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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Pansharpening Based on Joint Gaussian Guided Upsampling
摘要： Pansharpening has been an important technique to increase the spatial resolution of the multispectral (MS) images provided by many earth observation satellites. Since the different spatial resolutions exist between the multispectral and panchromatic (PAN) images, pansharpening usually upsamples the MS images to the same size as the PAN image and then injects the spatial details into the upscaled MS ones. In this paper, we propose a novel pansharpening method focusing on the structure injection into the MS images through a joint Gaussian guided upsampling. The original spectral information is transferred to the joint upsampling outputs by using the hyperspherical color transformation (HCT). The experimental results show that our proposed method can obtain high-quality pansharpened results and outperforms some existing methods.
关键词： HCT,Pansharpening,joint upsampling
更新于2025-09-23 15:21:01
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Study on Full Scale Injection Coefficients for Pansharpening
摘要： Pansharpening regards the fusion of a high spatial resolution but low spectral resolution (panchromatic) image with a high spectral resolution but low spatial resolution (multispectral) image. The estimation, at reduced resolution, of injection coefficients through regression is a widespread and powerful approach. In this work, the problem of the estimation of the injection coefficients at full resolution for regression-based pansharpening approaches is studied. Multiple approaches (based on guess images or an iterative method) are proposed. These are assessed at reduced resolution by exploiting a real dataset acquired by the IKONOS sensor. The quantitative results clearly demonstrate the superiority of the proposed iterative method.
关键词： Pansharpening,Remote Sensing,Iterative Methods,Data Fusion,Full Scale Estimation
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Block-Based and Segmentation-Based Approaches for Component Substitution based Hyperspectral Pansharpening
摘要： Pansharpening is the fusion of panchromatic (PAN) image and multispectral (MS) or hyperspectral (HS) images and provides high spatial and high spectral resolution MS or HS images. Pansharpening mainly extracs the high frequency details from the PAN image, and then injects these details to the MS or HS image. This detail injection procedure can be performed in a variety of ways: using global, block-based or clustering-based techniques. In this paper, block-based and clustering-based approaches are utilized for standart component substitution pansharpening approaches, namely Intensity Hue Saturation (IHS), Brovey Transform (BT), Gram Schmidt (GS) orthagonalization procedure and Principal Component Analysis (PCA) techniques. Both non-overlapping and overlapping blocks are considered, along with various segmentation approaches such as k-means, Iterative Self Organizing Data Analysis Techniques Algorithm (ISODATA) and Simple Linear Iterative Clustering (SLIC). Two datasets with different characteristics are used in order to evaluate the approaches, and the block-based and segmentation-based approaches are shown to provide enhanced performance.
关键词： hyperspectral,superpixels,pansharpening,segmentation,Block-based
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A CNN-Based Fusion Method for Super-Resolution of Sentinel-2 Data
摘要： Sentinel-2 data represent a rich source of information for the community due to the free access and to the temporal-spatial coverage assured. However, some of the spectral bands are sensed at reduced resolution due to a compromise between technological limitations and Copernicus program's objectives. For this reason in this work we present a new super-resolution method based on Convolutional Neural Networks (CNNs) to rise the resolution of the shortwave infra-red (SWIR) band from 20 to 10 meters, that is the highest resolution provided. This is accomplished by fusing the target band with the finer-resolution ones. The proposed solution compares favourably against several alternative methods according to different quality indexes. In addition we have also tested the use of the super-resolved band from an applicative perspective by detecting water basins through the Modified Normalized Difference Water Index (MNDWI).
关键词： convolutional neural network,Deep learning,Sentinel-2,pansharpening,normalized difference water index
更新于2025-09-09 09:28:46
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Pansharpening for Cloud-Contaminated Very High-Resolution Remote Sensing Images
摘要： The optical remote sensing images not only have to make a fundamental tradeoff between the spatial and spectral resolutions, but also are inevitable to be polluted by the clouds; however, the existing pansharpening methods mainly focus on the resolution enhancement of the optical remote sensing images without cloud contamination. How to fuse the cloud-contaminated images to achieve the joint resolution enhancement and cloud removal is a promising and challenging work. In this paper, a pansharpening method for the challenging cloud-contaminated very high-resolution remote sensing images is proposed. Furthermore, the cloud-contaminated conditions for the practical observations with all the thick clouds, the thin clouds, the haze, and the cloud shadows are comprehensively considered. In the proposed methods, a two-step fusion framework based on multisource and multitemporal observations is presented: 1) the thin clouds, the haze, and the light cloud shadows are proposed to be first jointly removed and 2) a variational-based integrated fusion model is then proposed to achieve the joint resolution enhancement and missing information reconstruction for the thick clouds and dark cloud shadows. Through the proposed fusion method, a promising cloud-free fused image with both high spatial and high spectral resolutions can be obtained. To comprehensively test and verify the proposed method, the experiments were implemented based on both the cloud-free and cloud-contaminated images, and a number of different remote sensing satellites including the IKONOS, the QuickBird, the Jilin (JL)-1, and the Deimos-2 images were utilized. The experimental results confirm the effectiveness of the proposed method.
关键词： remote sensing,image fusion,Pansharpening,cloud contamination,integrated model
更新于2025-09-09 09:28:46
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A novel iterative PCA–based pansharpening method
摘要： Image pansharpening methods are usually grouped into two main classes: the spectral methods and the spatial methods. For the first class, the multispectral image undergoes a spectral transformation and then one of the resultant components is totally substituted with the panchromatic image, hence leading to a considerable color distortion compared with the second class. In the literature, this issue is addressed by integrating the wavelet transform to the spectral methods in order to transfer only the spatial details of the panchromatic image. Furthermore, the spatial information quantity transferred during the fusion is usually defined by the resolution ratio between the multi-spectral and panchromatic images, and this is, however, not necessarily the optimal quantity providing the best images. Therefore, a simple iterative Principal Component Analysis (PCA) based method is proposed in this letter, to continuously transfer the spatial information from the panchromatic to the multispectral image until the best fused image is obtained. The spatial distortion Ds of the Quality with No Reference (QNR) index is used as a stopping criterion. The experiments applied on the Worldview–3 images show that the suggested method presents the best visual and numerical results comparatively to the PCA and the Additive Wavelet Principal Component (AWPC) methods.
关键词： wavelet transform,QNR index,pansharpening,spatial information,PCA
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A New Hyperspectral Pansharpening Method With Intrisic Image Decomposition
摘要： The component substitution (CS) and multiresolution analysis (MRA) based methods have been well adopted in hyperspectral pansharpening. The major contribution of this paper is a novel MRA and CS hybrid framework based on the intrinsic image decomposition. First, the weighted least squares (WLS) filter is performed on the sharpened panchromatic (P) image to extract the high-frequency component. Then, the intrinsic image decomposition (IID) is adopted to decompose the interpolated hyperspectral (H) image into the illumination and reflectance components. Finally, the detail map is generated by making a proper compromise between the high-frequency component of the P image and the illumination component of the H image. The detail map further refined by the information ratio of different bands of the H image is injected into each band of the interpolated H image. Experimental results indicate that the proposed method achieves a better fusion result than several state-of-the-art hyperspectral pansharpening methods.
关键词： intrinsic image decomposition (IID),panchromatic (P) image,hyperspectral (H) image,pansharpening
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Effects of Pansharpening Methods on Discrimination of Tropical Crop and Forest Using Very High-Resolution Satellite Imagery
摘要： This paper assesses the effect of pansharpening process in classification of tropical crop and forest areas. Supervised classifications based on Support Vector Machine were adopted. Different pansharpening methods using bilinear interpolation technique have been used to merge very high spatial resolution Quickbird multispectral and panchromatic imagery. To develop this study, seven sub-areas were extracted and human segmentations data were created. The quantitative results based on the mean of Probabilistic Rand Index, Variation of Information and Global Consistency Error, computed for all sub-areas, showed similar results by using (0.92, 0.87, 0.87, 1.23, 0,2 respectively) and by not applying (0.93, 0.89, 0.86, 1.23, 0.21 respectively) pansharpening methods.
关键词： Land cover,Pansharpening methods,image processing,Classification,Remote Sensing
更新于2025-09-04 15:30:14
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Application of Morphological Component Analysis to Optical Image Fusion
摘要： The image fusion technique is widely used in remote sensing. Its purpose is to provide comprehensive information without artefacts by combining the partial information from different source images. In this study, we propose a new model of images fusion with very high spatial resolution. We use the separation capacities of the Morphological Component Analysis (MCA) to extract the smooth and texture components of our images. These morphological components are then fused separately using the decomposition in the Laplacian pyramids for the smooth part and bivariate Hahn polynomials for texture part. Finally the image fusion is obtained through linear combination of merged smooth and texture components. The experiments carried out on IKONOS, LANDSAT and Quick Bird remote sensing images show the good performances of our method which has been compared to conventional methods. The performances obtained in our experiments are characterized by a small global metric such as ERGAS equals to 3.88 for IKONOS image and 3.65 for QuickBird image compared to 8.70 for IKONOS image and 6.97 for QuickBird for conventional HIS algorithms. We also have a mean loss of 15% for spectral information compare to those of the conventional methods which revolve around 25%. The degradation of spatial information in order of 17% in contrast to conventional HIS algorithms which oscillate around 21%.
关键词： Bivariate Hahn Polynomials,Texture Analysis,Image Pansharpening,Laplacian Pyramid
更新于2025-09-04 15:30:14
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Improving Hypersharpening for WorldView-3 Data
摘要： In this letter, hypersharpening is analyzed in depth by investigating some weaknesses present in its formulation. It is shown that the key formula of the synthesized band variant can be simplified under certain circumstances. In addition, a novel fusion schema is proposed. As a result, the gain factor adopted to weight the injected detail is computed in a different way. This schema can be applied to fuse a wide range of hyperspectral and multispectral data. In this letter, its effectiveness is demonstrated by taking into account the characteristics of WorldView-3 data.
关键词： pansharpening,image fusion,WorldView-3,remote sensing,Hypersharpening
更新于2025-09-04 15:30:14