Integrating temporal and spectral information from low-resolution MODIS and high-resolution optical satellite images: two Hungarian case studies

2007 ◽  
Author(s):  
Daniel Kristof ◽  
Robert Pataki ◽  
Dora Neidert ◽  
Zoltan Nagy ◽  
Krisztina Pinter
Keyword(s):  
High Resolution ◽  
Case Studies ◽  
Satellite Images ◽  
Spectral Information ◽  
Low Resolution ◽  
Hungarian Case ◽  
Optical Satellite Images

Toward a general tropical forest biomass prediction model from very high resolution optical satellite images

2017 ◽  
Vol 200 ◽  
pp. 140-153 ◽  
Author(s):  
P. Ploton ◽  
N. Barbier ◽  
P. Couteron ◽  
C.M. Antin ◽  
N. Ayyappan ◽  
...  
Keyword(s):  
High Resolution ◽  
Prediction Model ◽  
Tropical Forest ◽  
Satellite Images ◽  
Forest Biomass ◽  
Optical Satellite Images ◽  
Very High

AUTOMATIC REGISTRATION OF VECTOR DATA WITH OPTICAL IMAGES

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 191-196
Author(s):  
Y. Tanguy ◽  
J. Michel ◽  
G. Salgues
Keyword(s):  
High Resolution ◽  
Image Registration ◽  
Prior Knowledge ◽  
Open Source Software ◽  
Satellite Images ◽  
Automatic Registration ◽  
Vector Data ◽  
Optical Images ◽  
Optical Satellite Images ◽  
Very High

Abstract. This paper presents a method to perform automatic vector-to-image registration. The proposed method performs well on different kinds of optical satellite images from Very High Resolution (VHR, sub-meter resolution) to images in the 10/20 m resolution range. It allows to automatically register vector dataset such as urban maps (by using building layers). In contrast with existing methods, our method needs few prior-knowledge on the features to match and can therefore adapt to different landscapes.This paper demonstrates the method robustness in several use-cases and presents the implementation which will soon be available as open-source software.

scholarly journals Monitoring of seasonal glacier mass balance over the European Alps using low-resolution optical satellite images

2016 ◽  
Vol 62 (235) ◽  
pp. 912-927 ◽  
Author(s):  
VANESSA DROLON ◽  
PHILIPPE MAISONGRANDE ◽  
ETIENNE BERTHIER ◽  
ELSE SWINNEN ◽  
MATTHIAS HUSS
Keyword(s):  
Satellite Images ◽  
Glacier Mass Balance ◽  
Optical Remote Sensing ◽  
European Alps ◽  
Mass Balances ◽  
Low Resolution ◽  
Linear Relationships ◽  
The Alps ◽  
Optical Satellite Images ◽  
Good Agreement

ABSTRACTWe explore a new method to retrieve seasonal glacier mass balances (MBs) from low-resolution optical remote sensing. We derive annual winter and summer snow maps of the Alps during 1998–2014 using SPOT/VEGETATION 1 km resolution imagery. We combine these seasonal snow maps with a DEM to calculate a ‘mean regional’ altitude of snow (Z) in a region surrounding a glacier. Then, we compare the interannual variation of Z with the observed winter/summer glacier MB for 55 Alpine glaciers over 1998–2008, our calibration period. We find strong linear relationships in winter (mean R2 = 0.84) and small errors for the reconstructed winter MB (mean RMSE = 158 mm (w.e.) a−1). This is lower than errors generally assumed for the glaciological MB measurements (200–400 mm w.e. a−1). Results for summer MB are also satisfying (mean R2 and RMSE, respectively, 0.74 and 314 mm w.e. a−1). Comparison with observed seasonal MB available over 2009–2014 (our evaluation period) for 19 glaciers in winter and 13 in summer shows good agreement in winter (RMSE = 405 mm w.e. a−1) and slightly larger errors in summer (RMSE = 561 mm w.e. a−1). These results indicate that our approach might be valuable for remotely determining the seasonal MB of glaciers over large regions.

THE RECONSTRUCTION OF SATELLITE IMAGES BASED ON FRACTAL INTERPOLATION

Fractals ◽  
2011 ◽  
Vol 19 (03) ◽  
pp. 347-354 ◽  
Author(s):  
CHING-JU CHEN ◽  
SHU-CHEN CHENG ◽  
Y. M. HUANG
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Satellite Image ◽  
Image Data ◽  
Image Resolution ◽  
Source Image ◽  
Fractal Interpolation ◽  
Low Resolution ◽  
Resolution Image ◽  
High Resolution Image

This study discussed the application of a fractal interpolation method in satellite image data reconstruction. It used low-resolution images as the source data for fractal interpolation reconstruction. Using this approach, a high-resolution image can be reconstructed when there is only a low-resolution source image available. The results showed that the high-resolution image data from fractal interpolation can effectively enhance the sharpness of the border contours. Implementing fractal interpolation on an insufficient image resolution image can avoid jagged edges and mosaic when enlarging the image, as well as improve the visibility of object features in the region of interest. The proposed approach can thus be a useful tool in land classification by satellite images.

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