Multi-view learning for hyperspectral image classification: An overview

Li, Xuefei; Liu, Baodi; Zhang, Kai; Chen, Honglong; Cao, Weijia; Liu, Weifeng; Tao, Dapeng

doi:10.1016/j.neucom.2022.05.093

Cited by 25 publications

(10 citation statements)

References 117 publications

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“…In contrast to traditional imaging systems that record data in only a few broad spectral channels (e.g., RGB), hyperspectral sensors can acquire data in hundreds or thousands of contiguous and narrow bands [ 1 ]. This vast amount of spectral information provides high capabilities for various applications, including agriculture, environmental monitoring, mineral exploration, urban planning, and military surveillance [ 2 ]. Hyperspectral imaging can capture the unique spectral signature of materials, surfaces, and objects.…”

Section: Introductionmentioning

confidence: 99%

Advancing Hyperspectral Image Analysis with CTNet: An Approach with the Fusion of Spatial and Spectral Features

Yadav,

Kumar,

Jalal

et al. 2024

Sensors

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Hyperspectral image classification remains challenging despite its potential due to the high dimensionality of the data and its limited spatial resolution. To address the limited data samples and less spatial resolution issues, this research paper presents a two-scale module-based CTNet (convolutional transformer network) for the enhancement of spatial and spectral features. In the first module, a virtual RGB image is created from the HSI dataset to improve the spatial features using a pre-trained ResNeXt model trained on natural images, whereas in the second module, PCA (principal component analysis) is applied to reduce the dimensions of the HSI data. After that, spectral features are improved using an EAVT (enhanced attention-based vision transformer). The EAVT contained a multiscale enhanced attention mechanism to capture the long-range correlation of the spectral features. Furthermore, a joint module with the fusion of spatial and spectral features is designed to generate an enhanced feature vector. Through comprehensive experiments, we demonstrate the performance and superiority of the proposed approach over state-of-the-art methods. We obtained AA (average accuracy) values of 97.87%, 97.46%, 98.25%, and 84.46% on the PU, PUC, SV, and Houston13 datasets, respectively.

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Section: Introductionmentioning

confidence: 99%

Advancing Hyperspectral Image Analysis with CTNet: An Approach with the Fusion of Spatial and Spectral Features

Yadav,

Kumar,

Jalal

et al. 2024

Sensors

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“…This phenomenon highlights a paradox in pattern recognition: With an increase in dimensionality, the classifier's performance can degrade unless there is a proportionate increase in the training samples. The reason for this degradation is that with more dimensions, the volume of the feature space increases exponentially, and the available training samples become sparse, making it difficult for the classifier to generalize from the training data to new, unseen data [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

Shoeibi,

Nevisi,

Salehi

et al. 2024

CMC

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Hyperspectral (HS) image classification plays a crucial role in numerous areas including remote sensing (RS), agriculture, and the monitoring of the environment. Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification. This process involves selecting the most informative spectral bands, which leads to a reduction in data volume. Focusing on these key bands also enhances the accuracy of classification algorithms, as redundant or irrelevant bands, which can introduce noise and lower model performance, are excluded. In this paper, we propose an approach for HS image classification using deep Q learning (DQL) and a novel multi-objective binary grey wolf optimizer (MOBGWO). We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image classification. In the suggested MOBGWO, a new sigmoid function is introduced as a transfer function to modify the wolves' position. The primary objective of this classification is to reduce the number of bands while maximizing classification accuracy. To evaluate the effectiveness of our approach, we conducted experiments on publicly available HS image datasets, including Pavia University, Washington Mall, and Indian Pines datasets. We compared the performance of our proposed method with several state-of-the-art deep learning (DL) and machine learning (ML) algorithms, including long short-term memory (LSTM), deep neural network (DNN), recurrent neural network (RNN), support vector machine (SVM), and random forest (RF). Our experimental results demonstrate that the Hybrid MOBGWO-DQL significantly improves classification accuracy compared to traditional optimization and DL techniques. MOBGWO-DQL shows greater accuracy in classifying most categories in both datasets used. For the Indian Pine dataset, the MOBGWO-DQL architecture achieved a kappa coefficient (KC) of 97.68% and an overall accuracy (OA) of 94.32%. This was accompanied by the lowest root mean square error (RMSE) of 0.94, indicating very precise predictions with minimal error. In the case of the Pavia University dataset, the MOBGWO-DQL model demonstrated outstanding performance with the highest KC of 98.72% and an impressive OA of 96.01%. It also recorded the lowest RMSE at 0.63, reinforcing its accuracy in predictions. The results clearly demonstrate that the proposed MOBGWO-DQL architecture not only reaches a highly accurate model more quickly but also maintains superior performance throughout the training process.

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“…The pixel-wise classification of HSI, which appears as an important issue of HSI processing technology, achieves a phenomenal interest of researchers and has been studied by many scholars in recent years [9,10]. The purpose of the pixelwise classification is to assign a unique category label to each pixel of the HSI dataset.…”

Section: Introductionmentioning

confidence: 99%

Pyramidal Multiscale Convolutional Network With Polarized Self-Attention for Pixel-Wise Hyperspectral Image Classification

Wang

Liu

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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In recent years, pixel-wise hyperspectral image (HSI) classification has received growing attention in the field of remote sensing. Plenty of spectral-spatial convolutional neural network (CNN) methods with diverse attention mechanisms have been proposed for HSI classification due to the attention mechanisms being able to provide more flexibility over standard convolutional blocks. However, it remains a challenge to effectively extract multi-scale features of high-resolution HSI in a real-world complex environment. In this paper, we propose a pyramidal multi-scale spectral-spatial convolutional network with polarized self-attention for pixel-wise HSI classification. It contains three stages: channel-wise feature extraction network, spatial-wise feature extraction network, and classification network, which are used to extract spectral features, extract spatial features, and generate classification results, respectively. Pyramidal convolutional blocks and polarized attention blocks are combined to extract spectral and spatial features of HSI. Furthermore, residual aggregation and one-shot aggregation are employed to better converge the network. Experimental results on several public HSI datasets demonstrate that the proposed network outperforms other related methods.

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Multi-view learning for hyperspectral image classification: An overview

Cited by 25 publications

References 117 publications

Advancing Hyperspectral Image Analysis with CTNet: An Approach with the Fusion of Spatial and Spectral Features

Advancing Hyperspectral Image Analysis with CTNet: An Approach with the Fusion of Spatial and Spectral Features

Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

Pyramidal Multiscale Convolutional Network With Polarized Self-Attention for Pixel-Wise Hyperspectral Image Classification

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