An Adjustable Pan-Sharpening Approach for IKONOS/QuickBird/GeoEye-1/WorldView-2 Imagery

Tu, Te-Ming; Hsu, Ching-Luh; Tu, Pin-Yi; Lee, Ching-Hai

doi:10.1109/jstars.2011.2181827

Cited by 50 publications

(54 citation statements)

References 18 publications

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“…SFIM is similar to the -smart‖ mode of the Hyperspherical Color Sharpening (HCS) algorithm proposed by Padwick et al [30] and recently implemented in the ERDAS Imagine software package, while BT is similar to the -naï ve‖ mode of HCS [31]. Although many studies have evaluated the effects of FIHS, BT, AWT, and SFIM on the MS bands (e.g., [10,25,27,31]), the effects of VI transformations on the pansharpened bands has not been investigated in detail. This type of investigation is necessary because VIs will have an impact on the spectral and spatial quality of the pansharpened image bands.…”

Section: Fast Pansharpening Algorithmsmentioning

confidence: 99%

Effects of Pansharpening on Vegetation Indices

Johnson

2014

IJGI

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This study evaluated the effects of image pansharpening on Vegetation Indices (VIs), and found that pansharpening was able to downscale single-date and multi-temporal Landsat 8 VI data without introducing significant distortions in VI values. Four fast pansharpening methods-Fast Intensity-Hue-Saturation (FIHS), Brovey Transform (BT), Additive Wavelet Transform (AWT), and Smoothing Filter-based Intensity Modulation (SFIM)-and two VIs-Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR)-were tested. The NDVI and SR formulas were both found to cause some spatial information loss in the pansharpened multispectral (MS) bands, and this spatial information loss from VI transformations was not specific to Landsat 8 imagery (it will occur for any type of imagery). BT, SFIM, and other similar pansharpening methods that inject spatial information from the panchromatic (Pan) band by multiplication, lose all of the injected spatial information after the VI calculations. FIHS, AWT, and other similar pansharpening methods that inject spatial information by addition, lose some spatial information from the Pan band after VI calculations as well. Nevertheless, for all of the single-and multi-date VI images, the FIHS and AWT pansharpened images were more similar to the higher resolution reference data than the unsharpened VI images were, indicating that pansharpening was effective in downscaling the VI data. FIHS best enhanced the spectral and spatial information of the single-date and multi-date VI images, followed by AWT, and neither significantly over-or under-estimated VI values.

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Section: Fast Pansharpening Algorithmsmentioning

confidence: 99%

Effects of Pansharpening on Vegetation Indices

Johnson

2014

IJGI

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“…We utilized two different algorithms depending on the sensor type. For GeoEye and IKONOS imagery the Hyperspherical Color Sharpening (HCS) was used while the WorldView data has been processed with the University of New Brunswick (UNB) algorithm (Christopher and Deskevich 2014;Zhang and Mishra 2014;Ghosh and Joshi 2013;Tu et al 2012). Further pre-processing steps included orthorectification, resampling and co-registration of the imagery.…”

Section: Methodsmentioning

confidence: 99%

Object-based image analysis for the assessment of mineral extraction in conflict regions – a case study in the Democratic Republic of the Congo

Kranz¹,

Schöepfer²,

Spröhnle³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this study object-based image analysis (OBIA) techniques were applied to assess land cover changes related to mineral extraction in a conflict-affected area of the eastern Democratic Republic of the Congo (DRC) over a period of five years based on very high resolution (VHR) satellite data of different sensors. Object-based approaches explicitly consider spatio-temporal aspects which allow extracting important information to document mining activities. The use of remote sensing data as an independent, up-to-date and reliable data source provided hints on the general development of the mining sector in relation to socio-economic and political decisions. While in early 2010, the situation was still characterised by an intensification of mineral extraction, a mining ban between autumn 2010 and spring 2011 marked the starting point for a continuous decrease of mining activities. The latter can be substantiated through a decrease in the extend of the mining area as well as of the number of dwellings in the nearby settlement. A following demilitarisation and the mentioned need for accountability with respect to the origin of certain minerals led to organised, more industrialized exploitation. This development is likewise visible on satellite imagery as typical clearings within forested areas. The results of the continuous monitoring in turn facilitate non-governmental organisations (NGOs) to further foster the mentioned establishment of responsible supply chains by the mining industry throughout the entire period of investigation.

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“…The histogram matched PAN image is replaced with the intensity component and the pansharpened image is obtained after inverse HCS transformation (Chris, 2010). HCS pansharpening approach can be easily described in terms of CS based general pansharpening model (Tu., 2012).…”

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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An Adjustable Pan-Sharpening Approach for IKONOS/QuickBird/GeoEye-1/WorldView-2 Imagery

Cited by 50 publications

References 18 publications

Effects of Pansharpening on Vegetation Indices

Effects of Pansharpening on Vegetation Indices

Object-based image analysis for the assessment of mineral extraction in conflict regions – a case study in the Democratic Republic of the Congo

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

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