DFCFN: Dual-stage Feature Correction Fusion Network for Hyperspectral Pansharpening
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Hyperspectral (HS) pansharpening aims to fuse high-spatial-resolution panchromatic (PAN) images with low-spatial-resolution hyperspectral (LRHS) images to generate high-spatial-resolution hyperspectral (HRHS) images. Due to the lack of consideration for the modal feature difference between PAN and LRHS images, most deep leaning-based methods suffer from spectral and spatial distortions in the fusion results. In addition, most methods use upsampled LRHS images as network input, resulting in spectral distortion. To address these issues, we propose a dual-stage feature correction fusion network (DFCFN) that achieves accurate fusion of PAN and LRHS images by constructing two fusion sub-networks: a feature correction compensation fusion network (FCCFN) and a multi-scale spectral correction fusion network (MSCFN). Based on the lattice filter structure, FCCFN is designed to obtain the initial fusion result by mutually correcting and supplementing the modal features from PAN and LRHS images. To suppress spectral distortion and obtain fine HRHS results, MSCFN based on 2D discrete wavelet transform (2D-DWT) is constructed to gradually correct the spectral features of the initial fusion result by designing a conditional entropy transformer (CE-Transformer). Extensive experiments on three widely used simulated datasets and one real dataset demonstrate that the proposed DFCFN achieves significant improvements in both spatial and spectral quality metrics over other state-of-the-art (SOTA) methods. Specifically, the proposed method improves the SAM metric by 6.4%, 6.2%, and 5.3% compared to the second-best comparison approach on Pavia center, Botswana, and Chikusei datasets, respectively. The codes are made available at: https://github.com/EchoPhD/DFCFN.Keywords:
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Multiresolution analysis
Hyperspectral imaging, due to providing high spectral resolution images, is one of the most important tools in the remote sensing field. Because of technological restrictions hyperspectral sensors has a limited spatial resolution. On the other hand panchromatic image has a better spatial resolution. Combining this information together can provide a better understanding of the target scene. Spectral unmixing of mixed pixels in hyperspectral images results in spectral signature and abundance fractions of endmembers but gives no information about their location in a mixed pixel. In this paper we have used spectral unmixing results of hyperspectral images and segmentation results of panchromatic image for data fusion. The proposed method has been applied on simulated data using AVRIS Indian Pines datasets. Results show that this method can effectively combine information in hyperspectral and panchromatic images.
Panchromatic film
Full spectral imaging
Spectral signature
Spectral resolution
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In order to find out the fusion algorithm which is best suited for the panchromatic and multispectral images, fusion algorithms, such as PCA and wavelet algorithms have been employed and analyzed. In this paper, performance evaluation criteria are also used for quantitative assessment of the fusion performance. The spectral quality of fused images is evaluated by the ERGAS and Q4. The analysis indicates that the DWT fusion scheme has the best definition as well as spectral fidelity, and has better performance with regard to the high textural information absorption. Therefore, as the study area is concerned, it is most suited for the panchromatic and multispectral image fusion. an image fusion algorithm based on wavelet transform is proposed for Multispectral and panchromatic satellite image by using fusion in spatial and transform domains. In the proposed scheme, the images to be processed are decomposed into sub-images with the same resolution at same levels and different resolution at different levels and then the information fusion is performed using high-frequency sub-images under the Multi-resolution image fusion scheme based on wavelets produces better fused image than that by the MS or WA schemes.
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Abstract. Hyperspectral imaging, due to providing high spectral resolution images, is one of the most important tools in the remote sensing field. Because of technological restrictions hyperspectral sensors has a limited spatial resolution. On the other hand panchromatic image has a better spatial resolution. Combining this information together can provide a better understanding of the target scene. Spectral unmixing of mixed pixels in hyperspectral images results in spectral signature and abundance fractions of endmembers but gives no information about their location in a mixed pixel. In this paper we have used spectral unmixing results of hyperspectral images and segmentation results of panchromatic image for data fusion. The proposed method has been applied on simulated data using AVRIS Indian Pines datasets. Results show that this method can effectively combine information in hyperspectral and panchromatic images.
Panchromatic film
Full spectral imaging
Spectral signature
Spectral resolution
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This paper introduces a fusion method to merge the IKONOS low resolution multispectral image (MS) with the IKONOS high resolution panchromatic (PAN) image based on the Multiwavelet transform. Different fusion rules are used with the Multiwavelet method to improve the fusion quality based on pixel level fusion or feature level fusion. The best three old techniques for image fusion (IHS, Brovey, and Wavelet) are also tested in this paper. The performances of the existing and the proposed methods are calculated using Correlation Coefficient and Root Mean Square Error. Here we used our method to merge the panchromatic image of IKONOS sensor (1m resolution) with its multispectral image (4m resolution). Multiwavelet based image fusion method provides richer information in both spatial and spectral domains than the Wavelet based method, and the results clearly demonstrate the advantages of this approach.
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Merge (version control)
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Hyperspectral imaging, due to providing high spectral resolution images, is one of the most important tools in the remote sensing field. Because of technological restrictions hyperspectral sensors has a limited spatial resolution. On the other hand panchromatic image has a better spatial resolution. Combining this information together can provide a better understanding of the target scene. Spectral unmixing of mixed pixels in hyperspectral images results in spectral signature and abundance fractions of endmembers but gives no information about their location in a mixed pixel. In this paper we have used spectral unmixing results of hyperspectral images and segmentation results of panchromatic image for data fusion. The proposed method has been applied on simulated data using AVRIS Indian Pines datasets. Results show that this method can effectively combine information in hyperspectral and panchromatic images.
Panchromatic film
Full spectral imaging
Spectral signature
Spectral resolution
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The fusion of multispectral image and panchromatic image is an important technique in the domain of image fusion. The desired result is to obtain an image with the spatial resolution and quality of the panchromatic image and the spectral resolution and quality of the multispectral image in order to the next application. An introduction to an overview of image fusion methods are given, which include basic fusion methods and different models for injecting spatial detail information, as well as schemes that combine standard methods with wavelet transforms. Then, the advantages and disadvantages of the methods are given.
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Full spectral imaging
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With the growing of remote sensing data access methods,remote sensing image fusion technology has been concerned. The image of CBERS 2 has abundant information and low resolution,but the panchromatic image has high resolution and rich texture information. Taking the CBERS 2 multi-spectral image and panchromatic image of Zhengzhou City,Henan Province,as example,fusion experiments were carried out using four kinds of fusion methods including principal component analysis(PCA), wavelet transform(WPC), the brovey transformation(Brovey) and high-pass filtering(HPF) method. Comparison of the fusion image was made in the mean,the standard deviation,the deviation index,and the spectral fidelity,and the multi-spectral image and panchromatic image fusion method and effect of CBERS 2 was studied.
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This paper introduces the general process and characteristics of image fusion, investigates common algorithms of pixel level fusion by comparing five basic methods of the image fusion. The pixel level fusion experiments of the multispectral image and panchromatic image are performed by using the five methods. Finally, we analysis the experimental result, more detailed information of wavelet and IHS transform based image fusion is given, and the fusion of panchromatic and multispectral images has the best visual effect.
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Image fusion is a basic tool for combining low spatial resolution multi-spectral image and high spatial resolution panchromatic image using advanced image processing techniques. Many approaches have been developed to combine these images to obtain a fused one. Intensity-hue-saturation IHS transform is one of the widespread image fusion methods in the remote sensing community. However, this method introduces color distortion; therefore many papers have investigated modifications for the IHS method that can reduce this problem. In this paper, a filterbank-enhanced IHS method is developed for fusing satellite images. In this method a group of filters (a low pass filter, a high pass filter and several band pass filters) are used for filtering the panchromatic image and the intensity component of the original multispectral image. The resultant filtered images are combined to obtain a fused image. Experimental results indicate that filterbank-enhanced IHS method gives improved results relative to other fusion techniques.
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Filter bank
Composite image filter
Distortion (music)
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