Determining the optimum image fusion method for better interpretation of the surface of the Earth

Yılmaz, Volkan; Güngör, Oğuz

doi:10.1080/00291951.2015.1126761

Cited by 15 publications

(6 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Such a spatial detail enhancement may come in handy in cases where an image of high spectral and spatial fidelity is needed but not available for some reason. Possible reasons include unfavorable weather conditions, financial constraints, or intrinsic sensor limitations that prevent the acquisition of images with high color and spatial detail fidelity at the same time 1,2 . An extensive literature review revealed that the pansharpening has been resorted for a wide range of purposes, including landslide monitoring, 3 change detection, 4 mapping of diseased trees, 5 analyzing agricultural landscapes, 6 crop type differentiation, 7 vegetation mapping, 8 soil mapping, 9 coastline extraction, 10 vineyard segmentation, 11 and so forth.…”

Section: Introductionmentioning

confidence: 99%

The role of image interpolation in pansharpening

Yılmaz

2022

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

Pansharpening is an efficient way of producing images of higher spectral and spatial fidelity. Since pansharpening aims to generate a spectrally enhanced image with the same spatial detail content of the source panchromatic (PAN) image, the source multispectral (MS) image is upsampled to the size of the source PAN image prior to pansharpening. Several image interpolation algorithms have been proposed for this purpose, which may lead the analysts to a confusion as to which of these algorithms should be used for the best pansharpening performance. Hence, this study aimed to investigate the role of widely used image interpolation algorithms in the quality of the pansharpened images. For this purpose, the nearest neighbor interpolation, bilinear interpolation, bicubic interpolation, interpolation with a polynomial kernel of 23 coefficients (INTERP23) and cubic spline interpolation algorithms were tested through several pansharpening techniques on three test sites with different characteristics. Investigations revealed that upsampling the source MS images with the INTERP23 algorithm resulted in the pansharpened images with the optimum spectral and spatial quality.

show abstract

Section: Introductionmentioning

confidence: 99%

The role of image interpolation in pansharpening

Yılmaz

2022

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Surveyed data reflects geometric information and may serve well as auxiliary data. Point cloud are generated by LiDAR laser sensors and mainly reveals elevation of the study area [40][41][42]. PC can be studied separately and be the auxiliary data in RS imagery analysis as well.…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Land Cover Classification Combining Stratified Segmentation and Fusion of Point Cloud and Very High-Spatial Resolution Remote Sensing Image Data

Zhou

Ming

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Traditional and convolutional neural network (CNN)-based geographic object-based image analysis (GeOBIA) land-cover classification methods prosper in remote sensing and generate numerous distinguished achievements. However, a bottleneck emerges and hinders further improvements in classification results, due to the insufficiency of information provided by very high-spatial resolution images (VHSRIs). To be specific, the phenomenon of different objects with similar spectrum and the lack of topographic information (heights) are natural drawbacks of VHSRIs. Thus, multisource data steps into people’s sight and shows a promising future. Firstly, for data fusion, this paper proposed a standard normalized digital surface model (StdnDSM) method which was actually a digital elevation model derived from a digital terrain model (DTM) and digital surface model (DSM) to break through the bottleneck by fusing VHSRI and cloud points. It smoothed and improved the fusion of point cloud and VHSRIs and thus performed well in follow-up classification. The fusion data then were utilized to perform multiresolution segmentation (MRS) and worked as training data for the CNN. Moreover, the grey-level co-occurrence matrix (GLCM) was introduced for a stratified MRS. Secondly, for data processing, the stratified MRS was more efficient than unstratified MRS, and its outcome result was theoretically more rational and explainable than traditional global segmentation. Eventually, classes of segmented polygons were determined by majority voting. Compared to pixel-based and traditional object-based classification methods, majority voting strategy has stronger robustness and avoids misclassifications caused by minor misclassified centre points. Experimental analysis results suggested that the proposed method was promising for object-based classification.

show abstract

“…Görüntü kaynaştırma yüksek konumsal çözünürlüklü bir pankromatik (PAN) görüntüdeki konumsal detayların düşük konumsal çözünürlüklü bir çok bantlı (ÇB) görüntüye aktarılarak yüksek çözünürlüklü bir ÇB görüntü elde edilmesidir (Yilmaz ve Gungor 2016a). Başarılı bir görüntü kaynaştırma algoritması PAN görüntüdeki konumsal detayları aktarırken ÇB görüntünün renk yapısını da mümkün olduğunca korumalıdır.…”

Section: Introductionunclassified

“…Literatürdeki görüntü kaynaştırma yöntemleri hakkında detaylı bilgiler Pohl vd. (1998), Blum ve Liu (2005), Stathaki (2011), Pohl ve van Genderen (2016) ve Yilmaz ve Gungor (2016a) tarafından sağlanmıştır. Brovey, Multiplicative (MCV), Temel Bileşenler Analizi (Principal Component Analysis-PCA) ve Gram-Schmidt (GS) gibi yöntemler konumsal detay aktarımında genelde daha başarılı iken High-Pass Filtering (HPF), Wavelet, Ehlers ve Hyperspherical Colour Sharpening (HCS) gibi daha gelişmiş kaynaştırma yöntemleri renk yapısının korunmasında daha başarılıdır.…”

Section: Introductionunclassified

See 1 more Smart Citation

Kaynaştırılmış Görüntülerden Elde Edilen Doku Özellikleri ile DVM Sınıflandırma Performansının İyileştirilmesi

Yılmaz¹

2019

Geomatik

View full text Add to dashboard Cite

Uzaktan algılanmış görüntülerden elde edilen doku bilgisi yardımıyla yüksek doğruluklu arazi örtüsü haritalarının üretilmesi mümkündür. Kaynaştırılmış bir görüntüdeki doku bilgisinin sınıflandırma işlemine entegre edilmesinin sınıflandırma işleminin doğruluğuna olumlu yönde katkı yapması muhtemeldir. Bu çalışmada Brovey, Multiplicative (MCV), PCA (Principal Component Analysis), Gram-Schmidt (GS), HPF (High-Pass Filtering), Wavelet, Ehlers ve HCS (Hyperspherical Colour Sharpening) kaynaştırma yöntemleri kullanılarak bir WorldView-2 ÇB görüntüsü ile bir WorldView-2 PAN görüntüsü kaynaştırılmıştır. Elde edilen kaynaştırılmış görüntüler Watershed bölütleme (WB) algoritması ile bölütlenmiştir. Elde edilen bölütlerden dört adet eşdizimlilik doku özelliği çıkartılmıştır. Çıkartılan bu doku özellikleri destek vektör makineleri (DVM) sınıflandırıcısına entegre edilerek görüntü üzerindeki sınıfların birbirinden ayrılabilirliğinin arttırılması irdelenmiştir. Deneysel sonuçlar bütün kaynaştırma yöntemlerinden elde edilen doku özelliklerinin sınıflandırma doğruluğunu belli bir oranda arttırdığını göstermektedir. Wavelet ve Ehlers kaynaştırma yöntemlerinden elde edilen doku özelliklerinin sınıflandırma doğruluğunu %20.4 ve %18.9 oranında arttırarak bu alanda en başarılı kaynaştırma yöntemleri oldukları tespit edilmiştir.

show abstract

Determining the optimum image fusion method for better interpretation of the surface of the Earth

Cited by 15 publications

References 37 publications

The role of image interpolation in pansharpening

The role of image interpolation in pansharpening

CNN-Based Land Cover Classification Combining Stratified Segmentation and Fusion of Point Cloud and Very High-Spatial Resolution Remote Sensing Image Data

Kaynaştırılmış Görüntülerden Elde Edilen Doku Özellikleri ile DVM Sınıflandırma Performansının İyileştirilmesi

Contact Info

Product

Resources

About