A survey of classical methods and new trends in pansharpening of multispectral images

Amro, Israa; Mateos, Javier; Vega, Miguel; Molina, Rafael; Katsaggelos, Aggelos K.

doi:10.1186/1687-6180-2011-79

Cited by 222 publications

(172 citation statements)

References 80 publications

(187 reference statements)

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“…Pansharpening is an image fusion method in which high resolution panchromatic data is fused with lower resolution multispectral data to create a colorized high-resolution dataset. It is a pixel-level fusion technique that increases the spatial resolution while simultaneously preserving the spectral information in the multispectral image, giving the best of both worlds: high spectral resolution and high spatial resolution [26]. This enables to produce images with improved interpretability.…”

Section: Remote Sensing Data Analysismentioning

confidence: 99%

“…This is due to the fact that there cannot be sudden significant changes in the land use/land cover classes within one year as long as there is no change in the policy of the government at that time, as confirmed by key informants and from personal observation of historical happening of such facts in the country. Principal Component Spectral Sharpening technique was used to sharpen the false color composite of December 1979 Landsat MSS multispectral data with a mosaic of high spatial resolution scanned aerial photographs of January 1982 that were ortho-rectified and resampled with a spatial resolution of 15 m. Principal Component Spectral Sharpening assumes that the low spatial resolution spectral bands correspond to the high resolution panchromatic band [27] and relies on the Principal Component Analysis (PCA) mathematical transformation [26]. Ortho-rectification of the January 1982 aerial photos was done in the same way as that of the 1957 aerial photographs.…”

Section: Remote Sensing Data Analysismentioning

confidence: 99%

See 1 more Smart Citation

Identifying Land Use/Cover Dynamics in the Koga Catchment, Ethiopia, from Multi-Scale Data, and Implications for Environmental Change

Yeshaneh

Wagner

Exner-Kittridge

et al. 2013

IJGI

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This study analyzed more than 50 years of land cover and land use changes in the 260 km 2 Koga catchment in North Western Ethiopia. The data used includes 1:50,000 scale aerial photographs, Landsat MSS, TM and ETM images, and ASTER images together with ground truth data collected through field surveys and community elders' interviews. Aerial photographs have high spatial resolution but provide lower spectral resolution than satellite data. While most land use/cover change studies compare changes from different spatial scales, this study applied land use/cover classification techniques to bring the data to a relatively similar scale. The data revealed that woody vegetation decreased from 5,576 ha to 3,012 ha from the 1950s to 2010. Most of the deforestation took place between the 1970s and 1980s, but there is an increasing trend since then. No significant changes were observed in the area used for agriculture that comprises the pastures and crop fields since the 1950s, while there is an enormous increase in the area used for settlement, due to a tremendous increase in population from one point in time to another. The bare lands that used to exist in previous years were found to be totally covered with other land cover/use classes and no bare lands were observed in the study area in the year 2010. Population pressure and land use policies were found to be reasons for the changes in land use/cover while soil degradation,

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Section: Remote Sensing Data Analysismentioning

confidence: 99%

Section: Remote Sensing Data Analysismentioning

confidence: 99%

Identifying Land Use/Cover Dynamics in the Koga Catchment, Ethiopia, from Multi-Scale Data, and Implications for Environmental Change

Yeshaneh

Wagner

Exner-Kittridge

et al. 2013

IJGI

View full text Add to dashboard Cite

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“…The GF2 image comprises four spectral bands: Red (R), green (G), blue (B), and near infrared, as well as one panchromatic band. The pan-and multi-spectral images are fused using the Pan-sharp algorithm [52] and the spatial resolution of the fused image is 0.8 m. The bi-temporal images used for experiments are orthorectified and mainly include the three bands R, G, and B. The image contains 3749 × 3008 pixels.…”

Section: Dataset Descriptionmentioning

confidence: 99%

A Novel Change Detection Approach for Multi-Temporal High-Resolution Remote Sensing Images Based on Rotation Forest and Coarse-to-Fine Uncertainty Analyses

Feng

Sui

et al. 2018

Remote Sensing

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Abstract:In the process of object-based change detection (OBCD), scale is a significant factor related to extraction and analyses of subsequent change data. To address this problem, this paper describes an object-based approach to urban area change detection (CD) using rotation forest (RoF) and coarse-to-fine uncertainty analyses of multi-temporal high-resolution remote sensing images. First, highly homogeneous objects with consistent spatial positions are identified through vector-raster integration and multi-scale fine segmentation. The multi-temporal images are stacked and segmented under the constraints of a historical land use vector map using a series of optimal segmentation scales, ranging from coarse to fine. Second, neighborhood correlation image analyses are performed to highlight pixels with high probabilities of being changed or unchanged, which can be used as a prerequisite for object-based analyses. Third, based on the coarse-to-fine segmentation and pixel-based pre-classification results, change possibilities are calculated for various objects. Furthermore, changed and unchanged objects identified at different scales are automatically selected to serve as training samples. The spectral and texture features of each object are extracted. Finally, uncertain objects are classified using the RoF classifier. Multi-scale classification results are combined using a majority voting rule to generate the final CD results. In experiments using two pairs of real high-resolution remote sensing datasets, our proposed approach outperformed existing methods in terms of CD accuracy, verifying its feasibility and effectiveness.

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“…For the PS based on the MRA approach, most algorithms use the generalized Laplacian pyramid, the discrete wavelet transform and the contourlet transform, among others [31]. The basic idea is to extract the spatial detail information from the PAN image, not present in the low-resolution MS image, to inject it into the latter.…”

Section: Pansharpening Based On the Multi-resolution Approachmentioning

confidence: 99%

Scale-Aware Pansharpening Algorithm for Agricultural Fragmented Landscapes

et al. 2016

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Abstract:Remote sensing (RS) has played an important role in extensive agricultural monitoring and management for several decades. However, the current spatial resolution of satellite imagery does not have enough definition to generalize its use in highly-fragmented agricultural landscapes, which represents a significant percentage of the world's total cultivated surface. To characterize and analyze this type of landscape, multispectral (MS) images with high and very high spatial resolutions are required. Multi-source image fusion algorithms are normally used to improve the spatial resolution of images with a medium spatial resolution. In particular, pansharpening (PS) methods allow one to produce high-resolution MS images through a coherent integration of spatial details from a panchromatic (PAN) image with spectral information from an MS. The spectral and spatial quality of source images must be preserved to be useful in RS tasks. Different PS strategies provide different trade-offs between the spectral and the spatial quality of the fused images. Considering that agricultural landscape images contain many levels of significant structures and edges, the PS algorithms based on filtering processes must be scale-aware and able to remove different levels of detail in any input images. In this work, a new PS methodology based on a rolling guidance filter (RGF) is proposed. The main contribution of this new methodology is to produce artifact-free pansharpened images, improving the MS edges with a scale-aware approach. Three images have been used, and more than 150 experiments were carried out. An objective comparison with widely-used methodologies shows the capability of the proposed method as a powerful tool to obtain pansharpened images preserving the spatial and spectral information.

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A survey of classical methods and new trends in pansharpening of multispectral images

Cited by 222 publications

References 80 publications

Identifying Land Use/Cover Dynamics in the Koga Catchment, Ethiopia, from Multi-Scale Data, and Implications for Environmental Change

Identifying Land Use/Cover Dynamics in the Koga Catchment, Ethiopia, from Multi-Scale Data, and Implications for Environmental Change

A Novel Change Detection Approach for Multi-Temporal High-Resolution Remote Sensing Images Based on Rotation Forest and Coarse-to-Fine Uncertainty Analyses

Scale-Aware Pansharpening Algorithm for Agricultural Fragmented Landscapes

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