Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data

Yuhendra,; Alimuddin, Ilham; Sumantyo, Josaphat Tetuko Sri; Kuze, Hiroaki

doi:10.1016/j.jag.2012.01.013

Cited by 87 publications

(35 citation statements)

References 39 publications

(43 reference statements)

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“…Herewith, the Supervised Minimum Distance classifier was applied to the pan-sharpened WV2 imagery. Results in Table 2 suggest moderate agreement (0.4-0.8) on Kappa coefficient (KC) [45] with 70.59% overall accuracy (OA), less than the previous study, which also produced a confusion matrix for WV2 images and which found 87.87% user accuracy and 77.91% producer accuracy for the roof class [46]. However, 81.47% OA with 0.75 KC of their study is categorized as moderate agreement, which is in line with results of the present study.…”

Section: Accuracy Assessment Of Roof Classificationmentioning

confidence: 63%

Pitch and Flat Roof Factors’ Association with Spatiotemporal Patterns of Dengue Disease Analysed Using Pan-Sharpened Worldview 2 Imagery

Rinawan

Tateishi

Raksanagara

et al. 2015

IJGI

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Dengue disease incidence is related with the construction of a house roof, which is an Aedes mosquito habitat. This study was conducted to classify pitch roof (PR) and flat roof (FR) surfaces using pan-sharpened Worldview 2 to identify dengue disease patterns (DDPs) and their association with DDP. A Supervised Minimum Distance classifier was applied to 653 training data from image object segmentations: PR (81 polygons), FR (50), and non-roof (NR) class (522). Ground validation of 272 pixels (52 for PR, 51 for FR, and 169 for NR) was done using a global positioning system (GPS) tool. Getis-Ord score pattern analysis was applied to 1154 dengue disease incidence with address-approach-based data with weighted temporal value of 28 days within a 1194 m spatial radius. We used ordinary least squares (OLS) and geographically weighted regression (GWR) to assess

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Section: Accuracy Assessment Of Roof Classificationmentioning

confidence: 63%

Pitch and Flat Roof Factors’ Association with Spatiotemporal Patterns of Dengue Disease Analysed Using Pan-Sharpened Worldview 2 Imagery

Rinawan

Tateishi

Raksanagara

et al. 2015

IJGI

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“…SVM is a machinelearning technique that is well adapted to solving nonlinear, high dimensional space classifications. SVM can be used for remote sensing applications, for classification of either multispectral or hyperspectral data, in which there is a spectral similarity between the pixels [42]. SVM aims to identify the boundaries between classes in n-dimensional spectral-space [43].…”

Section: Case Study Area 2: Mironikitas Areamentioning

confidence: 99%

Detection of olive oil mill waste (OOMW) disposal areas using high resolution GeoEye’s OrbView-3 and Google Earth images

2016

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Abstract:The olive oil industry is considered to be as one of the driving sectors of the agricultural economy of the Mediterranean basin. The extraction of olive oil generates huge quantities of wastes that may have a great impact on land and water environments due to high concentrations in phenolic compounds that could cause ophytotoxicity. This paper aims to examine the potential use of freely distributed satellite images for the detection of olive oil mil waste (OOMW) areas in the island of Crete through the use of two cases studies. In the first case study an archive GeoEye OrbView-3 image was used to detect OOMW areas using the Spectral Angle Mapper detection algorithm and other geometric and topographic parameters. In the second case study, Google Earth images were examined through different classification algorithms at different scales. The overall results demonstrate that remote sensing techniques can be used as an alternative to field observations so as to detect and monitor OOMW areas Furthermore, freely distributed RGB images from digital globes (such as Google Earth) can be sufficiently and effectively used for this purpose.

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“…This approach for quality assessment is used in many studies. For details, works presented by Tsai (2004), Zhang (2008), Khan et al (2008), Chen et al (2008), Ehlers (2009), Makarau et al (2012), Yuhendra et al (2012), Yusuf et al (2013) and Sarp (2014) can be referred. Aforementioned works require a reference data which is generally a reference image at the same resolution with pansharpened images.…”

Section: Introductionmentioning

confidence: 99%

Pan Sharpening Quality Investigation of Turkish in-Operation Remote Sensing Satellites: Applications With Rasat and Göktürk-2 Images

Özendi

Topan

Cam

et al. 2016

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

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ABSTRACT:Recently two optical remote sensing satellites, RASAT and GÖKTÜRK-2, launched successfully by the Republic of Turkey. RASAT has 7.5 m panchromatic, and 15 m visible bands whereas GÖKTÜRK-2 has 2.5 m panchromatic and 5 m VNIR (Visible and Near Infrared) bands. These bands with various resolutions can be fused by pan-sharpening methods which is an important application area of optical remote sensing imagery. So that, the high geometric resolution of panchromatic band and the high spectral resolution of VNIR bands can be merged. In the literature there are many pan-sharpening methods. However, there is not a standard framework for quality investigation of pan-sharpened imagery.The aim of this study is to investigate pan-sharpening performance of RASAT and GÖKTÜRK-2 images. For this purpose, pansharpened images are generated using most popular pan-sharpening methods IHS, Brovey and PCA at first. This procedure is followed by quantitative evaluation of pan-sharpened images using Correlation Coefficient (CC), Root Mean Square Error (RMSE), Relative Average Spectral Error (RASE), Spectral Angle Mapper (SAM) and Erreur Relative Globale Adimensionnelle de Synthése (ERGAS) metrics. For generation of pan-sharpened images and computation of metrics SharpQ tool is used which is developed with MATLAB computing language. According to metrics, PCA derived pan-sharpened image is the most similar one to multispectral image for RASAT, and Brovey derived pan-sharpened image is the most similar one to multispectral image for GÖKTÜRK-2. Finally, pansharpened images are evaluated qualitatively in terms of object availability and completeness for various land covers (such as urban, forest and flat areas) by a group of operators who are experienced in remote sensing imagery.

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Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data

Cited by 87 publications

References 39 publications

Pitch and Flat Roof Factors’ Association with Spatiotemporal Patterns of Dengue Disease Analysed Using Pan-Sharpened Worldview 2 Imagery

Pitch and Flat Roof Factors’ Association with Spatiotemporal Patterns of Dengue Disease Analysed Using Pan-Sharpened Worldview 2 Imagery

Detection of olive oil mill waste (OOMW) disposal areas using high resolution GeoEye’s OrbView-3 and Google Earth images

Pan Sharpening Quality Investigation of Turkish in-Operation Remote Sensing Satellites: Applications With Rasat and Göktürk-2 Images

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