A New Adaptive Component-Substitution-Based Satellite Image Fusion by Using Partial Replacement

Choi, Jaewon; Yu, Kiyun; Kim, Yong‐Il

doi:10.1109/tgrs.2010.2051674

Cited by 493 publications

(225 citation statements)

References 36 publications

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“…Although these techniques preserve spatial information accurately, and are quite robust to co-registration errors, they are often characterized by high spectral distortion, because PAN and MS components are acquired in spectral ranges that overlap only partially. In [9] two enhanced versions of GIHS and GS are proposed to deal with this problem, while in [10] an adaptive approach using partial replacement (PRACS) is presented.…”

Section: Introductionmentioning

confidence: 99%

Pansharpening by Convolutional Neural Networks

et al. 2016

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Abstract:A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices typical of remote sensing. Experiments on three representative datasets show the proposed method to provide very promising results, largely competitive with the current state of the art in terms of both full-reference and no-reference metrics, and also at a visual inspection.

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Section: Introductionmentioning

confidence: 99%

Pansharpening by Convolutional Neural Networks

et al. 2016

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“…The PRACS method based on component substitute was proposed by Choi et al (2011) [58] to improve the spatial and spectral quality of original MS imagery. To obtain the fine spatial resolution MS image, the method is organized into two steps.…”

Section: Partial Replacement Adaptive Component Substitution (Pracs)mentioning

confidence: 99%

“…Considering the unfixed relationship varying with different object, area, and circumstance, a linear regression algorithm is performed between PAN image and each MS bands. The second step is find an optimal CS fusion model to minimize the local and global spectral distortion between the PAN image and each MS band while preserving the spatial information within original PAN image [58].…”

Section: Partial Replacement Adaptive Component Substitution (Pracs)mentioning

confidence: 99%

Analysis of Coastline Extraction from Landsat-8 OLI Imagery

Liu

Wang

Ling

et al. 2017

Water

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Abstract:Coastline extraction is a fundamental work for coastal resource management, coastal environmental protection and coastal sustainable development. Due to the free access and long-term record, Landsat series images have the potential to be used for coastline extraction. However, dynamic features of different types of coastlines (e.g., rocky, sandy, artificial), caused by sea level fluctuation from tidal, storm and reclamation, make it difficult to be accurately extracted with coarse spatial resolution, e.g., 30 m, of Landsat images. To access this problem, we analyze the performance of coastline extraction by integrating downscaling, pansharpening and water index approaches in increasing the accuracy of coastline extraction from the latest Landsat-8 Operational Land Imager (OLI) imagery. In order to prove the availability of the proposed method, we designed three strategies: (1) Strategy 1 uses the traditional water index method to extract coastline directly from original 30 m Landsat-8 OLI multispectral (MS) image; (2) Strategy 2 extracts coastlines from 15 m fused MS images generated by integrating 15 m panchromatic (PAN) band and 30 m MS image with ten pansharpening algorithms; (3) Strategy 3 first downscales the PAN band to a finer spatial resolution (e.g., 7.5 m) band, and then extracts coastlines from pansharpened MS images generated by integrating downscaled spatial resolution PAN band and 30 m MS image with ten pansharpening algorithms. Using the coastline extracted from ZiYuan-3 (ZY-3) 5.8 m MS image as reference, accuracies of coastlines extracted from MS images in three strategies were validated visually and quantitatively. The results show that, compared with coastline extracted directly from 30 m Landsat-8 MS image (strategy 1), strategy 3 achieves the best accuracies with optimal mean net shoreline movement (MNSM) of −2.54 m and optimal mean absolute difference (MAD) of 11.26 m, followed by coastlines extracted in strategy 2 with optimal MNSM of −4.23 m and optimal MAD of 13.54 m. Further comparisons with single-band thresholding (Band 6), AWEI, and ISODATA also confirmed the superiority of strategy 3. For the various used pansharpening algorithms, five multiresolution analysis MRA-based pansharpening algorithms are more efficient than the component substitution CS-based pansharpening algorithms for coastline extraction from Landsat-8 OLI imagery. Among the five MRA-based fusion methods, the coastlines extracted from the fused images generated by Indusion, additive à trous wavelet transform (ATWT) and additive wavelet luminance proportional (AWLP) produced the most accurate and visually realistic representation. Therefore, pansharpening approaches can improve the accuracy of coastline extraction from Landsat-8 OLI imagery, and downscaling the PAN band to finer spatial resolution is able to further improve the coastline extraction accuracy, especially in crenulated coasts.

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“…Thereafter, some coefficients are evaluated in order to minimize the distinction between PAN and MS image bands new adaptive CS-based fusion technique is described as follows (Choi, 2011): …”

Section: Component Substitution (Cs) Based Pansharpening Algorithmsmentioning

confidence: 99%

A Comprehensive Review of Pansharpening Algorithms for Göktürk-2 Satellite Images

Kahraman

Ertürk

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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In this paper, a comprehensive review and performance evaluation of pansharpening algorithms for GÖKTÜRK-2 images is presented. GÖKTÜRK-2 is the first high resolution remote sensing satellite of Turkey which was designed and built in Turkey, by The Ministry of Defence, TUBITAK-UZAY and Turkish Aerospace Industry (TUSAŞ) collectively. GÖKTÜRK-2 was launched at 18th. December 2012 in Jinguan, China and provides 2.5 meter panchromatic (PAN) and 5 meter multispectral (MS) spatial resolution satellite images. In this study, a large number of pansharpening algorithms are implemented and evaluated for performance on multiple GÖKTÜRK-2 satellite images. Quality assessments are conducted both qualitatively through visual results and quantitatively using Root Mean Square Error (RMSE), Correlation Coefficient (CC), Spectral Angle Mapper (SAM), <i>Erreur Relative Globale Adimensionnelle de Synthése</i> (ERGAS), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Universal Image Quality Index (UIQI).

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A New Adaptive Component-Substitution-Based Satellite Image Fusion by Using Partial Replacement

Cited by 493 publications

References 36 publications

Pansharpening by Convolutional Neural Networks

Pansharpening by Convolutional Neural Networks

Analysis of Coastline Extraction from Landsat-8 OLI Imagery

A Comprehensive Review of Pansharpening Algorithms for Göktürk-2 Satellite Images

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