Multi-Scale Semi-Coupled Convolutional Sparse Coding for the Super-Resolution Reconstruction of Remote Sensing Image

Chen, Nan; Zhang, Biao

doi:10.3724/sp.j.1089.2022.18903

Cited by 1 publication

(1 citation statement)

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“…Their inability to pinpoint and mitigate interference compromises the reliability of mineral element identification results [17][18][19]. Finally, the increasing demands of processing vast swathes of remote sensing imagery data have accentuated challenges in achieving precise mineral element identification [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Mineral Element Identification in Remote Sensing Imagery: A Fusion Approach Using CH-Tucker Decomposition and RFDNet

Ding,

Hu,

et al. 2023

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In the realm of geological and mineral exploration, remote sensing technology has emerged as a pivotal high-tech instrument. However, the effective interpretation of remote sensing images, especially in the context of heterogeneous data processing, noise, and the identification of fine granularity, remains a challenge. In this study, a novel method for the identification of mineral elements within remote sensing imagery was introduced. Firstly, a heterogeneous feature tensor migration technique anchored on the Coupled Heterogeneous Tucker Decomposition (CH-Tucker decomposition) was presented. Through this technique, multi-source remote sensing data were effectively processed and fused. Notably, associated data features from varying resolutions and angles were seamlessly coupled. Subsequently, an optical remote sensing image processing model founded on the RFDNet network was established. This model demonstrated robustness against noise data, thereby enabling the identification of mineral elements with a higher degree of granularity. The proposed methodology exhibited the capacity to extract mineral element information comprehensively and with remarkable accuracy. Thus, this research offers both valuable theoretical insights and practical evidence for furtherance in geological research and mineral element exploration.

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Section: Introductionmentioning

confidence: 99%

Mineral Element Identification in Remote Sensing Imagery: A Fusion Approach Using CH-Tucker Decomposition and RFDNet

Ding,

Hu,

et al. 2023

View full text Add to dashboard Cite

show abstract

Multi-Scale Semi-Coupled Convolutional Sparse Coding for the Super-Resolution Reconstruction of Remote Sensing Image

Cited by 1 publication

References 16 publications

Mineral Element Identification in Remote Sensing Imagery: A Fusion Approach Using CH-Tucker Decomposition and RFDNet

Mineral Element Identification in Remote Sensing Imagery: A Fusion Approach Using CH-Tucker Decomposition and RFDNet

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