Image Degradation for Quality Assessment of Pan-Sharpening Methods

Dou, Wenbin

doi:10.3390/rs10010154

Cited by 18 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For the pan-sharpening, a Hybrid Fusion Technique -Wavelet Principal Component (WPC) was used. For the quantitative analyses of the pan-sharpened image, Wald`s protocol was followed which the most widely used one for validation of pan-sharpening methods [18]. For the quantitative analyses, four indices were used: correlation coefficient (CC) which provides correlation between the fused and the reference image, Universal Image Quality Index (UIQI) which uses covariance, variance, and means of fused and reference image [19], Relative Average Spectral Error (RASE) [20], and Spectral Angle Mapper (SAM), curtail for the case under concern [21].…”

Section: Methodsmentioning

confidence: 99%

Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands

Kaplan¹,

Avdan²

2018

Preprint

View full text Add to dashboard Cite

Wetlands benefits can be summarized but are not limited to their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Over the past few decades, remote sensing and geographical information technologies has proven to be a useful and frequent applications in monitoring and mapping wetlands. Combining both optical and microwave satellite data can give significant information about the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing data from different sensors, such as radar and optical remote sensing data, can increase the wetland classification accuracy. In this paper we investigate the ability of fusion two fine spatial resolution satellite data, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, for mapping wetlands. As a study area in this paper, Balikdami wetland located in the Anatolian part of Turkey has been selected. Both Sentinel-1 and Sentinel-2 images require pre-processing before their use. After the pre-processing, several vegetation indices calculated from the Sentinel-2 bands were included in the data set. Furthermore, an object-based classification was performed. For the accuracy assessment of the obtained results, number of random points were added over the study area. In addition, the results were compared with data from Unmanned Aerial Vehicle collected on the same data of the overpass of the Sentinel-2, and three days before the overpass of Sentinel-1 satellite. The accuracy assessment showed that the results significant and satisfying in the wetland classification using both multispectral and microwave data. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, with an overall classification accuracy of approximately 90% in the object-based classification. Compared with the high resolution UAV data, the classification results give promising results for mapping and monitoring not just wetlands, but also the sub-classes of the study area. For future research, multi-temporal image use and terrain data collection are recommended.

show abstract

Section: Methodsmentioning

confidence: 99%

Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands

Kaplan¹,

Avdan²

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Herhangi bir pan-keskinleştirilmiş görüntü için hesaplanan metrik değeri referans değere ne kadar yakınsa spektral veya uzamsal detay kalitesi o kadar yüksektir. Pan-keskinleştirilmiş görüntülerin spektral kalitelerinin nicel metriklerle irdelenmesi için Wald vd., (1997) tarafından önerilen protokolden sıklıkla faydalanılmaktadır (Alparone vd., 2008;Amro vd., 2011;Delleji vd., 2016;Dou, 2018;Serifoglu Yilmaz vd., 2019;Yilmaz vd., 2021). Bu protokole göre pan-keskinleştirilmiş bir görüntünün çözünürlüğü girdi çok bantlı görüntünün çözünürlüğüne kötüleştirildiğinde elde edilen görüntü, girdi çok bantlı görüntü ile aynı olmalıdır.…”

Section: Pan-keskinleştirmede Doğruluk Analiziunclassified

Çok bantlı görüntülerde pan-keskinleştirme üzerine bir inceleme

Yilmaz¹,

Yılmaz²,

Güngör³

2021

Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi

View full text Add to dashboard Cite

ÖzUzaktan algılama uyduları, algılayıcılarındaki teknik kısıtlamalardan dolayı hem uzamsal detay kalitesi hem de spektral kalitesi yüksek görüntüler üretememektedir. Bu durum, kullanıcıları yüksek uzamsal çözünürlüklü çok bantlı görüntüler elde edebilme konusunda yeni arayışlar içine sokmaktadır. Pan-keskinleştirme işlemi bu probleme etkin bir çözüm sunmaktadır. Pan-keskinleştirme, yüksek uzamsal çözünürlüklü bir pankromatik görüntünün uzamsal detaylarının, yüksek spektral çözünürlüklü çok bantlı bir görüntüye aktarılarak uzamsal çözünürlüğü yüksek çok bantlı bir görüntü üretilmesi işlemidir. Literatürde pan-keskinleştirme için oldukça fazla sayıda yöntem geliştirilmiştir. Bu yöntemlerin her birinin kendine has avantaj ve dezavantajları vardır. Bu durum, kullanıcıları hangi durumda hangi yöntemin kullanılması gerektiği hususunda tereddüte düşürmektedir. Genel amacı, literatürdeki çeşitli konvansiyonel ve gelişmiş pankeskinleştirme yöntemleri hakkında teorik bilgiler vermek ve bu yöntemlerin hangi durumlarda kullanılabileceği hususunda analistlere yol göstermek olan bu çalışmanın, pan-keskinleştirme hakkında iyi bir rehber olacağı kanaatindeyiz. Çalışmada, ayrıca pan-keskinleştirilmiş görüntülerin spektral ve uzamsal detay kalitelerinin görsel ve sayısal olarak nasıl değerlendirilebileceği hakkında da bilgiler verilmiştir.

show abstract

“…For the pansharpening, a Hybrid Fusion Technique -Wavelet Principal Component (WPC) was used. For the quantitative analyses of the pan-sharpened image, Wald`s protocol was followed which the most widely used one for validation of pan-sharpening methods (Dou 2018). For the quantitative analyses, four indices were used: correlation coefficient (CC) which provides correlation between the fused and the reference image, Universal Image Quality Index (UIQI) which uses covariance, variance, and means of fused and reference image (Pohl and Van Genderen 2016), Relative Average Spectral Error (RASE) (Ranchin and Wald 2000), and Spectral Angle Mapper (SAM), curtail for the case under concern (Kaplan et al 2018).…”

Section: Pre-processingmentioning

confidence: 99%

Sentinel-1 and Sentinel-2 Data Fusion for Wetlands Mapping: Balikdami, Turkey

Kaplan¹,

Avdan²

2018

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

View full text Add to dashboard Cite

ABSTRACT:Wetlands provide a number of environmental and socio-economic benefits such as their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Remote sensing technology has proven to be a useful and frequent application in monitoring and mapping wetlands. Combining optical and microwave satellite data can help with mapping and monitoring the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing radar and optical remote sensing data can increase the wetland classification accuracy. In this paper, data from the fine spatial resolution optical satellite, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, were fused for mapping wetlands. Both Sentinel-1 and Sentinel-2 images were pre-processed. After the pre-processing, vegetation indices were calculated using the Sentinel-2 bands and the results were included in the fusion data set. For the classification of the fused data, three different classification approaches were used and compared. The results showed significant improvement in the wetland classification using both multispectral and microwave data. Also, the presence of the red edge bands and the vegetation indices used in the data set showed significant improvement in the discrimination between wetlands and other vegetated areas. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, showing an overall classification accuracy of approximately 90% in the object-based classification method. For future research, we recommend multi-temporal image use, terrain data collection, as well as a comparison of the used method with the traditional image fusion techniques.

show abstract

Image Degradation for Quality Assessment of Pan-Sharpening Methods

Cited by 18 publications

References 20 publications

Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands

Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands

Çok bantlı görüntülerde pan-keskinleştirme üzerine bir inceleme

Sentinel-1 and Sentinel-2 Data Fusion for Wetlands Mapping: Balikdami, Turkey

Contact Info

Product

Resources

About