Diffused Convolutional Neural Network for Hyperspectral Image Super-Resolution

Jia, Sen; Zhu, Shuangzhao; Wang, Wenwen; Xu, Meng; Wang, Weixi; Guo, Yujuan

doi:10.1109/tgrs.2023.3250640

Cited by 14 publications

(3 citation statements)

References 50 publications

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“…For example, Jia et al [37] proposed a lightweight convolutional neural network (LWCNN) for hyperspectral image classification that uses spatial-spatial schroedinger eigenmap (SSSE) feature extraction to obtain joint spatial spectrum information and decreases the number of parameters in the depth learning model by compressing dimensions. Furthermore, some techniques, such as LiteDepthwiseNet [38] and LiteHCNet [39], propose a two-branch network structure to decrease parameters and processing. Recently, Cao et al [40] developed a lightweight, efficient search space made up of equivalent lightweight convolution operators with multiscale receptive fields and suggested a lightweight multiscale NAS model with spatialspectral attention (LMSS-NAS) for HSI classification.…”

Section: B Parameter Performance Optimization Methods In Hsi Classifi...mentioning

confidence: 99%

LSSMA: Lightweight Spectral–Spatial Neural Architecture With Multiattention Feature Extraction for Hyperspectral Image Classification

Ding,

Ruan,

Yang

et al. 2024

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

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Deep learning has been utilized for hyperspectral image (HSI) classification in recent years, with notable performance improvements. In particular, convolutional neural networks (CNNs) methods have achieved major advancements in this area. However, there are some drawbacks to the existing CNN-based HSI classification approaches: 1) the lack of effective and simple feature representations in CNNs, which overlook the effects of spectral differences and spatial contextual information; 2) the classification model has an enormous network complexity as a result of its numerous training parameters and high computational requirements; and 3) the category samples in HSI data exhibit a significant long tail distribution issue, which affects the classification performance of HSI. To address these issues, we propose a lightweight spectral-spatial neural architecture with multi-attention feature extraction (LSSMA) for HSI classification. The main work consists of three areas:1) a spectral feature extraction and fusing module (SFEF) is created to facilitate the fusing of spectral-spatial features while reducing the number of trainable parameters and computational complexity of the model. This module uses convolutions of various kernel sizes for residual connection and feature fusion, and introduces group convolution to achieve efficient feature representation; 2)to utilize the spectralspatial correlation of HSI data to its fullest, a multi-scale convolutional activation guided attention mechanism (MsCA) is designed and the position attention module (PAM) is referenced, which can capture spectral differences and spatial contextual relationships between ground objects; and 3) focal loss (FL) is applied in computer vision tasks to the LSSMA model to enhance its capacity to handle category imbalance. Experimental results on four publicly available hyperspectral datasets show that the method obtains better classification performance at a lower computational cost.

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Section: B Parameter Performance Optimization Methods In Hsi Classifi...mentioning

confidence: 99%

LSSMA: Lightweight Spectral–Spatial Neural Architecture With Multiattention Feature Extraction for Hyperspectral Image Classification

Ding,

Ruan,

Yang

et al. 2024

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

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“…The primary limitation of these conventional techniques is the requirement for human parameter adjustment, leading to inadequate resilience and sluggish repair. Deep learning approaches have shown significant prowess in recent years in image production (Qian et al, 2022;Yu et al, 2018;Chopra et al, 2022), image retrieval (Nhi et al, 2022;Chu et al, 2022;Wang et al, 2020), image-semantic analysis , image classification (Ghoneim et al, 2018;Mandle et al, 2022) and reconstruction (Arnab et al, 2021), such as image denoising, image super-resolution, and rain and fog removal (Jia et al, 2023;Liu et al, 2022;Liang et al, 2022), and have also been applied to spectral image reconstruction. PnP introduces a denoising module based on the traditional method, but with limited improvement in reconstruction speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Reversible-Prior-Based Spectral-Spatial Transformer for Efficient Hyperspectral Image Reconstruction

Cai,

Liu,

et al. 2024

International Journal on Semantic Web and Information Systems

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The task of reconstructing a 3D cube from a 2D measurement is not well-defined in spectral imaging. Unfortunately, existing Deep Unfolding Network (DU) and End-to-End (E2E) approaches can't strike an optimal balance between computational complexity and reconstruction quality. The goal of this study is to think about ways to merge the E2E's violent mapping with DU's iterative method. Our proposed deep learning framework, the Reversible-prior-based Spectral-Spatial Transformer, combines the high-quality reconstruction capabilities of DU with the advantages of having fewer parameters and lower computing cost, similar to the E2E approach. SST-ReversibleNet uses a reversible prior to project the end-to-end mapping reconstruction results back into the measurement space, construct the residuals between the reprojection and the actual measurement, and improve reconstruction accuracy. Extensive trials show that our SST-ReversibleNet outperforms cutting-edge approaches by at least 0.8 dB and only use 34.3% Params and 44.1% giga floating-point operations per second (GFLOP).

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“…To save parameters, Li et al proposed a combined spectrum and feature context network [35]. In order to address the issue of excessive parameters in 3D convolutions, Jia et al proposed a method called "Diffused Convolutional Neural Network for Hyperspectral Image Super-Resolution" [36], which has achieved good results.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Super-Resolution Reconstruction Network Based on Hybrid Convolution and Spectral Symmetry Preservation

Dai

Zhang

et al. 2023

Remote Sensing

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Hyperspectral images (HSI) have high-dimensional and complex spectral characteristics, with dozens or even hundreds of bands covering the same area of pixels. The rich information of the ground objects makes hyperspectral images widely used in satellite remote sensing. Due to the limitations of remote sensing satellite sensors, hyperspectral images suffer from insufficient spatial resolution. Therefore, utilizing software algorithms to improve the spatial resolution of hyperspectral images has become an urgent problem that needs to be solved. The spatial information and spectral information of hyperspectral images are strongly correlated. If only the spatial resolution is improved, it often damages the spectral information. Inspired by the high correlation between spectral information in adjacent spectral bands of hyperspectral images, a hybrid convolution and spectral symmetry preservation network has been proposed for hyperspectral super-resolution reconstruction. This includes a model to integrate information from neighboring spectral bands to supplement target band feature information. The proposed model introduces flexible spatial-spectral symmetric 3D convolution in the network structure to extract low-resolution and neighboring band features. At the same time, a combination of deformable convolution and attention mechanisms is used to extract information from low-resolution bands. Finally, multiple bands are fused in the reconstruction module, and the high-resolution hyperspectral image containing global information is obtained by Fourier transform upsampling. Experiments were conducted on the indoor hyperspectral image dataset CAVE, the airborne hyperspectral dataset Pavia Center, and Chikusei. In the X2 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 46.335, 36.321, and 46.310, respectively. In the X4 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 41.218, 30.377, and 38.365, respectively. The results show that our method outperforms many advanced algorithms in objective indicators such as PSNR and SSIM while maintaining the spectral characteristics of hyperspectral images.

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Diffused Convolutional Neural Network for Hyperspectral Image Super-Resolution

Cited by 14 publications

References 50 publications

LSSMA: Lightweight Spectral–Spatial Neural Architecture With Multiattention Feature Extraction for Hyperspectral Image Classification

LSSMA: Lightweight Spectral–Spatial Neural Architecture With Multiattention Feature Extraction for Hyperspectral Image Classification

Reversible-Prior-Based Spectral-Spatial Transformer for Efficient Hyperspectral Image Reconstruction

Hyperspectral Super-Resolution Reconstruction Network Based on Hybrid Convolution and Spectral Symmetry Preservation

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