Pan-sharpening is used to fuse multispectral images and panchromatic images to produce a multispectral image with high spatial resolution. In this paper, we design a new iterative method based on framelet for pan-sharpening. The proposed model takes...
In the literature of pan-sharpening based on neural networks, high resolution multispectral images as ground-truth labels generally are unavailable. To tackle the issue, a common method is to degrade original images into a lower resolution space for...
Pan-sharpening aims to sharpen a low spatial resolution multispectral (MS) image by combining the spatial detail information extracted from a panchromatic (PAN) image. An effective pan-sharpening method should produce a high spatial resolution MS ima...
Pan-sharpening is a pixel-level image fusion process whereby a lower-spatial-resolution multispectral image is merged with a higher-spatial-resolution panchromatic one. One of the drawbacks of this process is that it may introduce spectral or radiome...
In this paper, we propose a cross-direction and progressive network, termed CPNet, to solve the pan-sharpening problem. The full processing of information is the main characteristic of our model, which is reflected as follows: on the one hand, we pro...
In this paper, we propose a new architecture of densely connected convolutional networks for pan-sharpening (DCCNP). Since the traditional convolution neural network (CNN) has difficulty handling the lack of a training sample set in the field of remo...
Pan-sharpening methods allow the transfer of higher resolution panchromatic images to multispectral ones concerning the same scene. Different approaches are available in the literature, and only a part of these approaches is included in remote sensin...
Pan-sharpening methods based on deep neural network (DNN) have produced state-of-the-art fusion performance. However, DNN-based methods mainly focus on the modeling of the local properties in low spatial resolution multispectral (LR MS) and panchroma...
Pan-sharpening is an important means to improve the spatial resolution of multispectral (MS) images. Although a large number of pan-sharpening methods have been developed, improving the spatial resolution of MS while effectively maintaining its spect...
Based on deep learning, various pan-sharpening models have achieved excellent results. However, most of them adopt simple addition or concatenation operations to merge the information of low spatial resolution multi-spectral (LRMS) images and panchro...
In recent years, the demand for very high geometric resolution satellite images has increased significantly. The pan-sharpening techniques, which are part of the data fusion techniques, enable the increase in the geometric resolution of multispectral...
The majority of existing deep learning pan-sharpening methods often use simulated degraded reference data due to the missing of real fusion labels which affects the fusion performance. The normally used convolutional neural network (CNN) can only ext...
Pan-sharpening aims to create high-resolution spectrum images by fusing low-resolution hyperspectral (HS) images with high-resolution panchromatic (PAN) images. Inspired by the Swin transformer used in image classification tasks, this research constr...
High-resolution multi-spectral images are desired for applications in remote sensing. However, multi-spectral images can only be provided in low resolutions by optical remote sensing satellites. The technique of pan-sharpening wants to generate high-...
Pan-sharpening algorithms are data-and computation-intensive, and the processing performance can be poor if common serial processing techniques are adopted. This paper presents a parallel computing paradigm for pan-sharpening algorithms based on a ge...
Pan-sharpening aims to generate high-resolution multispectral (HRMS) images by combining high-resolution panchromatic (PAN) images with low-resolution multispectral (LRMS) data, while maintaining the symmetry of spatial and spectral characteristics....
The increasing availability of sensors enables the combination of a high-spatial-resolution panchromatic image and a low-spatial-resolution multispectral image, which has become a hotspot in recent years for many applications. To address the spectral...
Precision viticulture benefits from the accurate detection of vineyard vegetation from remote sensing, without a priori knowledge of vine locations. Vineyard detection enables efficient, and potentially automated, derivation of spatial measures such...
Wald’s protocol is the most widely accepted protocol to assess pan-sharpening algorithms. In particular, the synthesis property—which is usually validated on a reduced scale—is thought to be a necessary and sufficient condition of a success image fus...
Achieving a balance between spectral resolution and spatial resolution in multi-spectral remote sensing images is challenging due to physical constraints. Consequently, pan-sharpening technology was developed to address this challenge. While signific...
KOMPSAT-3, a Korean earth observing satellite, provides the panchromatic (PAN) band and four multispectral (MS) bands. They can be fused to obtain a pan-sharpened image of higher resolution in both the spectral and spatial domain, which is more infor...
The objective of pan-sharpening is to effectively fuse high-resolution panchromatic (PAN) images with limited spectral information and low-resolution multispectral (LR-MS) images, thereby generating a fused image with a high spatial resolution and ri...
The Pan-Sharpening (PS) techniques provide a better visualization of a multi-band image using the high-resolution single-band image. To support their development and evaluation, in this paper, a novel, accurate, and automatic No-Reference (NR) PS Ima...
Pan-sharpening (PS) is a method of fusing the spatial details of a high-resolution panchromatic (PAN) image with the spectral information of a low-resolution multi-spectral (MS) image. Visual inspection is a crucial step in the evaluation of fused pr...
Most of the existing pan-sharpening quality assessment methods consider only the spectral quality and there are just few investigations, which concentrate on spatial characteristics. Spatial quality of pan-sharpened images is vital in elaborating the...
In modern agriculture, the spatially differentiated assessment of the leaf area index (LAI) is of utmost importance to allow an adapted field management. Current hyperspectral satellite systems provide information with a high spectral but only a medi...
Recent very high spatial resolution (VHR) remote sensing satellites provide high spatial resolution panchromatic (Pan) images in addition to multispectral (MS) images. The pan sharpening process has a critical role in image processing tasks and geosp...
Pan Sharpening is an important part of the Remote Sensing science. Obtaining high spatial resolution data can be crucial in some studies. Sentinel-2 provides data of 10, 20 and 60 m, and it is a promising program for Earth observation studies. Althou...
Pan-sharpening aims at integrating spectral information from a multi-spectral (MS) image and spatial information from a panchromatic (PAN) image in a fused image with both high spectral and spatial resolutions. Numerous pan-sharpening methods are bas...
A critical analysis of remote sensing image fusion methods based on the super-resolution (SR) paradigm is presented in this paper. Very recent algorithms have been selected among the pioneering studies adopting a new methodology and the most promisin...
Remote sensing images have been widely applied in various industries; nevertheless, the resolution of such images is relatively low. Panchromatic sharpening (pan-sharpening) is a research focus in the image fusion domain of remote sensing. Pan-sharpe...
With the rapid development of deep neural networks in the field of remote sensing image fusion, the pan-sharpening method based on convolutional neural networks has achieved remarkable effects. However, because remote sensing images contain complex f...
Commonly used image fusion techniques generally produce good results for images obtained from the same sensor, with a standard ratio of spatial resolution (1:4). However, an atypical high ratio of resolution reduces the effectiveness of fusion method...
Monitoring open water bodies accurately is an important and basic application in remote sensing. Various water body mapping approaches have been developed to extract water bodies from multispectral images. The method based on the spectral water index...
Pan-sharpening is the process of fusing higher spatial resolution panchromatic (PAN) with lower spatial resolution multispectral (MS) imagery to create higher spatial resolution MS images. Here, our overall objective was to pan-sharpen Landsat-8 imag...
GaoFen-2 (GF-2) is a civilian optical satellite self-developed by China equipped with both multispectral and panchromatic sensors, and is the first satellite in China with a resolution below 1 m. Because the pan-sharpening methods on GF-2 imagery hav...
Pan-sharpening aims to generate high-resolution (HR) multispectral (MS) images by fusing HR panchromatic (PAN) and low-resolution (LR) MS images covering the same area. However, due to the lack of real HR MS reference images, how to accurately evalua...
To meet the need for multispectral images having high spatial resolution in practical applications, we propose a dense encoder–decoder network with feedback connections for pan-sharpening. Our network consists of four parts. The first part consists o...
Pan-sharpening is a fusion process that combines a low-spatial resolution, multi-spectral image that has rich spectral characteristics with a high-spatial resolution panchromatic (PAN) image that lacks spectral characteristics. Most previous learning...
RGB-guided spectral recovery algorithms include both spectral reconstruction (SR) methods that map image RGBs to spectra and pan-sharpening (PS) methods, where an RGB image is used to guide the upsampling of a low-resolution spectral image. In this p...
Efficient implementation of remote sensing image classification can facilitate the extraction of spatiotemporal information for land use and land cover (LULC) classification. Mapping LULC change can pave the way to investigate the impacts of differen...
This paper presents two categories of features extraction and mapping suite, a very high-resolution suite and an ultra-resolution suite at 2 m and 0.5 m resolutions, respectively, for the differentiation and mapping of land cover and community-level...
Unmanned aerial vehicles (UAVs) are extensively used to gather remote sensing data, offering high image resolution and swift data acquisition despite being labor-intensive. In contrast, satellite-based remote sensing, providing sub-meter spatial reso...
Cities, the core of the global climate change and economic development, are high impact land cover land use change (LCLUC) hotspots. Comprehensive records of land cover land use dynamics in urban regions are essential for strategic climate change ada...
Pansharpening is one of the main research topics in the field of remote sensing image processing. In pansharpening, the spectral information from a low spatial resolution multispectral (LRMS) image and the spatial information from a high spatial reso...
Satellite-based remote sensing is important for monitoring the spatial distribution of water resources. The water index is currently one of the most widely used water body extraction methods. Based on Sentinel-2 remote sensing image, this study combi...
Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large b...
In order to combine the spectral information of the multispectral (MS) image and the spatial information of the panchromatic (PAN) image, a pan-sharpening method based on β-divergence Non-negative Matrix Factorization (NMF) in the Non-Subsampled...
Monitoring the dynamics of surface water using remotely sensed data generally requires both high spatial and high temporal resolutions. One effective and popular approach for achieving this is image fusion. This study adopts a widely accepted fusion...
The successful launch and operation of Landsat-8 extends the remarkable 40-year acquisition of space-based land remote-sensing data. To respond quickly to emergency needs, real-time data are directly downlinked to 17 ground stations across the world...
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