Abstract:In this work, the bands of a Sentinel-2A image with spatial resolutions of 20 m and 60 m are sharpened to a spatial resolution of 10 m to obtain visible and near-infrared (VNIR) and shortwave infrared (SWIR) spectral bands with a spatial resolution of 10 m. In particular, we propose a two-step sharpening algorithm for Sentinel-2A imagery based on modified, selected, and synthesized band schemes using layer-stacked bands to sharpen Sentinel-2A images. The modified selected and synthesized band schemes proposed in this study extend the existing band schemes for sharpening Sentinel-2A images with spatial resolutions of 20 m and 60 m to improve the pan-sharpening accuracy by changing the combinations of bands used for multiple linear regression analysis through band-layer stacking. The proposed algorithms are applied to the pan-sharpening algorithm based on component substitution (CS) and a multiresolution analysis (MRA), and our results are then compared to the sharpening results when using sharpening algorithms based on existing band schemes. The experimental results show that the sharpening results from the proposed algorithm are improved in terms of the spatial and spectral properties when compared to existing methods. However, the results of the sharpening algorithm when applied to our modified band schemes show differing tendencies. With the modified, selected band scheme, the sharpening result when applying the CS-based algorithm is higher than the result when applying the MRA-based algorithm. However, the quality of the sharpening results when using the MRA-based algorithm with the modified synthesized band scheme is higher than that when using the CS-based algorithm.
Abstract:The objective of this work is to develop an algorithm for pansharpening of very high resolution (VHR) satellite imagery that reduces the spectral distortion of the pansharpened images and enhances their spatial clarity with minimal computational costs. In order to minimize the spectral distortion and computational costs, the global injection gain is transformed to the local injection gains using the normalized difference vegetation index (NDVI), on the assumption that the NDVI are positively or negatively correlated with local injection gains obtained from each band of the satellite data. In addition, the local injection gains are then applied in the hybrid pansharpening algorithm to optimize the spatial clarity. In particular, in the proposed algorithm, a synthetic intensity image is determined using block-based linear regression. In experiments using imagery collected by various satellites, such as KOrea Multi-Purpose SATellite-3 (KOMPSAT-3), KOMPSAT-3A and WorldView-3, the pansharpened results obtained using the proposed Hybrid Pansharpening algorithm using NDVI and based on the spectral mode (HP-NDVI spectral ) provide a better representation of the values of the Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS), the spectral angle mapper (SAM) and the Q4/Q8 than those produced by existing pansharpening algorithms. In terms of spatial quality, the pansharpened images obtained using the proposed pansharpening algorithm based on the spatial mode (HP-NDVI spatial ) have higher average gradient (AG) values than those obtained using existing pansharpening methods. In addition, the computational complexity of our method is similar to that of a pansharpening algorithm that is based on a global injection model, although our methodology has characteristics that are similar to those of a local injection gain-based model that has a very high computational cost. Thus, the quantitative and qualitative assessments presented here indicate that the proposed algorithm can be utilized in various applications that employ spectral information or require high spatial clarity.
Pansharpening algorithms are designed to enhance the spatial resolution of multispectral images using panchromatic images with high spatial resolutions. Panchromatic and multispectral images acquired from very high resolution (VHR) satellite sensors used as input data in the pansharpening process are characterized by spatial dissimilarities due to differences in their spectral/spatial characteristics and time lags between panchromatic and multispectral sensors. In this manuscript, a new pansharpening framework is proposed to improve the spatial clarity of VHR satellite imagery. This algorithm aims to remove the spatial dissimilarity between panchromatic and multispectral images using guided filtering (GF) and to generate the optimal local injection gains for pansharpening. First, we generate optimal multispectral images with spatial characteristics similar to those of panchromatic images using GF. Then, multiresolution analysis (MRA)-based pansharpening is applied using normalized difference vegetation index (NDVI)-based optimal injection gains and spatial details obtained through GF. The algorithm is applied to Korea multipurpose satellite (KOMPSAT)-3/3A satellite sensor data, and the experimental results show that the pansharpened images obtained with the proposed algorithm exhibit a superior spatial quality and preserve spectral information better than those based on existing algorithms.
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