Fast coastal algorithm for automatic geometric correction of AVHRR images

Parada, M.; Millan, A.; Lobato, Antonio; Hermosilla, Augusto Y.

doi:10.1080/01431160050029585

Cited by 14 publications

(4 citation statements)

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“…In recent years great efforts have been made to obtain automatic registration approaches and techniques. Some approaches carry out automatic registration using linear features extracted from NOAA AVHRR image, such as region boundary, contour line and coastline [1] [6][7][8][9]. Most linear features are the boundary of water bodies (sea, lakes, etc.)…”

Section: Introductionmentioning

confidence: 99%

A Feature-Based Approach for Automatic Registration of NOAA AVHRR Images

Lou

Huang²,

Fu³

et al. 2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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In this paper, a feature-based approach for automatic registration of NOAA AVHRR images is presented. The proposed approach makes use of the correlations between regions of the reference and sensed images both in feature and image spaces. The approach is summarized as follows: i) selection of potential control points in the reference image, automatically. Distinctive feature points with stronger edge energy than most of other pixels in reference image are selected as potential control points. ii) Finding for corresponding control points in the sensed image. The positions of corresponding control points in the sensed image are computed by means of feature matching using region similarity measures. iii) Mapping function estimation and transformation of the sensed image. Experimental results demonstrate that the proposed approach is capable of registering NOAA AVHRR images with high accuracy.

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Section: Introductionmentioning

confidence: 99%

A Feature-Based Approach for Automatic Registration of NOAA AVHRR Images

Lou

Huang²,

Fu³

et al. 2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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“…A second automatic geometric correction technique taking a set of 97 ground control points (GCPs; Fig. 1b) (Ho and Asem 1986;Brush 1988;Bachmann and Bendix 1992;Moreno and Meliá 1994;Parada et al 2000) along the coastline of southwestern Europe and the northern African continent was also applied to improve the registration of the output data.…”

Section: A Avhrr and Preprocessing Datamentioning

confidence: 99%

Sea-Breeze Convergence Zones from AVHRR over the Iberian Mediterranean Area and the Isle of Mallorca, Spain

Azorín-Molina

Connell

Baena-Calatrava

2009

Journal of Applied Meteorology and Climatology

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The aim of this study was to identify clear air boundaries and to obtain spatial distribution of convective areas associated with the sea breeze over the Iberian Mediterranean zone and the isle of Mallorca, both in Spain. Daytime Advanced Very High Resolution Radiometer (AVHRR) data from National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites were collected for May-October 2004. A cloud detection algorithm was used to identify clouds to derive daytime sea-breeze cloud frequency composites over land. The high-resolution composites aided in identifying the location of five preferential seabreeze convergence zones (SBCZ) in relation to the shape of coastline and orographic effects. Additionally, eight regimes were designated using mean boundary layer wind speed and direction to provide statistics about the effect of prevailing large-scale flows on sea-breeze convection over the five SBCZ. The offshore SW to W and the NW to N regimes were characterized by high cloud frequencies parallel to the coast. Small differences in mean cloud frequency values from morning to afternoon composites were detected with these regimes because sea-breeze fronts tended to form early and persist into the afternoon. Just the opposite occurred under the onshore NE to E and SE to S regimes. It was found that light to moderate (#5.1 m s 21 ) winds aloft result in more clouds at the leading edge of sea breezes. In contrast, strong synoptic-scale (.5.1 m s 21 ) flows weaken boundary layer convergence. The results from this satellite meteorology study could have practical applications for many people including those that forecast the weather and those that use the forecast for making decisions related to energy use, fishing, recreation, or agriculture activities, as well as for estimating pollution or issuing warnings for heavy rain or flash flooding.

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“…2017, 9, 303 4 of 17 [11,12]. The second possibility is to apply the compromised TLEs that already come with the raw data and then try to correct the resulting pixel shifts with hindsight as it was applied to the Canadian AVHRR Processing System by [16].…”

Section: Methodsmentioning

confidence: 99%

“…This problem is well known and has been addressed to by several researchers during the decades. Different solutions exist to overcome the imprecise geolocation, both approaching the sensor model itself [11,12] or correcting the already distorted data [13] (more information about these techniques will be presented in Section 3).…”

Section: Introductionmentioning

confidence: 99%

Automated Improvement of Geolocation Accuracy in AVHRR Data Using a Two-Step Chip Matching Approach—A Part of the TIMELINE Preprocessor

et al. 2017

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Abstract:The geolocation of Advanced Very High Resolution Radiometer (AVHRR) data is known to be imprecise due to minor satellite position and orbit uncertainties. These uncertainties lead to distortions once the data are projected based on the provided orbit parameters. This can cause geolocation errors of up to 10 km per pixel which is an obstacle for applications such as time series analysis, compositing/mosaicking of images, or the combination with other satellite data. Therefore, a fusion of two techniques to match the data in orbit projection has been developed to overcome this limitation, even without the precise knowledge of the orbit parameters. Both techniques attempt to find the best match between small image chips taken from a reference water mask in the first, and from a median Normalized Difference Vegetation Index (NDVI) mask in the second round. This match is determined shifting around the small image chips until the best correlation between reference and satellite data source is found for each respective image part. Only if both attempts result in the same shift in any direction, the position in the orbit is included in a third order polynomial warping process that will ultimately improve the geolocation accuracy of the AVHRR data. The warping updates the latitude and longitude layers and the contents of the data layers itself remain untouched. As such, original sensor measurements are preserved. An included automated quality assessment generates a quality layer that informs about the reliability of the matching.

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Fast coastal algorithm for automatic geometric correction of AVHRR images

Cited by 14 publications

References 0 publications

A Feature-Based Approach for Automatic Registration of NOAA AVHRR Images

A Feature-Based Approach for Automatic Registration of NOAA AVHRR Images

Sea-Breeze Convergence Zones from AVHRR over the Iberian Mediterranean Area and the Isle of Mallorca, Spain

Automated Improvement of Geolocation Accuracy in AVHRR Data Using a Two-Step Chip Matching Approach—A Part of the TIMELINE Preprocessor

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