Kernel Tensor Sparse Coding Model for Precise Crop Classification of UAV Hyperspectral Image

Yang, Lixia; Zhang, Rui; Bao, Yajun; Yang, Shuyuan; Jiao, Licheng

doi:10.1109/lgrs.2023.3326452

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Scholars have employed a range of modules to enhance the capabilities of their models. For instance, the CNN-enhanced incorporates CNN and Graph Convolutional Network branches to conduct feature learning [36], the kernel tensor sparse coding model can enhance linear separation by utilizing the kernel tensor representation mechanism [37], and the SSAN combines 2D-CNN spatial extraction and RNN exploitation of spectral information [38].…”

Section: Introductionmentioning

confidence: 99%

PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery

Jiang,

Zhang,

Tang

et al. 2024

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

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Hyperspectral remote sensing images with high spatial resolution (H 2 imagery) have an abundant spatial-spectral information, holding tremendous potential for remote sensing fine-grained monitoring and classification. However, challenges such as high spatial heterogeneity, severe intra-class spectral variability and poor signal-to-noise ratio especially in unmanned aerial vehicle (UAV) hyperspectral imagery constrain and hinder the performance of fine-grained classification. Convolutional neural network (CNN) emerges as a formidable and excellent tool for image mining and feature extraction, offering effective utility for land cover classification. In this paper, a parallel convolutional classification network model based on multi-modal filters (including ICA-2D-FPN and SA-3D-CNN branching structures) PCCN-MSS is proposed for precise H 2 imagery classification. The ICA-2D-FPN branch integrates independent component analysis (ICA) into 2D-CNN to extract the multispatial scale and spectral information of H 2 imagery by Feature Pyramid Networks (FPN), meanwhile the SA-3D-CNN branch is designed to extract the spatial and spectral information by combining spectral attention (SA) mechanism and 3D-CNN. Taking hyperspectral imagery of unmanned aerial vehicles containing vegetation and artifactual material ground as an example, the proposed PCCN-MSS model achieves an overall accuracy (OA) of 78.18%, which outperforms by 9.58% than the compared methods. The proposed PCCN-MSS method can mitigate the classification issues of severe salt-and-pepper noise and inaccurate boundary, delivering more satisfactory classification results with robust classification performance and remarkable advantages for H 2 imagery.

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Section: Introductionmentioning

confidence: 99%

PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery

Jiang,

Zhang,

Tang

et al. 2024

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

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Open-Set Cross-Domain Hyperspectral Image Classification Based on Manifold Mapping Alignment

Zhang,

Liu

2024

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

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Cross-scene hyperspectral image (HSI) classification using transfer learning can effectively address the challenge of insufficient labeled samples in classification. The majority of existing transfer learning methods assume consistency in feature labels between the source and target HSI. In practical situations, the target domain hyperspectral data is likely to contain some categories that do not exist in the source domain hyperspectral data. In order to solve this problem, this paper proposes an unsupervised open-set cross-domain manifold mapping aligned method for target domain HSI classification. This method designs manifold embedding maps to align the spectral-spatial features of hyperspectral in different scenes. The spectral-spatial feature data is mapped to the subspace using both the source domain and target domain mapping matrices. Gradually, target domain samples are chosen for pseudo-labeling within the shared subspace, while samples from unknown classes are rejected. This method achieves subspace learning and pseudo-labeled sample updating through iteration, reducing the intra-class distance of all the classes and pushing rejected target data away from known classes. Experiments on the three cross-domain hyperspectral data pairs demonstrate that the proposed method outperforms related state-of-the-art methods.

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Research on hyperspectral remote sensing image classification method based on tensor decomposition

Liao,

Zhang,

Han

et al. 2024

2024 3rd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC)

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Kernel Tensor Sparse Coding Model for Precise Crop Classification of UAV Hyperspectral Image

Cited by 3 publications

References 16 publications

PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery

PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery

Open-Set Cross-Domain Hyperspectral Image Classification Based on Manifold Mapping Alignment

Research on hyperspectral remote sensing image classification method based on tensor decomposition

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