E2TNet: Efficient enhancement Transformer network for hyperspectral image classification

IF 3.1 3区 物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION
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引用次数: 0

Abstract

Recently, Convolutional Transformer-based models have become popular in hyperspectral image (HSI) classification tasks and gained competitive classification performance. However, some Convolutional Transformer-based models fail to effectively mine the global correlations of coarse-grained and fine-grained features, which is adverse to recognizing the refined scale variation information of land-cover. The combination of convolution operations and multihead self-attention mechanisms also increases the computational cost, leading to low classification efficiency. In addition, shallow spectral–spatial features are directly input into the encoder, which inevitably incurs redundant spectral information. Therefore, this paper proposes an efficient enhancement Transformer network (E2TNet) for HSI classification. Specifically, this paper first designs a spectral–spatial feature fusion module to extract spectral and spatial features from HSI cubes and fuse them. Second, considering that redundant spectral information has a negative impact on classification performance, this paper designs a spectral–spatial feature weighted module to improve the feature representation of critical spectral information. Finally, to explore the global correlations of coarse-grained and fine-grained features and improve classification efficiency, an efficient multigranularity information fusion module is embedded in the encoder of E2TNet. The experiment is conducted on four benchmark hyperspectral datasets, and the experimental results demonstrate that the proposed E2TNet is better than several Convolutional Transformer-based classification models in terms of classification accuracy and classification efficiency.
E2TNet:用于高光谱图像分类的高效增强变换器网络
最近,基于卷积变换器的模型在高光谱图像(HSI)分类任务中开始流行,并获得了具有竞争力的分类性能。然而,一些基于卷积变换器的模型无法有效挖掘粗粒度和细粒度特征的全局相关性,不利于识别土地覆盖的精细尺度变化信息。卷积运算与多头自关注机制的结合也增加了计算成本,导致分类效率低下。此外,浅层光谱空间特征直接输入编码器,不可避免地会产生冗余光谱信息。因此,本文提出了一种用于人机交互分类的高效增强变换器网络(E2TNet)。具体来说,本文首先设计了一个光谱-空间特征融合模块,从 HSI 立方体中提取光谱和空间特征并进行融合。其次,考虑到冗余光谱信息会对分类性能产生负面影响,本文设计了光谱-空间特征加权模块,以改进关键光谱信息的特征表示。最后,为了探索粗粒度和细粒度特征的全局相关性并提高分类效率,本文在 E2TNet 编码器中嵌入了高效的多粒度信息融合模块。实验在四个基准高光谱数据集上进行,实验结果表明,所提出的 E2TNet 在分类精度和分类效率方面都优于几个基于卷积变换器的分类模型。
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来源期刊
CiteScore
5.70
自引率
12.10%
发文量
400
审稿时长
67 days
期刊介绍: The Journal covers the entire field of infrared physics and technology: theory, experiment, application, devices and instrumentation. Infrared'' is defined as covering the near, mid and far infrared (terahertz) regions from 0.75um (750nm) to 1mm (300GHz.) Submissions in the 300GHz to 100GHz region may be accepted at the editors discretion if their content is relevant to shorter wavelengths. Submissions must be primarily concerned with and directly relevant to this spectral region. Its core topics can be summarized as the generation, propagation and detection, of infrared radiation; the associated optics, materials and devices; and its use in all fields of science, industry, engineering and medicine. Infrared techniques occur in many different fields, notably spectroscopy and interferometry; material characterization and processing; atmospheric physics, astronomy and space research. Scientific aspects include lasers, quantum optics, quantum electronics, image processing and semiconductor physics. Some important applications are medical diagnostics and treatment, industrial inspection and environmental monitoring.
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