Phase correlation-based illumination-insensitive image matching for terrain-related applications Article Swipe
The Earth surface is monitored by air-borne and space-borne sensors locally, regionally and globally and at short time intervals. The capability of multi-temporal image matching is essential for many terrain-related applications, such as glacier motion monitoring, landslide localisation and earthquake deformation assessment. Robust image matching using time-varying remotely sensed data is, however, not always achievable because images are acquired separately, under different illumination conditions. For remotely sensed images of natural landscapes, such as mountains, deserts and glaciers, the majority of image patterns is produced by topographic features, such as shading and shadows. The appearances of these image patterns can be greatly altered by illumination variation, especially local illumination variation caused by solar position change, and thus parts of the images may lose correspondence for accurate matching. This research provides a thorough mathematical analysis of the impact of local illumination variation via Phase Correlation (PC) cross power spectrum in the Fourier frequency domain and thus proves the illumination-insensitive property of PC-based image matching algorithms. Specifically, the relationship between PC fringe appearance and sun angle variation is investigated. Two improved PC based approaches, ADCF-PC (Absolute Dirichlet Curve Fitting based Phase Correlation) and PLSF-PC (Piecewise Least Square Fitting based Phase Correlation), are presented and compared for illumination-insensitive image matching. Finally, three original approaches to explore terrain related applications have been developed including: vision-based UAV navigation, illumination-insensitive change detection and DEM generation from multi-temporal remotely sensed images.
Related Topics
Concepts
Terrain
Phase correlation
Matching (statistics)
Digital image correlation
Computer vision
Image (mathematics)
Artificial intelligence
Computer science
Image matching
Geography
Geology
Remote sensing
Cartography
Computer graphics (images)
Mathematics
Optics
Physics
Statistics
Mathematical analysis
Short-time Fourier transform
Fourier analysis
Fourier transform
Metadata
- Type
- dissertation
- Language
- en
- Landing Page
- http://hdl.handle.net/10044/1/28965
- http://hdl.handle.net/10044/1/28965
- OA Status
- green
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2900358897
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2900358897Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.25560/28965Digital Object Identifier
- Title
-
Phase correlation-based illumination-insensitive image matching for terrain-related applicationsWork title
- Type
-
dissertationOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-10-01Full publication date if available
- Authors
-
Xue WanList of authors in order
- Landing page
-
https://hdl.handle.net/10044/1/28965Publisher landing page
- PDF URL
-
https://hdl.handle.net/10044/1/28965Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://hdl.handle.net/10044/1/28965Direct OA link when available
- Concepts
-
Terrain, Phase correlation, Matching (statistics), Digital image correlation, Computer vision, Image (mathematics), Artificial intelligence, Computer science, Image matching, Geography, Geology, Remote sensing, Cartography, Computer graphics (images), Mathematics, Optics, Physics, Statistics, Mathematical analysis, Short-time Fourier transform, Fourier analysis, Fourier transformTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.solar | 111 |
| abstract_inverted_index.these | 95 |
| abstract_inverted_index.three | 207 |
| abstract_inverted_index.under | 60 |
| abstract_inverted_index.using | 45 |
| abstract_inverted_index.Robust | 42 |
| abstract_inverted_index.Square | 193 |
| abstract_inverted_index.always | 53 |
| abstract_inverted_index.caused | 109 |
| abstract_inverted_index.change | 223 |
| abstract_inverted_index.domain | 151 |
| abstract_inverted_index.fringe | 168 |
| abstract_inverted_index.images | 56, 67, 119 |
| abstract_inverted_index.impact | 135 |
| abstract_inverted_index.motion | 34 |
| abstract_inverted_index.proves | 154 |
| abstract_inverted_index.sensed | 48, 66, 231 |
| abstract_inverted_index.ADCF-PC | 181 |
| abstract_inverted_index.Fitting | 185, 194 |
| abstract_inverted_index.Fourier | 149 |
| abstract_inverted_index.PLSF-PC | 190 |
| abstract_inverted_index.altered | 101 |
| abstract_inverted_index.because | 55 |
| abstract_inverted_index.between | 166 |
| abstract_inverted_index.change, | 113 |
| abstract_inverted_index.deserts | 74 |
| abstract_inverted_index.explore | 211 |
| abstract_inverted_index.glacier | 33 |
| abstract_inverted_index.greatly | 100 |
| abstract_inverted_index.images. | 232 |
| abstract_inverted_index.natural | 69 |
| abstract_inverted_index.related | 213 |
| abstract_inverted_index.sensors | 9 |
| abstract_inverted_index.shading | 89 |
| abstract_inverted_index.surface | 2 |
| abstract_inverted_index.terrain | 212 |
| abstract_inverted_index.Finally, | 206 |
| abstract_inverted_index.PC-based | 159 |
| abstract_inverted_index.accurate | 124 |
| abstract_inverted_index.acquired | 58 |
| abstract_inverted_index.analysis | 132 |
| abstract_inverted_index.compared | 201 |
| abstract_inverted_index.globally | 13 |
| abstract_inverted_index.however, | 51 |
| abstract_inverted_index.improved | 177 |
| abstract_inverted_index.locally, | 10 |
| abstract_inverted_index.majority | 78 |
| abstract_inverted_index.matching | 24, 44, 161 |
| abstract_inverted_index.original | 208 |
| abstract_inverted_index.patterns | 81, 97 |
| abstract_inverted_index.position | 112 |
| abstract_inverted_index.produced | 83 |
| abstract_inverted_index.property | 157 |
| abstract_inverted_index.provides | 128 |
| abstract_inverted_index.remotely | 47, 65, 230 |
| abstract_inverted_index.research | 127 |
| abstract_inverted_index.shadows. | 91 |
| abstract_inverted_index.spectrum | 146 |
| abstract_inverted_index.thorough | 130 |
| abstract_inverted_index.(Absolute | 182 |
| abstract_inverted_index.Dirichlet | 183 |
| abstract_inverted_index.air-borne | 6 |
| abstract_inverted_index.detection | 224 |
| abstract_inverted_index.developed | 217 |
| abstract_inverted_index.different | 61 |
| abstract_inverted_index.essential | 26 |
| abstract_inverted_index.features, | 86 |
| abstract_inverted_index.frequency | 150 |
| abstract_inverted_index.glaciers, | 76 |
| abstract_inverted_index.landslide | 36 |
| abstract_inverted_index.matching. | 125, 205 |
| abstract_inverted_index.monitored | 4 |
| abstract_inverted_index.presented | 199 |
| abstract_inverted_index.variation | 108, 139, 173 |
| abstract_inverted_index.(Piecewise | 191 |
| abstract_inverted_index.achievable | 54 |
| abstract_inverted_index.appearance | 169 |
| abstract_inverted_index.approaches | 209 |
| abstract_inverted_index.capability | 20 |
| abstract_inverted_index.earthquake | 39 |
| abstract_inverted_index.especially | 105 |
| abstract_inverted_index.generation | 227 |
| abstract_inverted_index.including: | 218 |
| abstract_inverted_index.intervals. | 18 |
| abstract_inverted_index.mountains, | 73 |
| abstract_inverted_index.regionally | 11 |
| abstract_inverted_index.variation, | 104 |
| abstract_inverted_index.Correlation | 142 |
| abstract_inverted_index.algorithms. | 162 |
| abstract_inverted_index.appearances | 93 |
| abstract_inverted_index.approaches, | 180 |
| abstract_inverted_index.assessment. | 41 |
| abstract_inverted_index.conditions. | 63 |
| abstract_inverted_index.deformation | 40 |
| abstract_inverted_index.landscapes, | 70 |
| abstract_inverted_index.monitoring, | 35 |
| abstract_inverted_index.navigation, | 221 |
| abstract_inverted_index.separately, | 59 |
| abstract_inverted_index.space-borne | 8 |
| abstract_inverted_index.topographic | 85 |
| abstract_inverted_index.Correlation) | 188 |
| abstract_inverted_index.applications | 214 |
| abstract_inverted_index.illumination | 62, 103, 107, 138 |
| abstract_inverted_index.localisation | 37 |
| abstract_inverted_index.mathematical | 131 |
| abstract_inverted_index.relationship | 165 |
| abstract_inverted_index.time-varying | 46 |
| abstract_inverted_index.vision-based | 219 |
| abstract_inverted_index.Correlation), | 197 |
| abstract_inverted_index.Specifically, | 163 |
| abstract_inverted_index.applications, | 30 |
| abstract_inverted_index.investigated. | 175 |
| abstract_inverted_index.correspondence | 122 |
| abstract_inverted_index.multi-temporal | 22, 229 |
| abstract_inverted_index.terrain-related | 29 |
| abstract_inverted_index.illumination-insensitive | 156, 203, 222 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5102796596 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 1 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.18194736 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |