Analysis of Satellite Images for the Extraction of Structural Features

Abraham, Lizy; Sasikumar, M.

doi:10.1080/02564602.2014.892737

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…InSAR techniques can monitor infrastructure both day and night, penetrate cloud cover, produce accurate and high-resolution imagery, provide high-density measurements, have a relatively short satellite repetition time, and be a cost-effective and near real-time method. However, processing the collected InSAR data is time consuming and requires expertise [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Improved Method for Estimating Construction Year of Road Bridges by Analyzing Landsat Normalized Difference Water Index 2

2023

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The construction year of road bridges plays an important role in bridge management systems. Based on the age of road bridges and other factors, deterministic and probabilistic deterioration models can be used to calculate deterioration rates and predict the future physical condition of road bridges. Two new techniques are proposed in this manuscript for estimating the construction year of road bridges by analyzing the normalized difference water index 2 (NDWI_2). Technique 1 uses both the target bridge point (TBP) and a selected optimal reference control point, while Technique 2 uses only the TBP. Landsat 5 Thematic Mapper NDWI_2 data were analyzed at all 44 road bridges in Nago City, Japan of the bridges’ overall length ≤ 100 m and construction year between 1990 and 2006. The sequential t-test analysis of the regime shift method, at a significance level α = 0.05 and cutoff length l = 2 to l = 27, was used to interpret the estimated construction year from the NDWI_2 for both techniques. Both techniques successfully determined the estimated construction year, which was statistically significant with p-values < 0.05, except for seven road bridges in Technique 1 and one road bridge in Technique 2. The correlation and comparative analysis of the actual and estimated construction years yielded R2 = 0.24 and R2 = 0.33, as well as an average deviation of S = 5.81 years and S = 4.08 years for Technique 1 and Technique 2, respectively. The findings suggest that Technique 2 is more accurate and provides a better estimate than Technique 1. It was observed that, as the cutoff length l increased, the absolute error between the actual and estimated construction year increased. Therefore, as a measure of accuracy, the upper limit of cutoff length l was set to l≤ 12. It was also observed that the increase in the bridge’s overall length and forested area contributed to the accuracy of the results. By using the construction year as one of the inputs into bridge management systems, bridge managers can make more informed decisions about how best to maintain and improve road bridges to ensure user safety and road bridge preservation for the future.

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Section: Introductionmentioning

confidence: 99%

Improved Method for Estimating Construction Year of Road Bridges by Analyzing Landsat Normalized Difference Water Index 2

2023

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“…Structural displacement monitoring based on computer vision has been applied in many tasks of bridge health monitoring, such as bridge deflection measurement [32], bridge alignment [33], bearing capacity evaluation [34], finite element model calibration [35], modal analysis [36], damage identification [18], cable force detection [29], and dynamic weighing assistance [12]. The images can also improve the accuracy of the estimation part through several means, such as deblurring, denoising and image enhancement, and even satellite images are gradually applied in structural monitoring [37,38].…”

Section: Introductionmentioning

confidence: 99%

A Novel Dense Full-Field Displacement Monitoring Method Based on Image Sequences and Optical Flow Algorithm

et al. 2020

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This paper aims to achieve a large bridge structural health monitoring (SHM) efficiently, economically, credibly, and holographically through noncontact remote sensing (NRS). For these purposes, the author proposes a NRS method for collecting the holographic geometric deformation of test bridge, using static image sequences. Specifically, a uniaxial automatic cruise acquisition device was designed to collect static images on bridge elevation under different damage conditions. Considering the strong spatiotemporal correlations of the sequence data, the relationships between six fixed fields of view were identified through the SIFT algorithm. On this basis, the deformation of the bridge structure was obtained by tracking a virtual target using the optical flow algorithm. Finally, the global holographic deformation of the test bridge was derived. The research results show that: The output data of our NRS method are basically consistent with the finite-element prediction (maximum error: 11.11%) and dial gauge measurement (maximum error: 12.12%); the NRS method is highly sensitive to the actual deformation of the bridge structure under different damage conditions, and can capture the deformation in a continuous and accurate manner. The research findings lay a solid basis for structure state interpretation and intelligent damage identification.

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“…Computer vision plays a very active role in satellite remote sensing for change detection, pattern recognition, feature extraction, etc; of an area or a scene [18]. Edge detection is one of the important tasks in computer vision which extracts most important visual clues from the image, which is significant in understanding and interpreting the image features.…”

Section: Introductionmentioning

confidence: 99%

Study of Change Detection based on Edge Detection of Satellite Images

Singh¹

2017

International Journal of Applied Engineering Research

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This paper focuses on a robust and flexible edge detection technique based on Independent Component Analysis (ICA) on satellite images for recognition and subjective analysis of changes caused due to Japan earthquake 2011 followed by a devastating tsunami. ICA has been applied to satellite image patches to learn the basis functions using the fixed-point FastICA algorithm. As most of the basis functions are sparse, they are used as pattern template for feature extraction. These basis functions are usually localized, band-limited and oriented like Human Visual System (HVS) and resemble as Gabor wavelet basis function. In the proposed edge detection technique GeoEye's IKONOS satellite images corresponding to pre and post events have been first transformed to pattern maps (feature map) in which edges and background pixels have been classified into different classes. The edges have been extracted by using sparse components only, whereas non sparse components have been suppressed and treated as background.

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Analysis of Satellite Images for the Extraction of Structural Features

Cited by 8 publications

References 6 publications

Improved Method for Estimating Construction Year of Road Bridges by Analyzing Landsat Normalized Difference Water Index 2

Improved Method for Estimating Construction Year of Road Bridges by Analyzing Landsat Normalized Difference Water Index 2

A Novel Dense Full-Field Displacement Monitoring Method Based on Image Sequences and Optical Flow Algorithm

Study of Change Detection based on Edge Detection of Satellite Images

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