loss problem brought about by the deepening of the model. Compared with five hyperspectral image classification algorithms, SVM, SSRN, A2S2K-ResNet, HyBridSN, SSDGL, RSSGL and LANet, this method significantly improves the classification performance on four representative datasets. Experiments have demonstrated that FHDANet can better extract and utilise the spatial and spectral information in hyperspectral images with excellent classification performance under small sample conditions.

show abstract

Section: Hyperspectral Image Classificationmentioning

confidence: 99%

Hyperspectral Image Classification Based on Two-Branch Multiscale Spatial Spectral Feature Fusion with Self-Attention Mechanisms

Ma,

Wang,

Wang

2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Koch et al [40] applied the Siamese network to the one-shot image recognition task, which obtains promising results. With respect to HSI classification, Zhao et al [41] utilized the Siamese network to enlarge the training set and extract the effective spatial-spectral features, which is able to improve the classification performance. Liu et al [42] proposed a Siamese network supervised with a margin ranking loss function for HSI classification, which can obtain better classification results than those of the conventional methods.…”

Section: Siamese Networkmentioning

confidence: 99%

Fusion of Multidimensional CNN and Handcrafted Features for Small-Sample Hyperspectral Image Classification

Tang

Huang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Hyperspectral image (HSI) classification has attracted widespread concern in recent years. However, due to the complexity of the HSI gathering environment, it is difficult to obtain a great number of HSI labeled samples. Therefore, how to effectively extract the spatial–spectral feature with small-scale training samples is the crucial point of HSI classification. In this paper, a novel fusion framework for small-sample HSI classification is proposed to fully combine the advantages of multidimensional CNN and handcrafted features. Firstly, a 3D fuzzy histogram of oriented gradients (3D-FHOG) descriptor is proposed to fully extract the handcrafted spatial–spectral feature of HSI pixels, which is suggested to be more robust by overcoming the local spatial–spectral feature uncertainty. Secondly, a multidimensional Siamese network (MDSN), which is updated by minimizing both contrastive loss and classification loss, is designed to effectively exploit the CNN-based spatial–spectral features from multiple dimensions. Finally, the proposed MDSN combined with 3D-FHOG is utilized for small-sample HSI classification to verify the effectiveness of our proposed fusion framework. The experimental results on three public data sets indicate that the proposed MDSN combined with 3D-FHOG is significantly better than the representative handcrafted feature-based and CNN-based methods, which in turn demonstrates the superiority of the proposed fusion framework.

show abstract

“…Hunt et al [ 14 ] applied SNNs for the classification of electrograms. Zhao et al [ 15 ] have used SNNs for hyperspectral image classification.…”

Section: Introductionmentioning

confidence: 99%

Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks: Algorithm Development and Validation Study

Oniani¹,

Chandrasekar²,

Sivarajkumar³

et al. 2023

JMIR AI

View full text Add to dashboard Cite

Background Natural language processing (NLP) has become an emerging technology in health care that leverages a large amount of free-text data in electronic health records to improve patient care, support clinical decisions, and facilitate clinical and translational science research. Recently, deep learning has achieved state-of-the-art performance in many clinical NLP tasks. However, training deep learning models often requires large, annotated data sets, which are normally not publicly available and can be time-consuming to build in clinical domains. Working with smaller annotated data sets is typical in clinical NLP; therefore, ensuring that deep learning models perform well is crucial for real-world clinical NLP applications. A widely adopted approach is fine-tuning existing pretrained language models, but these attempts fall short when the training data set contains only a few annotated samples. Few-shot learning (FSL) has recently been investigated to tackle this problem. Siamese neural network (SNN) has been widely used as an FSL approach in computer vision but has not been studied well in NLP. Furthermore, the literature on its applications in clinical domains is scarce. Objective The aim of our study is to propose and evaluate SNN-based approaches for few-shot clinical NLP tasks. Methods We propose 2 SNN-based FSL approaches, including pretrained SNN and SNN with second-order embeddings. We evaluate the proposed approaches on the clinical sentence classification task. We experiment with 3 few-shot settings, including 4-shot, 8-shot, and 16-shot learning. The clinical NLP task is benchmarked using the following 4 pretrained language models: bidirectional encoder representations from transformers (BERT), BERT for biomedical text mining (BioBERT), BioBERT trained on clinical notes (BioClinicalBERT), and generative pretrained transformer 2 (GPT-2). We also present a performance comparison between SNN-based approaches and the prompt-based GPT-2 approach. Results In 4-shot sentence classification tasks, GPT-2 had the highest precision (0.63), but its recall (0.38) and F score (0.42) were lower than those of BioBERT-based pretrained SNN (0.45 and 0.46, respectively). In both 8-shot and 16-shot settings, SNN-based approaches outperformed GPT-2 in all 3 metrics of precision, recall, and F score. Conclusions The experimental results verified the effectiveness of the proposed SNN approaches for few-shot clinical NLP tasks.

show abstract

Hyperspectral Classification Based on Siamese Neural Network Using Spectral-Spatial Feature

Cited by 16 publications

References 10 publications

Hyperspectral Image Classification Based on Two-Branch Multiscale Spatial Spectral Feature Fusion with Self-Attention Mechanisms

Hyperspectral Image Classification Based on Two-Branch Multiscale Spatial Spectral Feature Fusion with Self-Attention Mechanisms

Fusion of Multidimensional CNN and Handcrafted Features for Small-Sample Hyperspectral Image Classification

Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks: Algorithm Development and Validation Study

Contact Info

Product

Resources

About