SIFT optimization and automation for matching images from multiple temporal sources
| Autor: | Sebastián Castillo-Carrión, J.E. Guerrero-Ginel |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Global and Planetary Change
Matching (graph theory) business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Orthophoto Scale-invariant feature transform Feature transformation Pattern recognition 02 engineering and technology Management Monitoring Policy and Law Scale invariance Automation 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing Computer vision Artificial intelligence Computers in Earth Sciences business Focus (optics) 021101 geological & geomatics engineering Earth-Surface Processes Mathematics |
| Zdroj: | International Journal of Applied Earth Observation and Geoinformation. 57:113-122 |
| ISSN: | 1569-8432 |
| Popis: | Scale Invariant Feature Transformation (SIFT) was applied to extract tie-points from multiple source images. Although SIFT is reported to perform reliably under widely different radiometric and geometric conditions, using the default input parameters resulted in too few points being found. We found that the best solution was to focus on large features as these are more robust and not prone to scene changes over time, which constitutes a first approach to the automation of processes using mapping applications such as geometric correction, creation of orthophotos and 3D models generation. The optimization of five key SIFT parameters is proposed as a way of increasing the number of correct matches; the performance of SIFT is explored in different images and parameter values, finding optimization values which are corroborated using different validation imagery. The results show that the optimization model improves the performance of SIFT in correlating multitemporal images captured from different sources. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect