Landsat TM Satellite Image Restoration Using Kalman Filters

Arbel, Dan; Cohen, E.; Citroen, Meira; Blumberg, Dan G.; Kopeika, Norman S.

doi:10.14358/pers.70.1.91

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…KF is a recursive inference algorithm that integrates observations, models and their respective uncertainties to estimate the state of a process minimizing the mean of the squared errors [53,54]. Kalman filter algorithms have been applied to correct atmospheric effects in satellite images [55], image fusion of multisource images [56][57][58][59], integrate remote sensing data and ecosystem models in a number of studies to retrieve soil temperature [60], surface BRDF parameters [61], soil moisture [62], change detection [63], ecosystem productivity [64] or monitoring crop phenology [65].…”

Section: Methodsmentioning

confidence: 99%

A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images

2014

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A data assimilation method to produce complete temporal sequences of synthetic medium-resolution images is presented. The method implements a Kalman filter recursive algorithm that integrates medium and moderate resolution imagery. To demonstrate the approach, time series of 30-m spatial resolution NDVI images at 16-day time steps were generated using Landsat NDVI images and MODIS NDVI products at four sites with different ecosystems and land cover-land use dynamics. The results show that the time series of synthetic NDVI images captured seasonal land surface dynamics and maintained the spatial structure of the landscape at higher spatial resolution. The time series of synthetic medium-resolution NDVI images were validated within a Monte Carlo simulation framework. Normalized residuals decreased as the number of available observations increased, ranging from 0.2 to below 0.1. Residuals were also significantly lower for time series of synthetic NDVI images generated at combined recursion (smoothing) than individually at forward and backward recursions (filtering). Conversely, the uncertainties of the synthetic images also decreased when the number of available observations increased and combined recursions were implemented.

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Section: Methodsmentioning

confidence: 99%

A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images

2014

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“…The RSI has blue and green bands, as indicated in table 1, but their spatial resolution would be too low. In a panchromatic image with higher spatial resolution, the detection could be likely hampered by a contrast decrease through (1) a wide spectral range covering the infrared region, where desert dust and vegetation show higher reflectance, (2) residual errors in subpixel image registration (Townshend et al 1992;Verbyla & Boles 2000), (3) the instrument MTF (Lee et al 2011), and (4) atmospheric blurring (Arbel et al 2004). If we can make an accurate measurement of the parachute's absolute reflectance in the laboratory, it would be of great help to distinguish the real radiance change from the false detections in figure 6.…”

Section: Resultsmentioning

confidence: 99%

Experimental Search for the HAYABUSA Re-Entry Module on the Ground by Using a Remote-Sensing Instrument onboard Formosat-2 Satellite

Nakamura¹,

Chen²,

Matsuoka³

et al. 2011

Publications of the Astronomical Society of Japan

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HAYABUSA was the first asteroid sample return mission launched from Uchinoura, Japan in 2003. The sample capsule re-entered the Earth's atmosphere at approximately 13:50 (UT) on 2010 June 13th, and safely landed on the Woomera Prohibited Area (WPA) in Australia. The capsule had to be found and retrieved as soon as possible to reduce any contamination risk. On the morning of the June 13th and 14th, the landing area was imaged by using Remote Sensing Instrument (RSI) onboard the Formosat-2 satellite to locate the HAYABUSA re-entry module, consisting of a capsule and a parachute. In spite of an accurate geometric registration and radiance comparison of the image pair, no statistically significant radiance variation was found on the landing site. It was demonstrated through this experiment, however, that satellite imagery could be a promising backup tool for the prompt discovery and retrieval of the sampling capsule of the HAYABUSA follow-on mission if we can utilize space-borne imagers with sub-meter spatial resolution and short revisit capability.

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“…To restore remote sensing images, the MTF (Modulation Transfer Function) based methods are most commonly used. Some representative references address the deconvolution problems of TM (Arbel et al, 2004), SPOT (Pinilla Ruiz and Ariza Lopez, 2002), IKONOS (Ryan et al, 2003) and CBERS-2 (Papa et al, 2008). The Wiener filter (Hillery and Chin, 1991) is a widely employed method to restore the image after the estimation of MTF.…”

Section: Introductionmentioning

confidence: 99%

Universal reconstruction method for radiometric quality improvement of remote sensing images

Shen

Liu

et al. 2010

International Journal of Applied Earth Observation and Geoinfor

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Landsat TM Satellite Image Restoration Using Kalman Filters

Cited by 9 publications

References 15 publications

A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images

A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images

Experimental Search for the HAYABUSA Re-Entry Module on the Ground by Using a Remote-Sensing Instrument onboard Formosat-2 Satellite

Universal reconstruction method for radiometric quality improvement of remote sensing images

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