研究目的
To develop a method for assessing the geometric accuracy of Landsat and other RS images covering areas of offshore oil/gas development, to build a global offshore platform inventory to address the current GCP deficiency, and to present a quantitative description of the geometric performance of Landsat images by the derived offshore platform inventory.
研究成果
The study establishes a global inventory of 16,131 offshore platforms and develops a novel method for assessing geometric accuracy of RS images over oceans. Landsat OLI images show optimal performance, while TM/ETM+ images have larger errors. The inventory and method can improve GCP libraries and support marine applications, though limitations exist in platform availability and image conditions.
研究不足
The method is limited to areas with sufficient offshore platforms; low successful assessment ratio due to insufficient reference points or cloud cover; not applicable to areas without platforms; potential biases from resampling effects and platform elevation in SAR images; and only partial coverage for some sensors due to data volume and cost constraints.
1:Experimental Design and Method Selection:
A top-down data framework incorporating macro/meso/micro scales of RS images was designed to efficiently detect and verify global offshore platforms. The method uses the position-invariant characteristic of offshore platforms and the coherent characteristic of geometric shift among tie-points. Steps include extracting platforms from time-series OLI images, determining positions with median filtering, assessing coherence in images, and calculating systematic geometric shift using mean shift algorithm.
2:Sample Selection and Data Sources:
Datasets include low-resolution fire products (VIIRS Night-fire, VIIRS active fire, MODIS MCD14ML), Landsat pre-collection and collection-1 images (TM, ETM+, OLI), and other moderate/high resolution images (JERS-1 SAR, RADARSAT-1 SAR, ALOS-1 PALSAR, ENVISAT ASAR, Sentinel-1 SAR, Sentinel-2 MSI, NAIP aerial images, Chinese GF-1/ZY-3 images).
3:List of Experimental Equipment and Materials:
Various satellite sensors and products as listed in Table 1, including Landsat series, MODIS, VIIRS, SAR sensors, and high-resolution imagers.
4:Experimental Procedures and Operational Workflow:
Automated detection of offshore platforms from time-series OLI Band 6 images using Order Statistic Filtering thresholding, accumulation of binary images, and median filtering for position determination. For geometric accuracy assessment, candidate-sensed-points are detected, offsets calculated, and 2D histograms of shift distances analyzed to find aggregation centers using mean shift algorithm.
5:Data Analysis Methods:
Statistical analysis of geometric shifts (mean, standard deviation, percentiles), visual inspection of histograms, and correlation analysis for successful assessment ratios.
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Landsat-8 OLI
Operational Land Imager
NASA/USGS
Acquiring medium-resolution remote sensing images for detecting offshore platforms and assessing geometric accuracy.
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VIIRS
Visible Infrared Imaging Radiometer Suite
NOAA/NASA
Providing Night-fire and active fire products for detecting offshore gas flaring and guiding image selection.
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MODIS
Moderate Resolution Imaging Spectroradiometer
NASA
Providing active fire products for detecting thermal anomalies from offshore platforms.
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ENVISAT ASAR
Advanced Synthetic Aperture Radar
ESA
Acquiring SAR images for cross-verification and geometric accuracy assessment.
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ALOS-1 PALSAR
Phased Array type L-band Synthetic Aperture Radar
JAXA
Acquiring SAR images for cross-verification and geometric accuracy assessment.
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Sentinel-1 SAR
Synthetic Aperture Radar
ESA
Acquiring SAR images for geometric accuracy assessment.
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Sentinel-2 MSI
Multi-Spectral Instrument
ESA
Acquiring optical images for geometric accuracy assessment.
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NAIP
National Agriculture Imagery Program
USDA
Providing high-resolution aerial images for validation and geometric accuracy assessment.
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GF-1
GaoFen-1
CRESDA
Acquiring high-resolution images for cross-verification.
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ZY-3
ZiYuan-3
CRESDA
Acquiring high-resolution images for cross-verification.
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