Research on Hyperspectral Identification of Altered Minerals in Yemaquan West Gold Field, Xinjiang

Pan, Ziwu; Liu, Junjie; Ma, Liqun; Chen, Fengrui; Zhu, Guangli; Qin, Fen; Zhang, Hongliang; Huang, Jianhui; Li, Yang; Wang, Juefan

doi:10.3390/su11020428

Cited by 7 publications

(2 citation statements)

References 39 publications

(47 reference statements)

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“…Unlike regular RGB or grayscale images, HSI provides more information in the band dimension, which allows subsequent tasks to distinguish materials and molecular components that are difficult to distinguish from normal RGB through their stored explicit or implicit distinctions. As a result, HSI has distinct advantages in a variety of tasks, including object detection [1,2], water quality monitoring [3][4][5], intelligent agriculture [6][7][8], geological prospecting [9,10], etc.…”

Section: Introductionmentioning

confidence: 99%

Unmixing-Guided Convolutional Transformer for Spectral Reconstruction

Duan

Song

et al. 2023

Remote Sensing

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Deep learning networks based on CNNs or transformers have made progress in spectral reconstruction (SR). However, many methods focus solely on feature extraction, overlooking the interpretability of network design. Additionally, models exclusively based on CNNs or transformers may lose other prior information, sacrificing reconstruction accuracy and robustness. In this paper, we propose a novel Unmixing-Guided Convolutional Transformer Network (UGCT) for interpretable SR. Specifically, transformer and ResBlock components are embedded in Paralleled-Residual Multi-Head Self-Attention (PMSA) to facilitate fine feature extraction guided by the excellent priors of local and non-local information from CNNs and transformers. Furthermore, the Spectral–Spatial Aggregation Module (S2AM) combines the advantages of geometric invariance and global receptive fields to enhance the reconstruction performance. Finally, we exploit a hyperspectral unmixing (HU) mechanism-driven framework at the end of the model, incorporating detailed features from the spectral library using LMM and employing precise endmember features to achieve a more refined interpretation of mixed pixels in HSI at sub-pixel scales. Experimental results demonstrate the superiority of our proposed UGCT, especially in the grss_d f c_2018 dataset, in which UGCT attains an RMSE of 0.0866, outperforming other comparative methods.

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Section: Introductionmentioning

confidence: 99%

Unmixing-Guided Convolutional Transformer for Spectral Reconstruction

Duan

Song

et al. 2023

Remote Sensing

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“…Due to the advancements in remote sensing technology, hyperspectral images (HSIs) are containing increasing spectral and spatial information (SSI), resulting in their extensive use in domains, such as forest inventory [1], urban area monitoring [2], road extraction [3], geological surveys [4], precision agriculture [5], environmental protection [6], military applications [7], hydrocarbon detection [8], oil reservoir exploration [9], and lake sediment analysis [10]. HSI classification is a crucial research topic related to these applications.…”

Section: Introductionmentioning

confidence: 99%

Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs

Chen

Chang

Shi

et al. 2021

Discrete Dynamics in Nature and Society

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Hyperspectral images (HSIs) contain large amounts of spectral and spatial information, and this provides the possibility for ground object classification. However, when using the traditional method, achieving a satisfactory classification result is difficult because of the insufficient labeling of samples in the training set. In addition, parameter adjustment during HSI classification is time-consuming. This paper proposes a novel fusion method based on the maximum noise fraction (MNF) and adaptive random multigraphs for HSI classification. Considering the overall spectrum of the object and the correlation of adjacent bands, the MNF was utilized to reduce the spectral dimension. Next, a multiscale local binary pattern (LBP) analysis was performed on the MNF dimension-reduced data to extract the spatial features of different scales. The obtained multiscale spatial features were then stacked with the MNF dimension-reduced spectral features to form multiscale spectral-spatial features (SSFs), which were sent into the RMG for HSI classification. Optimal performance was obtained by fusion. For all three real datasets, our method achieved competitive results with only 10 training samples. More importantly, the classification parameters corresponding to different hyperspectral data can be automatically optimized using our method.

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