Spatial–Spectral ConvNeXt for Hyperspectral Image Classification

Zhu, Yaobin; Yuan, Kexin; Zhong, W.; Xu, Linlin

doi:10.1109/jstars.2023.3282975

Cited by 11 publications

(2 citation statements)

References 43 publications

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“…3(a)] by adding Gaussian noise, and then it is fed into the pre-trained CD-CSCNet. The obtained feature maps 3), where abs(•) and mean(•, 3) compute the absolute value and the mean value in the third dimension, respectively. From Fig.…”

Section: Separable Implementation Of Cd-convmentioning

confidence: 99%

Deep Content-Dependent 3-D Convolutional Sparse Coding for Hyperspectral Image Denoising

Yin,

Chen

2024

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

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Despite the significant successes in hyperspectral image (HSI) denoising, pure data-driven HSI denoising networks still suffer from limited understanding of inference. Deep unfolding (DU) is a feasible way to improve the interpretability of deep network. However, the specialized spatial-spectral DU methods are seldom studied, and the simple spatial-spectral extension leads to unpleasant spectral distortion. To tackle these issues, we firstly propose a Content-Dependent 3D Convolutional Sparse Coding (CD-CSC) to jointly represent spatial-spectral feature. Specifically, the 3D filters used in CD-CSC for each HSI are unique, which are determined by linear combination of base 3D filters. Then, we develop a novel CD-CSC-inspired DU network for HSI denoising, called CD-CSCNet. Furthermore, by exploiting the lightweight of separable convolution and the adaptability of hypernetwork, we design a Separable Content-Dependent 3D Convolution (SCD-Conv) to carry out CD-CSCNet. SCD-Conv not only reduces computational complexity, but also can be viewed as the convolutional sparse coding with spatial and spectral dictionaries. Extensive experimental results on the ICVL, Zhuhai-1 OHS-3C, and GaoFen-5 datasets demonstrate that CD-CSCNet outperforms several recent pure data-driven and DU-based networks quantitatively and visually.

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Section: Separable Implementation Of Cd-convmentioning

confidence: 99%

Deep Content-Dependent 3-D Convolutional Sparse Coding for Hyperspectral Image Denoising

Yin,

Chen

2024

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

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“…After the feature extraction architecture, the global average pooling layer is first added to compress the feature map parameters to reduce its dimensions. 37 Next, the LN normalized feature distribution is accessed to facilitate the classification of tree species data, and finally, the fully connected layer with node 10 is accessed to discriminate the class of tree species.…”

Section: Convnext Tiny Modelmentioning

confidence: 99%

Simple linear iterative clustering and ConvNeXt for mapping vectorize tree species

Wang

Chen

2023

J. Appl. Rem. Sens.

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We propose a pioneering approach for gathering data on the forest canopy, one that merges two cutting-edge technologies: ConvNext tiny and simple linear iterative clustering (SLIC) (ConvNeXt and SLIC vectorize for tree species mapping, CSVTSM). By leveraging the clustering label generated by SLIC, CSVTSM obtains the vectorized result of land cover and allows us to obtain the location and distribution range information about the individual canopy. Moreover, CSVTSM employed ConvNeXt tiny, an advanced model, to identify vector objects and produce vector tree species maps across the experimental area to obtain the area of various tree species. To evaluate the accuracy of our methodology, we compared the canopy boundary of the original image to the four experimental areas that were vectorized by CSVTSM. Our model's efficacy was assessed by calculating the overall accuracy and Kappa coefficient on the validation set. The effectiveness of the proposed method was qualitatively evaluated by calculating the difference between the tree species area extracted by CSVTSM and manual extraction. The results indicate that, when considering the same canopy size, the vectorization approach based on the SLIC-based clustering label generates vector boundaries that are more closely aligned with the distribution of actual canopy boundaries in the experimental area. Furthermore, when used in conjunction with the SLIC vectorization approach, the ConvNeXt model can produce an even more precise vector map of tree species and more accurate tree species area information is obtained (only a 0.26 ha difference from manual extraction). These findings demonstrate that CSVTSM can be used to quantitatively acquire a wealth of information on individual tree positions, crown distribution ranges, and planting areas from high-resolution RGB photos captured by low-consumption unmanned aerial vehicle platforms. The implications of these results are wide-ranging, with local managers expressing keen interest in our findings as they offer trustworthy support for statistical application fields, such as gathering area ecological information, locating tree species resources, and disseminating spatial information about forests..

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A variable fidelity approach for predicting aerodynamic wall quantities of hypersonic vehicles using the ConvNeXt encoder-decoder framework

Yang,

Yao,

Xue

et al. 2024

Aerospace Science and Technology

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Spatial–Spectral ConvNeXt for Hyperspectral Image Classification

Cited by 11 publications

References 43 publications

Deep Content-Dependent 3-D Convolutional Sparse Coding for Hyperspectral Image Denoising

Deep Content-Dependent 3-D Convolutional Sparse Coding for Hyperspectral Image Denoising

Simple linear iterative clustering and ConvNeXt for mapping vectorize tree species

A variable fidelity approach for predicting aerodynamic wall quantities of hypersonic vehicles using the ConvNeXt encoder-decoder framework

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