GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification

Sidike, Paheding; Reyes, Abel A.; Kasaragod, Anush; Oommen, Thomas

doi:10.1109/cvprw56347.2022.00056

Cited by 10 publications

(7 citation statements)

References 19 publications

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“…In scRNA-seq data, the large number of dropouts greatly affects the downstream analysis, and to address this problem, this paper proposes a variable combinatorial gap-filling method based on U-Net. the U-net method is based on the U-Net network architecture [34][35][36] and improves U-Net by introducing the idea of image repair and applying it to single-cell sequencing data gap-filling. Inspired by the scIGANs single-cell gap-filling algorithm, unlike the method of data repair by fitting a probabilistic model, U-net partitions scRNA-seq sequencing data into multiple images on a cell-bycell basis, and generates a certain number of random mask regions (masks) for each image, and convolves each image with mask by the modified U-Net network.…”

Section: Methods Design Ideasmentioning

confidence: 99%

Variable combinatorial gap-filling method for single-cell RNA-seq data

YiXia,

Sun,

Yang

et al. 2023

Applied Mathematics and Nonlinear Sciences

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With the increasing development of single-cell RNA sequencing technology, a huge amount of sequencing data has emerged. The use of computational methods to fill in the gene expression information in scRNA-seq data is not only an important guide for gene regulatory network construction, embryonic development, and neurological research in the brain but also provides an important basis for drug development and clinical medicine. In this paper, we propose a variable combination of single-cell gap-filling algorithms with high gap-filling accuracy and fast computation speed through the comprehensive study and analysis of image repair technology and single-cell gap-filling algorithm. The experiments demonstrate that the U-net-based gap-filling method proposed in this paper has high accuracy in recovering gene expression values, can reduce the analysis errors caused by dropout events, and applies to large-scale data sets. In summary, the variable combinatorial gap-filling method for single-cell RNA-seq data proposed in this paper can effectively improve the results of downstream analysis and promote the development of research in the field of RNA sequencing data.

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Section: Methods Design Ideasmentioning

confidence: 99%

Variable combinatorial gap-filling method for single-cell RNA-seq data

YiXia,

Sun,

Yang

et al. 2023

Applied Mathematics and Nonlinear Sciences

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“…Liu et al [33] enhanced informative features that proved beneficial for classification and suppressing irrelevant information by constructing an interaction attention module. GAF-NAU [34] departs from the conventional practice of classifying patches, and instead represents one-dimensional spectral features as two-dimensional feature maps using the Gramian angular field (GAF). Subsequently, these GAF representations are embedded into a deep network to generate classification results.…”

Section: Introductionmentioning

confidence: 99%

A Spatial–Spectral Transformer for Hyperspectral Image Classification Based on Global Dependencies of Multi-Scale Features

Ma,

Lan,

Xie

et al. 2024

Remote Sensing

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Vision transformers (ViTs) are increasingly utilized for HSI classification due to their outstanding performance. However, ViTs encounter challenges in capturing global dependencies among objects of varying sizes, and fail to effectively exploit the spatial–spectral information inherent in HSI. In response to this limitation, we propose a novel solution: the multi-scale spatial–spectral transformer (MSST). Within the MSST framework, we introduce a spatial–spectral token generator (SSTG) and a token fusion self-attention (TFSA) module. Serving as the feature extractor for the MSST, the SSTG incorporates a dual-branch multi-dimensional convolutional structure, enabling the extraction of semantic characteristics that encompass spatial–spectral information from HSI and subsequently tokenizing them. TFSA is a multi-head attention module with the ability to encode attention to features across various scales. We integrated TFSA with cross-covariance attention (CCA) to construct the transformer encoder (TE) for the MSST. Utilizing this TE to perform attention modeling on tokens derived from the SSTG, the network effectively simulates global dependencies among multi-scale features in the data, concurrently making optimal use of spatial–spectral information in HSI. Finally, the output of the TE is fed into a linear mapping layer to obtain the classification results. Experiments conducted on three popular public datasets demonstrate that the MSST method achieved higher classification accuracy compared to state-of-the-art (SOTA) methods.

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“…Remote sensing images captured by the satellite or unmanned aerial vehicle are degraded by the existing haze or cloud [1][2][3][4], which destroys the surface information acquisition and further degrades the downstream tasks including image classification [5][6][7], object detection [8][9][10], change detection [11,12], object tracking [13,14], image segmentation [15,16], and so on. Remote image dehazing methods are to recover the clean image from its haze or cloud-polluted variants, which could be applied in applications with environment monitoring, military surveillance, and so on.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Dehazing via a Local Context-Enriched Transformer

Nie,

Xie,

Sun

2024

Remote Sensing

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Remote sensing image dehazing is a well-known remote sensing image processing task focused on restoring clean images from hazy images. The Transformer network, based on the self-attention mechanism, has demonstrated remarkable advantages in various image restoration tasks, due to its capacity to capture long-range dependencies within images. However, it is weak at modeling local context. Conversely, convolutional neural networks (CNNs) are adept at capturing local contextual information. Local contextual information could provide more details, while long-range dependencies capture global structure information. The combination of long-range dependencies and local context modeling is beneficial for remote sensing image dehazing. Therefore, in this paper, we propose a CNN-based adaptive local context enrichment module (ALCEM) to extract contextual information within local regions. Subsequently, we integrate our proposed ALCEM into the multi-head self-attention and feed-forward network of the Transformer, constructing a novel locally enhanced attention (LEA) and a local continuous-enhancement feed-forward network (LCFN). The LEA utilizes the ALCEM to inject local context information that is complementary to the long-range relationship modeled by multi-head self-attention, which is beneficial to removing haze and restoring details. The LCFN extracts multi-scale spatial information and selectively fuses them by the the ALCEM, which supplements more informative information compared with existing regular feed-forward networks with only position-specific information flow. Powered by the LEA and LCFN, a novel Transformer-based dehazing network termed LCEFormer is proposed to restore clear images from hazy remote sensing images, which combines the advantages of CNN and Transformer. Experiments conducted on three distinct datasets, namely DHID, ERICE, and RSID, demonstrate that our proposed LCEFormer achieves the state-of-the-art performance in hazy scenes. Specifically, our LCEFormer outperforms DCIL by 0.78 dB and 0.018 for PSNR and SSIM on the DHID dataset.

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GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification

Cited by 10 publications

References 19 publications

Variable combinatorial gap-filling method for single-cell RNA-seq data

Variable combinatorial gap-filling method for single-cell RNA-seq data

A Spatial–Spectral Transformer for Hyperspectral Image Classification Based on Global Dependencies of Multi-Scale Features

Remote Sensing Image Dehazing via a Local Context-Enriched Transformer

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