A Triple-Path Spectral–Spatial Network With Interleave-Attention for Hyperspectral Image Classification

Deng, Ziqing; Wang, Yan; Zhang, Bing; Li, Linwei; Wang, Jihong; Bian, Lifeng; Yang, Chen

doi:10.1109/jstars.2022.3192470

Cited by 8 publications

(3 citation statements)

References 64 publications

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“…1 http://icvl.cs.bgu.ac.il/hyperspectral/ 3) Compared Methods: We compare our CD-CSCNet with several popular HSI denoising methods selected from different categories, including one filtering-based method (BM4D 2 [14]), six optimization model-based methods (LRTV 3 [39], LRTDTV 4 [41], TDL 5 [28], LLRT 6 [43], NMoG 7 [25], and ITSReg 8 [27]), four pure data-driven DL-based methods (HD-CNN 9 [46], QRNN 10 [55], SQAD 11 [52], and GRN 12 [47]), and two DU-based methods (FastHyMix 13 [61] and T3SC 14 [62]). All compared methods are reproduced with default settings.…”

Section: A Experimental Settings 1) Training Datasetmentioning

confidence: 99%

Deep Content-Dependent 3-D Convolutional Sparse Coding for Hyperspectral Image Denoising

Yin,

Chen

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Despite the significant successes in hyperspectral image (HSI) denoising, pure data-driven HSI denoising networks still suffer from limited understanding of inference. Deep unfolding (DU) is a feasible way to improve the interpretability of deep network. However, the specialized spatial-spectral DU methods are seldom studied, and the simple spatial-spectral extension leads to unpleasant spectral distortion. To tackle these issues, we firstly propose a Content-Dependent 3D Convolutional Sparse Coding (CD-CSC) to jointly represent spatial-spectral feature. Specifically, the 3D filters used in CD-CSC for each HSI are unique, which are determined by linear combination of base 3D filters. Then, we develop a novel CD-CSC-inspired DU network for HSI denoising, called CD-CSCNet. Furthermore, by exploiting the lightweight of separable convolution and the adaptability of hypernetwork, we design a Separable Content-Dependent 3D Convolution (SCD-Conv) to carry out CD-CSCNet. SCD-Conv not only reduces computational complexity, but also can be viewed as the convolutional sparse coding with spatial and spectral dictionaries. Extensive experimental results on the ICVL, Zhuhai-1 OHS-3C, and GaoFen-5 datasets demonstrate that CD-CSCNet outperforms several recent pure data-driven and DU-based networks quantitatively and visually.

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Section: A Experimental Settings 1) Training Datasetmentioning

confidence: 99%

Deep Content-Dependent 3-D Convolutional Sparse Coding for Hyperspectral Image Denoising

Yin,

Chen

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Vision transformer (ViT)-based methodologies have been utilized for HSIC and have shown promising results in a range of applications in order to overcome these problems [30]. In [31], a center-to-surround interactive learning (CSIL) architecture was created for HSIC by tackling the problems of geometric restrictions on the input and the central pixels' fuzzy contribution.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral image classification using a large selective kernel network hybridized to-kenization transformer

Wan,

He,

Gao

et al. 2024

Preprint

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One of the very popular deep learning-based techniques for classifying hyperspectral image (HSI) is convolutional neural networks (CNNs). However, extensive spatial-spectral infor-mation with several continuous bands is one of HSIs' main characteristics; this invariably leads to issues with large computing costs and computational complexity for network opti-mization. Additionally, current CNN-based classification techniques are only partially able to extract deep semantic characteristics. In response to these problems, this paper presents a novel HSI classification framework, a large selective kernel network, associated with a to-kenization transformer, namely LSKTT, which mainly includes three parts: First, HSI's spectral dimensions are decreased via principal component analysis, and a model for char-acterizing abstract feature representations by cascading a 3-D convolution layer and a 2-D convolution layer is employed. Second, to better model the ranging context of various ob-jects, the large selective kernel network is utilized by modifying its vast spatial receptive field dynamically. Third, a feature tokenization transformer is further utilized to exploit deeper semantic characteristics by converting and learning abstract spatial-spectral features. Experiments with three genuine HSIs show that the proposed LSKTT architecture beats many cutting-edge techniques in both qualitative and quantitative performance.

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“…Moreover, the attention maps generated by SSTs offer insights into the model's decision-making process. This interpretability is valuable for understanding which parts of the image contribute most to the specific predictions [44].…”

Section: Introductionmentioning

confidence: 99%

Groundwater Contamination Issues in Chiniot Area, Punjab, Pakistan

Ahmad¹,

Sultana²,

Yoshida³

et al. 2020

IJESD

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This study was planned to assess the groundwater quality of the District Chiniot, Pakistan. The majority of the people in the area use the groundwater from private wells. Water quality is very poor in the area especially close to the Pre Cambrian exposed Hills. TDS (total dissolved solids), hardness, Chloride, Sulfate and Calcium were very high in groundwater. Trace element chemistry shows that As, Mn and S are above the recommended limit. As and Mn contamination is due to water-rock interaction. Whereas S is encountered in shallow wells, which may be due to improper solid waste management and septic tanks. Microbiological analysis of municipal supply water shows that 25% of water samples have faecal contamination. High TDS groundwater is the cause of economic loss due to damage of sanitary fittings.

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A Triple-Path Spectral–Spatial Network With Interleave-Attention for Hyperspectral Image Classification

Cited by 8 publications

References 64 publications

Deep Content-Dependent 3-D Convolutional Sparse Coding for Hyperspectral Image Denoising

Deep Content-Dependent 3-D Convolutional Sparse Coding for Hyperspectral Image Denoising

Hyperspectral image classification using a large selective kernel network hybridized to-kenization transformer

Groundwater Contamination Issues in Chiniot Area, Punjab, Pakistan

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