Abstract:
Pansharpening is a fundamental issue in remote sensing field. This paper proposes a side information partially guided convolutional sparse coding (SCSC) model for panshar...Show MoreMetadata
Abstract:
Pansharpening is a fundamental issue in remote sensing field. This paper proposes a side information partially guided convolutional sparse coding (SCSC) model for pansharpening. The key idea is to split the low resolution multispectral image into a panchromatic image related feature map and a panchromatic image irrelated feature map, where the former one is regularized by the side information from panchromatic images. With the principle of algorithm unrolling techniques, the proposed model is generalized as a deep neural network, called as SCSC pansharpening neural network (SCSC-PNN). Compared with 13 classic and state-of-the-art methods on three satellites, the numerical experiments show that SCSC-PNN is superior to others. The codes are available at https://github.com/xsxjtu/SCSC-PNN.
Date of Conference: 05-09 July 2021
Date Added to IEEE Xplore: 09 June 2021
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- IEEE Keywords
- Index Terms
- Deep Network ,
- Sparse Coding ,
- Convolutional Codes ,
- Convolutional Sparse Coding ,
- Neural Network ,
- Low Resolution ,
- Deep Neural Network ,
- Feature Maps ,
- Multispectral Images ,
- Panchromatic Image ,
- High-resolution ,
- Convolutional Layers ,
- High Spatial Resolution ,
- Feature Space ,
- Spectral Resolution ,
- Image Space ,
- Iteration Step ,
- Low Spatial Resolution ,
- Fewer Parameters ,
- Flow Estimation ,
- Common Information ,
- High Spectral Resolution ,
- Data Fidelity Term ,
- High-resolution Multispectral Image ,
- Convolution Unit
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Deep Network ,
- Sparse Coding ,
- Convolutional Codes ,
- Convolutional Sparse Coding ,
- Neural Network ,
- Low Resolution ,
- Deep Neural Network ,
- Feature Maps ,
- Multispectral Images ,
- Panchromatic Image ,
- High-resolution ,
- Convolutional Layers ,
- High Spatial Resolution ,
- Feature Space ,
- Spectral Resolution ,
- Image Space ,
- Iteration Step ,
- Low Spatial Resolution ,
- Fewer Parameters ,
- Flow Estimation ,
- Common Information ,
- High Spectral Resolution ,
- Data Fidelity Term ,
- High-resolution Multispectral Image ,
- Convolution Unit
- Author Keywords