EAS $$^2$$ KAM: enhanced adaptive source-selection kernel with attention mechanism for hyperspectral image classification

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Earth Science Informatics Pub Date : 2024-08-27 DOI:10.1007/s12145-024-01466-5
Ahmed R. El-gabri, Hussein A. Aly, Mohamed A. Elshafey, Tarek S. Ghoniemy
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Abstract

Hyperspectral Images (HSIs) possess extensive applications in remote sensing, especially material discrimination and earth observation monitoring. However, constraints in spatial resolution increase sensitivity to spectral noise, limiting the ability to adjust Receptive Fields (RFs). Convolutional Neural Networks (CNNs) with fixed RFs are a common choice for HSI classification tasks. However, their potential in leveraging the appropriate RF remains under-exploited, thus affecting feature discriminative capabilities. This study introduces an Enhanced Adaptive Source-Selection Kernel with Attention Mechanism (EAS\(^2\)KAM) for HSI Classification. The model incorporates a Three Dimensional Enhanced Function Mixture (3D-EFM) with a distinct RF for local low-rank contextual exploitation. Furthermore, it incorporates diverse global RF branches enriched with spectral attention and an additional spectral-spatial mixing branch to adjust RFs, enhancing multiscale feature discrimination. The 3D-EFM is integrated with a 3D Residual Network (3D ResNet) that includes a Channel-Pixel Attention Module (CPAM) in each segment, improving spectral-spatial feature utilization. Comprehensive experiments on four benchmark datasets show marked advancements, including a maximum rise of 0.67% in Overall Accuracy (OA), 0.87% in Average Accuracy (AA), and 1.33% in the Kappa Coefficient (\(\kappa \)), outperforming the top two HSI classifiers from a list of eleven state-of-the-art deep learning models. A detailed ablation study evaluates model complexity and runtime, confirming the superior performance of the proposed model.

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EAS $^$2$ KAM:用于高光谱图像分类的具有关注机制的增强型自适应源选择内核
高光谱图像(HSIs)在遥感领域有着广泛的应用,特别是在材料识别和地球观测监测方面。然而,空间分辨率的限制增加了对光谱噪声的敏感性,从而限制了调整接收场(RF)的能力。具有固定射频的卷积神经网络(CNN)是人机交互分类任务的常见选择。然而,它们在利用适当射频方面的潜力仍未得到充分开发,从而影响了特征判别能力。本研究介绍了用于人机交互分类的增强型自适应源选择内核(EAS/(^2/)KAM)。该模型结合了三维增强函数混合物(3D-EFM),具有独特的射频(RF),可用于局部低等级上下文利用。此外,该模型还包含多种全局射频分支,这些分支富含频谱注意力和额外的频谱-空间混合分支,用于调整射频,从而增强多尺度特征识别能力。3D-EFM 与 3D 残差网络(3D ResNet)集成,其中每个分段都包含一个通道-像素注意模块(CPAM),从而提高了频谱-空间特征的利用率。在四个基准数据集上进行的综合实验显示,该技术取得了显著进步,包括总体准确率(OA)最大提高了0.67%,平均准确率(AA)提高了0.87%,卡帕系数(\(\kappa \))提高了1.33%,超过了11个最先进深度学习模型中排名前两位的HSI分类器。一项详细的消融研究评估了模型的复杂性和运行时间,证实了拟议模型的卓越性能。
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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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