LFAH-Net: Laplacian frequency aware hierarchical network for hyperspectral image classification

IF 3 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Xiaoqing Wan , Hui Liu , Feng Chen , Kun Hu , Zhize Li
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引用次数: 0

Abstract

In recent years, the combination of convolutional neural networks (CNNs) with transformers for spectral-spatial feature extraction and robust semantic modeling has greatly improved the performance in hyperspectral image (HSI) classification tasks. However, these methods often overlook frequency information; CNNs struggle to capture global dependencies due to limited receptive fields, and transformers tend to lose fine-grained local structures and high-frequency variations. To address these challenges, this paper proposes a Laplacian frequency aware hierarchical network (LFAH-Net). We first design the method employing a diversity frequency-aware transformer (DFAT) module alongside a multi-level frequency fusion block (MFFB) stack to explicitly separate and integrate high-frequency signals such as edges and textures, as well as low-frequency signals like spectral contours, thereby achieving cross-level frequency feature complementarity. Besides, we propose a spectral-spatial adaptive recalibration fusion (SSARF) module, specifically designed to correct misalignments and suppress noise in hyperspectral features. Finally, the multi-scale dilation convolution (MSDC) module utilizes dilated convolutions to capture both local and global contextual information, while the adaptive feature fusion (AFF) module adaptively recalibrates and fuses these features with the spectral representations from DFAT. Experimental results on four popular hyperspectral datasets demonstrate that our framework significantly outperforms several state-of-the-art methods.
LFAH-Net:用于高光谱图像分类的拉普拉斯频率感知分层网络
近年来,卷积神经网络(cnn)与变压器相结合用于光谱空间特征提取和鲁棒语义建模,极大地提高了高光谱图像(HSI)分类任务的性能。然而,这些方法往往忽略了频率信息;由于有限的接受域,cnn很难捕捉到全局依赖关系,而变压器往往会失去细粒度的局部结构和高频变化。为了解决这些问题,本文提出了一种拉普拉斯频率感知分层网络(LFAH-Net)。我们首先设计了采用分集频率感知变压器(DFAT)模块和多级频率融合块(MFFB)堆栈的方法,以显式分离和集成边缘和纹理等高频信号以及频谱轮廓等低频信号,从而实现跨电平频率特征互补。此外,我们提出了一个光谱-空间自适应再校准融合(SSARF)模块,专门用于校正高光谱特征中的失调和抑制噪声。最后,多尺度扩展卷积(MSDC)模块利用扩展卷积捕获局部和全局上下文信息,而自适应特征融合(AFF)模块自适应地重新校准并融合这些特征与DFAT的频谱表示。在四种流行的高光谱数据集上的实验结果表明,我们的框架明显优于几种最先进的方法。
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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