A Multitemporal Profile-Based Interpolation Method for Gap Filling Nonstationary Data
| Autor: | Conrad D. Heatwole, Lonesome Malambo |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Spectral index
010504 meteorology & atmospheric sciences Pixel 0211 other engineering and technologies 02 engineering and technology Land cover Missing data 01 natural sciences Scan line General Earth and Planetary Sciences Electrical and Electronic Engineering Scale (map) Algorithm Change detection 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Mathematics Interpolation |
| Zdroj: | IEEE Transactions on Geoscience and Remote Sensing. 54:252-261 |
| ISSN: | 1558-0644 0196-2892 |
| DOI: | 10.1109/tgrs.2015.2453955 |
| Popis: | Missing data in Landsat imagery caused by cloud cover and the scan line corrector error confound analysis, and effective methods for gap filling would greatly improve the usability of these data. Several methods for gap filling Landsat data have been proposed, but they typically assume little change in the images being corrected, which is a weakness in change detection applications. We present a profile-based $k$ -nearest-neighbor method for estimating missing spectral index data that provides accurate gap filling under both gradual and abrupt changes. The profile-based interpolator (PBI) uses a multitemporal image sequence and exploits similarities in land cover temporal profiles and spatial relationships to reliably estimate missing data. PBI uses both complete and incomplete pixel profiles to maximize information for estimation and aligns pixel profiles to the target pixel profile to minimize scale biases. For testing, we used eight-image mid-infrared bispectral index sequences from 2009 and 2014 for a study area in eastern Zambia. For ten stratified random samples $(n=\mathbf{1200})$ , we simulated one to six missing values per pixel profile and estimated the missing values at various $k$ values (5–29). Comparison of estimated with original values found results to be highly correlated ( $R^2$ of 0.95–0.72 for one to six missing values, respectively) and precise (mean absolute percent errors of 4%–8%). PBI provided comparable performance to a dictionary learning image recovery method with much better computational efficiency. PBI provides spatially and temporally consistent outputs, preserving abrupt changes in the imagery due to landscape fires even with large data gaps. |
| Databáze: | OpenAIRE |
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