scholarly journals INFLUENCE OF IMAGE INTERPOLATION ON IMAGERY-BASED DETECTION AND COMPENSATION OF SATELLITE JITTER

2020 ◽  
Vol V-1-2020 ◽  
pp. 157-163
Author(s):  
Z. Ye ◽  
Y. Xu ◽  
C. Wei ◽  
X. Tong ◽  
U. Stilla
Keyword(s):  
Satellite Images ◽  
Image Interpolation ◽  
Systematic Bias ◽  
Image Correction ◽  
Distortion Compensation ◽  
Phase Errors ◽  
High Resolution Satellite Images ◽  
Geometric Quality ◽  
Subpixel Measurements

Abstract. Satellite jitter is a common and complicated phenomenon that degrades the geometric quality of high-resolution satellite images. Imagery-based detection and compensation of satellite jitter have recently been widely concerned. However, most of the existing studies overlook the issue of image interpolation in this topic involving subpixel measurements. In this study, the influence of image interpolation on imagery-based detection and compensation of satellite jitter is investigated. Four different interpolators are separately applied in dense least squares matching for jitter detection based on parallax observation and in intensity resampling for jitter distortion compensation. Experiments were carried out using ZiYuan-3 dataset to compare and analyze the results in the case of different image interpolation. The experimental results demonstrate the influence of image interpolation on imagery-based jitter processing. Inferior interpolators can induce pixel locking effect in subpixel matching and position-dependent systematic bias after image correction, which deteriorate the performance of jitter detection and compensation. To ensure the reliability, sophisticated interpolation algorithms with smaller phase errors are preferable in imagery-based jitter detection and compensation.

Determining the Quality of Compression in High Resolution Satellite Images Using Different Compression Methods

2015 ◽  
Vol 20 ◽  
pp. 202-217
Author(s):  
S. Sanjith ◽  
R. Ganesan
Keyword(s):  
High Resolution ◽  
Image Quality ◽  
Satellite Images ◽  
Signal To Noise Ratio ◽  
Joint Photographic Expert Group ◽  
Expert Group ◽  
Signal To Noise ◽  
High Resolution Satellite Images ◽  
Noise Ratio

Measuring the quality of image is very complex and hard process since the opinion of the humans are affected by physical and psychological parameters. So many techniques are invented and proposed for image quality analysis but none of the methods suits best for it. Assessment of image quality plays an important role in image processing. In this paper we present the experimental results by comparing the quality of different satellite images (ALOS, RapidEye, SPOT4, SPOT5, SPOT6, SPOTMap) after compression using four different compression methods namely Joint Photographic Expert Group (JPEG), Embedded Zero tree Wavelet (EZW), Set Partitioning in Hierarchical Tree (SPIHT), Joint Photographic Expert Group – 2000 (JPEG 2000). The Mean Square Error (MSE), Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR) values are calculated to determine the quality of the high resolution satellite images after compression.

scholarly journals GEOMETRIC QUALITY EVALUATION METHOD FOR MULTI-VIEW SATELLITE IMAGES IN 3D RECONSTRUCTION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 533-539
Author(s):  
H. Yi ◽  
X. Chen ◽  
D. Wang ◽  
S. Du ◽  
B. Xu ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Satellite Images ◽  
Evaluation Method ◽  
Satellite Image ◽  
Geometric Distortion ◽  
Geometric Quality ◽  
Deviation Coefficient ◽  
Ground Grid ◽  
Straight Lines

Abstract. The quality of the 3D model reconstructed using multi-view satellite image depends on the quality of the image. To evaluate the geometric quality of the satellite image, we proposed a method to evaluate the geometric distortion for satellite images and defined the deviation coefficient as a metric to evaluate the bending degree of a curve. After projecting a ground grid consisting of straight lines into the image space, the geometric distortion of the image can be evaluated quantitatively by calculating the deviation coefficients of the projection trajectories. Experiments have been carried out with three datasets obtained by JiLin-1, GaoFen-2, and WorldView-3 respectively. The results show that the proposed method can used to evaluate the geometric quality of satellite images effectively, and this evaluation method will be useful in image selecting in 3D reconstruction using multi-view satellite images.

scholarly journals GEOMETRIC ACCURACY ANALYSIS OF WORLDDEM IN RELATION TO AW3D30, SRTM AND ASTER GDEM2

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 211-217 ◽  
Author(s):  
S. Bayburt ◽  
A. B. Kurtak ◽  
G. Büyüksalih ◽  
K. Jacobsen
Keyword(s):  
Satellite Images ◽  
Error Component ◽  
Surface Model ◽  
Phase Errors ◽  
Contour Lines ◽  
Interferometric Phase ◽  
Lower Height ◽  
Definition Of ◽  
Optical Satellite Images

In a project area close to Istanbul the quality of WorldDEM, AW3D30, SRTM DSM and ASTER GDEM2 have been analyzed in relation to a reference aerial LiDAR DEM and to each other. The random and the systematic height errors have been separated. The absolute offset for all height models in X, Y and Z is within the expectation. The shifts have been respected in advance for a satisfying estimation of the random error component. All height models are influenced by some tilts, different in size. In addition systematic deformations can be seen not influencing the standard deviation too much. The delivery of WorldDEM includes information about the height error map which is based on the interferometric phase errors, and the number and location of coverage’s from different orbits. A dependency of the height accuracy from the height error map information and the number of coverage’s can be seen, but it is smaller as expected. WorldDEM is more accurate as the other investigated height models and with 10 m point spacing it includes more morphologic details, visible at contour lines. The morphologic details are close to the details based on the LiDAR digital surface model (DSM). As usual a dependency of the accuracy from the terrain slope can be seen. In forest areas the canopy definition of InSAR X- and C-band height models as well as for the height models based on optical satellite images is not the same as the height definition by LiDAR. In addition the interferometric phase uncertainty over forest areas is larger. Both effects lead to lower height accuracy in forest areas, also visible in the height error map.

Evaluating the Quality of Compression in Very High Resolution Satellite Images Using Different Compression Methods

2015 ◽  
Vol 19 ◽  
pp. 91-102 ◽  
Author(s):  
S. Sanjith ◽  
R. Ganeshan
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Sensing Technology ◽  
High Resolution Satellite Images ◽  
Data Volume ◽  
Noise Ratio ◽  
Very High

The rapid growth of remote sensing technology has a great advantage in producing high resolution images which are huge in data volume. Due to the huge volume it is tedious to store and transmit the data. In order to overcome this, a good compression algorithm should be used to compress the data before storing are transmitting. In this paper we have chosen seven different very high resolution satellite images namely Worldview 3, Worldview 2, GeoEye-1, Worldview 1, Pleiades, Quick Bird and IKONOS they are compressed using three different compression methods JPEG, SPIHT and JPEG2000. The Mean square Error, Signal to noise Ratio and Peak Signal to Noise Ratio are calculated to evaluate the quality of the compression methods in very high resolution satellite images.

Analysis of the influence of the hole depth on the geometric quality of the machined part, when the cutting fluid and the machining parameters are varied

2017 ◽  
Author(s):  
Eder Silva Costa ◽  
Pedro Henrique Pires França ◽  
Leonardo Rosa Ribeiro da Silva ◽  
Wisley Sales ◽  
Álisson Rocha Machado ◽  
...  
Keyword(s):  
Cutting Fluid ◽  
Machining Parameters ◽  
Hole Depth ◽  
Geometric Quality

scholarly journals Remote Sensing Methods for the Retrieval of Inventory and Bioproductivity Parameters of Forests Using High Resolution Satellite Images

2019 ◽  
Vol 75 ◽  
pp. 01003 ◽  
Author(s):  
Egor Dmitriev ◽  
Vladimir Kozoderov ◽  
Sergey Donskoy ◽  
Petr Melnik ◽  
Anton Sokolov
Keyword(s):  
Satellite Images ◽  
High Spatial Resolution ◽  
Texture Features ◽  
Forest Species ◽  
Effective Learning ◽  
Estimated Parameters ◽  
High Resolution Satellite Images ◽  
Automated Processing ◽  
Tver Region ◽  
Remote Sensing Methods

A method for automated processing high spatial resolution satellite images is proposed to retrieve inventory and bioproductivity parameters of forest stands. The method includes effective learning classifiers, inverse modeling, and regression modeling of the estimated parameters. Spectral and texture features are used to classify forest species. The results of test experiments for the selected area of Savvatievskoe forestry (Russia, Tver region) are presented. Accuracy estimates obtained using ground-based measurements demonstrate the effectiveness of using the proposed techniques to automate the process of updating information for the State Forest Inventory program of Russia.

scholarly journals Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of “Super Resolution-Then-Blend” and “Blend-Then-Super Resolution” Approaches

2021 ◽  
Vol 13 (4) ◽  
pp. 606
Author(s):  
Tee-Ann Teo ◽  
Yu-Ju Fu
Keyword(s):  
Satellite Images ◽  
Traditional Approach ◽  
Spectral Band ◽  
Super Resolution ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fusion Model ◽  
Image Blending ◽  
Fine Resolution

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.

SateLoc: A Virtual Fingerprinting Approach to Outdoor LoRa Localization Using Satellite Images

2021 ◽  
Vol 17 (4) ◽  
pp. 1-28
Author(s):  
Yuxiang Lin ◽  
Wei Dong ◽  
Yi Gao ◽  
Tao Gu
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Satellite Images ◽  
Low Cost ◽  
Tracking Error ◽  
Location Based Services ◽  
Distance Constraint ◽  
Combination Strategy ◽  
Land Cover Types ◽  
High Resolution Satellite Images

With the increasing relevance of the Internet of Things and large-scale location-based services, LoRa localization has been attractive due to its low-cost, low-power, and long-range properties. However, existing localization approaches based on received signal strength indicators are either easily affected by signal fading of different land-cover types or labor intensive. In this work, we propose SateLoc, a LoRa localization system that utilizes satellite images to generate virtual fingerprints. Specifically, SateLoc first uses high-resolution satellite images to identify land-cover types. With the path loss parameters of each land-cover type, SateLoc can automatically generate a virtual fingerprinting map for each gateway. We then propose a novel multi-gateway combination strategy, which is weighted by the environmental interference of each gateway, to produce a joint likelihood distribution for localization and tracking. We implement SateLoc with commercial LoRa devices without any hardware modification, and evaluate its performance in a 227,500-m urban area. Experimental results show that SateLoc achieves a median localization error of 43.5 m, improving more than 50% compared to state-of-the-art model-based approaches. Moreover, SateLoc can achieve a median tracking error of 37.9 m with the distance constraint of adjacent estimated locations. More importantly, compared to fingerprinting-based approaches, SateLoc does not require the labor-intensive fingerprint acquisition process.

An Approach for Building Rooftop Border Extraction from Very High-Resolution Satellite Images

2021 ◽  
pp. 1-11
Author(s):  
Yasser Mostafa ◽  
Mahmoud Nokrashy O. Ali ◽  
Faten Mostafa ◽  
Mohamed Yousef
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
High Resolution Satellite Images ◽  
Very High
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