Consolidated Convolutional Neural Network for Hyperspectral Image Classification
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TLDR
The experimental results proved that the proposed model can provide the optimal trade-off between accuracy and computational time compared to other related methods using the Indian Pines, Pavia University, and Salinas Scene hyperspectral benchmark datasets.Abstract:
The performance of hyperspectral image (HSI) classification is highly dependent on spatial and spectral information, and is heavily affected by factors such as data redundancy and insufficient spatial resolution. To overcome these challenges, many convolutional neural networks (CNN) especially 2D-CNN-based methods have been proposed for HSI classification. However, these methods produced insufficient results compared to 3D-CNN-based methods. On the other hand, the high computational complexity of the 3D-CNN-based methods is still a major concern that needs to be addressed. Therefore, this study introduces a consolidated convolutional neural network (C-CNN) to overcome the aforementioned issues. The proposed C-CNN is comprised of a three-dimension CNN (3D-CNN) joined with a two-dimension CNN (2D-CNN). The 3D-CNN is used to represent spatial–spectral features from the spectral bands, and the 2D-CNN is used to learn abstract spatial features. Principal component analysis (PCA) was firstly applied to the original HSIs before they are fed to the network to reduce the spectral bands redundancy. Moreover, image augmentation techniques including rotation and flipping have been used to increase the number of training samples and reduce the impact of overfitting. The proposed C-CNN that was trained using the augmented images is named C-CNN-Aug. Additionally, both Dropout and L2 regularization techniques have been used to further reduce the model complexity and prevent overfitting. The experimental results proved that the proposed model can provide the optimal trade-off between accuracy and computational time compared to other related methods using the Indian Pines, Pavia University, and Salinas Scene hyperspectral benchmark datasets.read more
Citations
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Journal ArticleDOI
FusionNet: A Convolution-Transformer Fusion Network for Hyperspectral Image Classification
TL;DR: A fusion network of convolution and Transformer for HSI classification is proposed, known as FusionNet, in which convolutionand Transformer are fused in both serial and parallel mechanisms to achieve the full utilization of HSI features.
Journal ArticleDOI
Hyperspectral Image Denoising via Adversarial Learning
TL;DR: This paper proposes an end-to-end HSI denoising model via adversarial learning that captures the subtle noise distribution from both spatial and spectral dimensions, and designs a Residual Spatial-Spectral Module and embed it in an UNet-like structure as the generator to obtain clean images.
Journal ArticleDOI
Tri-CNN: A Three Branch Model for Hyperspectral Image Classification
Mohammed Q. Alkhatib,Mina Al-Saad,Nour Aburaed,Saeed Almansoori,Jaime Zabalza,Stephen Marshall,Hussain Al Ahmad +6 more
TL;DR: Tri-CNN as discussed by the authors is based on a multi-scale 3D-CNN and three-branch feature fusion for hyperspectral image classification, which shows remarkable performance in terms of the Overall Accuracy (OA), Average Accuracy (AA), and Kappa metrics when compared with existing methods.
Journal ArticleDOI
CAEVT: Convolutional Autoencoder Meets Lightweight Vision Transformer for Hyperspectral Image Classification
TL;DR: This study built a lightweight vision transformer for HSI classification that can extract local and global information simultaneously, thereby facilitating accurate classification and validated the performance of the proposed CAEVT network using four widely used hyperspectral datasets.
Journal ArticleDOI
Shallow-to-Deep Spatial-Spectral Feature Enhancement for Hyperspectral Image Classification
TL;DR: In this article , a Shallow-to-Deep Feature Enhancement (SDFE) model with three modules based on Convolutional Neural Networks (CNNs) and VisionTransformer (ViT) is proposed.
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