Glacier-surface velocities in alpine terrain from optical satellite imagery—Accuracy improvement and quality assessment

Abstract. Nowadays, high-resolution fused satellite imagery is widely used in multiple remote sensing applications. Although the spectral quality of pan-sharpened images plays an important role in many applications, spatial quality becomes more important in numerous cases. The high spatial quality of the fused image is essential for extraction, identification and reconstruction of significant image objects, and will result in producing high-quality large scale maps especially in the urban areas. This paper introduces the most sensitive and effective methods in detecting the spatial distortion of fused images by implementing a number of spatial quality assessment indices that are utilized in the field of remote sensing and image processing. In this regard, in order to recognize the ability of quality assessment indices for detecting the spatial distortion quantity of fused images, input images of the fusion process are affected by some intentional spatial distortions based on non-registration error. The capabilities of the investigated metrics are evaluated on four different fused images derived from Ikonos and WorldView-2 initial images. Achieved results obviously explicate that two methods namely Edge Variance Distortion and the spatial component of QNR metric called Ds are more sensitive and responsive to the imported errors.

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Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining ◽

10.1145/3219819.3219924 ◽

2018 ◽

Cited By ~ 12

Author(s):

Barak Oshri ◽

Annie Hu ◽

Peter Adelson ◽

Xiao Chen ◽

Pascaline Dupas ◽

...

Keyword(s):

Deep Learning ◽

Quality Assessment ◽

Satellite Imagery

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DEM quality assessment for quantification of glacier surface change

Annals of Glaciology ◽

10.3189/172756407782871792 ◽

2007 ◽

Vol 46 ◽

pp. 189-194 ◽

Cited By ~ 12

Author(s):

Addy Pope ◽

Tavi Murray ◽

Adrian Luckman

Keyword(s):

Quality Assessment ◽

Control Points ◽

Glacier Surface ◽

Mountain Glacier ◽

Ground Control Points ◽

Data Quality Assessment ◽

Digital Elevation ◽

Multiple Input ◽

Warning Model

AbstractPhotogrammetric digital elevation models (DEMs) are often used to derive and monitor surfaces in inaccessible areas. They have been used to monitor the spatial and temporal change of glacier surfaces in order to assess glacier response to climate change. However, deriving photogrammetric DEMs of steep mountainous topography where the surface is often obscured by regions of deep shadow and snow is particularly difficult. Assessing the quality of the derived surface can also be problematic, as high-accuracy ground-control points may be limited and poorly distributed throughout the modelled area. We present a method of assessing the quality of a derived surface through a detailed sensitivity analysis of the DEM collection parameters through a multiple input failure warning model (MIFWM). The variance of a DEM cell elevation is taken as an indicator of surface reliability allowing potentially unreliable areas to be excluded from further analysis. This analysis allows the user to place greater confidence in the remaining DEM. An example of this method is presented for a small mountain glacier in Svalbard, and the MIFWM is shown to label as unreliable more DEM cells over the entire DEM area, but fewer over the glacier surface, than other methods of data quality assessment. The MIFWM is shown to be an effective and easily used method for assessing DEM surface quality.

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Geometric Positioning Accuracy Improvement of ZY-3 Satellite Imagery Based on Statistical Learning Theory

Sensors ◽

10.3390/s18061701 ◽

2018 ◽

Vol 18 (6) ◽

pp. 1701 ◽

Cited By ~ 3

Author(s):

Niangang Jiao ◽

Feng Wang ◽

Hongjian You ◽

Mudan Yang ◽

Xinghui Yao

Keyword(s):

Statistical Learning ◽

Satellite Imagery ◽

Learning Theory ◽

Statistical Learning Theory ◽

Accuracy Improvement ◽

Positioning Accuracy

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Evaluation of existing image matching methods for deriving glacier surface displacements globally from optical satellite imagery

Remote Sensing of Environment ◽

10.1016/j.rse.2011.11.024 ◽

2012 ◽

Vol 118 ◽

pp. 339-355 ◽

Cited By ~ 167

Author(s):

T. Heid ◽

A. Kääb

Keyword(s):

Satellite Imagery ◽

Image Matching ◽

Glacier Surface ◽

Matching Methods ◽

Surface Displacements

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Annual and Seasonal Glacier-Wide Surface Mass Balance Quantified from Changes in Glacier Surface State: A Review on Existing Methods Using Optical Satellite Imagery

Remote Sensing ◽

10.3390/rs9050507 ◽

2017 ◽

Vol 9 (5) ◽

pp. 507 ◽

Cited By ~ 9

Author(s):

Antoine Rabatel ◽

Pascal Sirguey ◽

Vanessa Drolon ◽

Philippe Maisongrande ◽

Yves Arnaud ◽

...

Keyword(s):

Mass Balance ◽

Satellite Imagery ◽

Surface State ◽

Surface Mass Balance ◽

Glacier Surface ◽

Surface Mass

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Satellite-Imagery Geometric Accuracy Improvement Based on Direct Correction of Dominant Coefficients

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.86.4.215 ◽

2020 ◽

Vol 86 (4) ◽

pp. 215-224

Author(s):

Xinming Tang ◽

Changru Liu ◽

Ping Zhou ◽

Ning Cao ◽

FengXiang Li ◽

...

Keyword(s):

Satellite Imagery ◽

Satellite Images ◽

Geometric Accuracy ◽

Accuracy Improvement ◽

First Order ◽

Polynomial Coefficients ◽

Sensor Model ◽

Rational Polynomial ◽

Refinement Method ◽

Correlation Problem

An important and difficult point in the application of satellite imagery is refining the positioning model and improving the geometric accuracy. In this study, we focus on improvement in geometric accuracy and develop a new rational function model (RFM) refinement method. First, we derive the conversion relationship between the rigorous sensor model and the RFM, based on which we illustrate the approximate meaning of the zero-order and first-order terms of the rational polynomial coefficients (RPCs). Second, the correlation problem between RPCs and the influence of individual RPCs on geometric positioning accuracy are analyzed and verified. The dominant coefficients that determine geolocation are then identified. Finally, a new RFM refinement method based on direct correction of the dominant coefficients is proposed and validated. The experiments, conducted with ZY3-02 satellite imagery, indicate that the proposed method can effectively improve the geometric accuracy of satellite images.

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