Multispectral Image Pansharpening Based on the Contourlet Transform

Amro, Israa; Mateos, Javier

doi:10.1007/978-1-4419-7380-1_20

Cited by 9 publications

(2 citation statements)

References 18 publications

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“…Multiresolution decomposition provides a simple hierarchical structure for merging images with varying spatial resolutions. As such, Laplacian pyramids, discrete wavelet transform, curvelet transform, contourlet transform, and non-subsampled contourlet transform [9]- [11] are the most commonly used multi-scale decomposition techniques in pansharpening. However, many studies have reviewed the performance of the aforementioned pansharpening categories [8], [12]- [14], which reflect the advancement of the pansharpening strategies.…”

Section: Introductionmentioning

confidence: 99%

A Pansharpening Based on the Non-Subsampled Contourlet Transform and Convolutional Autoencoder: Application to QuickBird Imagery

et al. 2022

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This paper presents a pansharpening technique based on the non-subsampled contourlet transform (NSCT) and convolutional autoencoder (CAE). NSCT is exceptionally proficient at presenting orientation information and capturing the internal geometry of objects. First, it's used to decompose the multispectral (MS) and panchromatic (PAN) images into high-frequency and low-frequency components using the same number of decomposition levels. Second, a CAE network is trained to generate original low-frequency PAN images from their spatially degraded versions. Low-resolution multispectral images are then fed into the trained convolutional autoencoder network to generate estimated high-resolution multispectral images. Third, another CAE network is trained to generate original high-frequency PAN images from their spatially degraded versions. The result of low-pass CAE is fed to the trained highpass CAE to generate estimated high-resolution multispectral images. The final pan-sharpened image is accomplished by injecting the detailed map of the spectral bands into the corresponding estimated high-resolution multispectral bands. The proposed method is tested on QuickBird datasets and compared with some existing pan-sharpening techniques. Objective and subjective results demonstrate the efficiency of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A Pansharpening Based on the Non-Subsampled Contourlet Transform and Convolutional Autoencoder: Application to QuickBird Imagery

et al. 2022

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“…The transform domain algorithm first transforms the original image, then fuses the transform coefficients of the transform domain to obtain the transform coefficients of the fusion image, and finally, reconstructs the fusion image by inverse transformation. Common pan-sharpening algorithms based on the transform domain mainly include pyramid transform [15], wavelet transform [16,17], and multiscale geometric transform [18,19]. Wavelet transform technology has been widely used in pan-sharpening processing, which has effectively solved the problem of generating many redundant data based on the pyramid transform method.…”

Section: Introductionmentioning

confidence: 99%

A New Architecture of Densely Connected Convolutional Networks for Pan-Sharpening

Huang

Feng

Wang

et al. 2020

IJGI

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In this paper, we propose a new architecture of densely connected convolutional networks for pan-sharpening (DCCNP). Since the traditional convolution neural network (CNN) has difficulty handling the lack of a training sample set in the field of remote sensing image fusion, it easily leads to overfitting and the vanishing gradient problem. Therefore, we employed an effective two-dense-block architecture to solve these problems. Meanwhile, to reduce the network architecture complexity, the batch normalization (BN) layer was removed in the design architecture of DenseNet. A new architecture of DenseNet for pan-sharpening, called DCCNP, is proposed, which uses a bottleneck layer and compression factors to narrow the network and reduce the network parameters, effectively suppressing overfitting. The experimental results show that the proposed method can yield a higher performance compared with other state-of-the-art pan-sharpening methods. The proposed method not only improves the spatial resolution of multi-spectral images, but also maintains the spectral information well.

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Log-Gabor directional region entropy adaptive guided filtering for multispectral pansharpening

Wang

Bai

et al. 2022

Appl Intell

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Multispectral Image Pansharpening Based on the Contourlet Transform

Cited by 9 publications

References 18 publications

A Pansharpening Based on the Non-Subsampled Contourlet Transform and Convolutional Autoencoder: Application to QuickBird Imagery

A Pansharpening Based on the Non-Subsampled Contourlet Transform and Convolutional Autoencoder: Application to QuickBird Imagery

A New Architecture of Densely Connected Convolutional Networks for Pan-Sharpening

Log-Gabor directional region entropy adaptive guided filtering for multispectral pansharpening

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