Multispectral and panchromatic image fusion based on improved bilateral filter

Shi, Aiye; Xu, Lizhong; Xu, Feng; Huang, Chengrong

doi:10.1117/1.3616010

Cited by 13 publications

(9 citation statements)

References 17 publications

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“…The literature shows a large collection of pan-sharpening methods developed, [1][2][3][4][5][6][7] and recent reviews can be found in Refs. [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images

2014

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Commercial multispectral satellite datasets, such as WorldView-2 and Geoeye-1 images, are often delivered with a high-spatial resolution panchromatic image (PAN) as well as a corresponding lower resolution multispectral image (MSI). Certain fine features are only visible on the PAN but are difficult to discern on the MSI. To fully utilize the high-spatial resolution of the PAN and the rich spectral information from the MSI, a pan-sharpening process can be carried out. However, difficulties arise in maintaining radiometric accuracy, particularly for applications other than visual assessment. We propose a fast pan-sharpening process based on nearest-neighbor diffusion with the aim to enhance the salient spatial features while preserving spectral fidelity. Our approach assumes that each pixel spectrum in the pan-sharpened image is a weighted linear mixture of the spectra of its immediate neighboring superpixels; it treats each spectrum as its smallest element of operation, which is different from the most existing algorithms that process each band separately. Our approach is shown to be capable of preserving salient spatial and spectral features. We expect this algorithm to facilitate fine feature extraction from satellite images.

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“…The literature shows a large collection of pan-sharpening methods developed, [1][2][3][4][5][6][7] and recent reviews can be found in Refs. [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images

2014

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“…11 Compared with traditional pixel-oriented method, object-oriented change detection (OOCD) method chooses geographic object as basic unit for change detection and provides a new solution to the mentioned difficulties. [17][18][19] Finally, the results of change detection are related with the scale; that is, a single scale is insufficient to capture all the characteristics of objects within different sizes, shapes, etc. 12,13 Scholars have proposed some effective OOCD methods; [14][15][16][17][18] e.g., Miller et al proposed a method to detect blobs changes between gray-scale images, that is, first using connectivity analysis to obtain objects and then finding the matching object of each object in another image to make a comparison.…”

Section: Introductionmentioning

confidence: 99%

Object-oriented change detection approach for high-resolution remote sensing images based on multiscale fusion

Wang

et al. 2013

J. Appl. Remote Sens

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Aiming at the difficulties in change detection caused by the complexity of highresolution remote sensing images that exist in varied ecological environments and artificial objects, in order to overcome the limitations in traditional pixel-oriented change detection methods and improve the detection precision, an innovative object-oriented change detection approach based on multiscale fusion is proposed. This approach introduced the classical color texture segmentation algorithm J-segmentation (JSEG) to change detection and achieved the multiscale feature extraction and comparison of objects based on the sequence of J-images produced in JSEG. By comprehensively using the geometry, spectrum, and texture features of objects, and proposing two different multiscale fusing strategies, respectively, based on Dempster/Shafer evidence theory and weighted data fusion, the algorithm further improves the divisibility between changed and unchanged areas, thereby establishing an integrated framework of object-oriented change detection based on multiscale fusion. Experiments were performed on high-resolution airborne and SPOT 5 remote sensing images. Compared with different object-oriented and pixel-oriented detection methods, results of the experiments verified the validity and reliability of the proposed approach. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Recently, the compressed sensing theory provides a new approach for computer vision [15–17], image acquisition [18, 19], and reconstruction [20–22]. This method can get the reconstruction results as effectively as the traditional imaging systems do, or even higher quality (in resolution, SNR, etc.…”

Section: Introductionmentioning

confidence: 99%

Self-Adaptive Image Reconstruction Inspired by Insect Compound Eye Mechanism

Zhang

Shi

Wang

et al. 2012

Computational and Mathematical Methods in Medicine

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Inspired by the mechanism of imaging and adaptation to luminosity in insect compound eyes (ICE), we propose an ICE-based adaptive reconstruction method (ARM-ICE), which can adjust the sampling vision field of image according to the environment light intensity. The target scene can be compressive, sampled independently with multichannel through ARM-ICE. Meanwhile, ARM-ICE can regulate the visual field of sampling to control imaging according to the environment light intensity. Based on the compressed sensing joint sparse model (JSM-1), we establish an information processing system of ARM-ICE. The simulation of a four-channel ARM-ICE system shows that the new method improves the peak signal-to-noise ratio (PSNR) and resolution of the reconstructed target scene under two different cases of light intensity. Furthermore, there is no distinct block effect in the result, and the edge of the reconstructed image is smoother than that obtained by the other two reconstruction methods in this work.

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Multispectral and panchromatic image fusion based on improved bilateral filter

Cited by 13 publications

References 17 publications

Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images

Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images

Object-oriented change detection approach for high-resolution remote sensing images based on multiscale fusion

Self-Adaptive Image Reconstruction Inspired by Insect Compound Eye Mechanism

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