Pansharpening Using Guided Filtering to Improve the Spatial Clarity of VHR Satellite Imagery

Choi, Jaewon; Park, Honglyun; Seo, Doochun

doi:10.3390/rs11060633

Cited by 20 publications

(10 citation statements)

References 46 publications

(42 reference statements)

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“…The matching results show that the HOSM descriptor has preserved a larger number of correct matching points on the two multispectral image pairs and has fewer false matches. This is because the guidance images have preserved the significant contour information in multispectral images, the guided filtering operation is able to enhance the structures of the structure maps to improve the description ability [38]. In detail, due to the abundant structures in the visible and NIR images, the descriptor without guided filtering can also achieve satisfactory results on the EPFL dataset, as shown in Figure 9a.…”

Section: The Advantages Of Guided Filteringmentioning

confidence: 97%

A Local Feature Descriptor Based on Oriented Structure Maps with Guided Filtering for Multispectral Remote Sensing Image Matching

2019

Remote Sensing

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Multispectral image matching plays a very important role in remote sensing image processing and can be applied for registering the complementary information captured by different sensors. Due to the nonlinear intensity difference in multispectral images, many classic descriptors designed for images of the same spectrum are unable to work well. To cope with this problem, this paper proposes a new local feature descriptor termed histogram of oriented structure maps (HOSM) for multispectral image matching tasks. This proposed method consists of three steps. First, we propose a new method based on local contrast to construct the structure guidance images from the multispectral images by transferring the significant contours from source images to results, respectively. Second, we calculate oriented structure maps with guided image filtering. In details, we first construct edge maps by the progressive Sobel filters to extract the common structure characteristics from the multispectral images, and then we compute the oriented structure maps by performing the guided filtering on the edge maps with the structure guidance images constructed in the first step. Finally, we build the HOSM descriptor by calculating the histogram of oriented structure maps in a local region of each interest point and normalize the feature vector. The proposed HOSM descriptor was evaluated on three commonly used datasets and was compared with several state-of-the-art methods. The experimental results demonstrate that the HOSM descriptor can be robust to the nonlinear intensity difference in multispectral images and outperforms other methods.

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Section: The Advantages Of Guided Filteringmentioning

confidence: 97%

A Local Feature Descriptor Based on Oriented Structure Maps with Guided Filtering for Multispectral Remote Sensing Image Matching

2019

Remote Sensing

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“…The paper "Pansharpening Using Guided Filtering to Improve the Spatial Clarity of VHR Satellite Imagery" prepared by J. Choi, H. Park, and D. Seo addressed an important task of improving the spatial clarity of very high resolution (VHR) satellite imagery [3]. In the proposed approach, guided filtering was employed to generate optimal multispectral images for pansharpening.…”

Section: Overview Of the Issue: Multispectral Image Acquisition Procmentioning

confidence: 99%

Editorial to Special Issue “Multispectral Image Acquisition, Processing, and Analysis”

Vozel

Bazi

2019

Remote Sensing

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“…Through image fusion techniques, it is possible to synthesize images with a high spatial resolution with the appropriate spectral content. Pansharpening aims at improving spectral information in a fused image as well as adding spatial details to it [21][22][23]. The SR technique can be applied to the PAN image and combined with the fusion technique to have strong feature representation at microscopic levels.…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution Based Deep Learning Techniques for Panchromatic Satellite Images in Application to Pansharpening

Rohith¹,

Kumar²

2020

IEEE Access

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Pansharpening is a technique that fuses the coarser resolution of multispectral imagery (MS) with high spatial resolution panchromatic (PAN) imagery. Pansharpening is prone to spectral distortions based on the nature of the panchromatic band. If the spatial features are unclear in the panchromatic image, the pan-sharpened image will not be able to produce clear images. Super-Resolution (SR) is a technique that enhances minute details of the features in the image, thereby improving spatial information in the image. By fusing the Multispectral image with the super-resolved panchromatic image, there is a chance for producing high-quality multispectral imagery (pan-sharpened image). In this paper, ten state-of-the-art super-resolution based on deep learning techniques are tested and analyzed using ten different publicly available panchromatic datasets. On analysis, a feedback network for image super-resolution (SRFBN) technique outperforms the other algorithms in terms of sharp edges and pattern clarity, which are not visible in the input image. The proposed method is the fusion of SR applied PAN image with the MS image using a benchmarked Band Depended Spatial Detail (BDSD) pansharpening algorithm. The proposed method experiments with six datasets from different sensors. On analysis, the proposed technique outperforms the other counterpart pansharpening algorithms in terms of enhanced spatial information in addition to sharp edges and pattern clarity at reduced spectral distortion. Hence, the super-resolution based pansharpening algorithm is recommended for high spatial image applications.

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Pansharpening Using Guided Filtering to Improve the Spatial Clarity of VHR Satellite Imagery

Cited by 20 publications

References 46 publications

A Local Feature Descriptor Based on Oriented Structure Maps with Guided Filtering for Multispectral Remote Sensing Image Matching

A Local Feature Descriptor Based on Oriented Structure Maps with Guided Filtering for Multispectral Remote Sensing Image Matching

Editorial to Special Issue “Multispectral Image Acquisition, Processing, and Analysis”

Super-Resolution Based Deep Learning Techniques for Panchromatic Satellite Images in Application to Pansharpening

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