Removing Automatically the Ambiguity in Wind Direction Retrieved

from SAR Images

Maria da Conceição Proença

Department of Physics, Marine and Environmental Sciences Centre (MARE-ULisboa), Faculty of Sciences,

University of Lisbon, Campo Grande, 1749-016 Lisboa, Portugal

Keywords: Wind Direction, Wind Shadows, Direction Ambiguity, SAR Images, Image Processing.

Abstract: The evaluation of the wind resource in large areas to study the viability of wind farms is ideally studied using

synthetic aperture radar (SAR) images in which the direction of the wind can be mapped from its effects on

the water surface. Methods in use usually assume a fixed direction from a measurement for the whole image

or interpolate the direction of wind fields from numerical weather models, that can be non-coincident in time

with the SAR snapshot and of much less spatial resolution. The problem remains in the directional ambiguity

of 180 degrees. This work presents three indexes to identify and validate initial “anchor vectors” that could

be used as an aid in the complex process of remove this ambiguity, using wind shadows in the water near the

coastline. These indexes consider several hypotheses to provide for local variability such as physiographic

accidents, the eccentricity of the shadows and the effect of bay-shaped areas, all quantified through image

processing methods. Comparing the results with the reference wind field provided by ESA for the time of

acquisition of the ENVISAT-ASAR image used we could conclude that this is a promising line of work.

1 INTRODUCTION

The ambiguity in wind direction retrieval is a key

problem to which there exists a very recent solution

(

Zhang, 2021) using support vector machine (SVM)

based models, with performance still depending on

sea surface wind speed. The issue of ambiguity has

been addressed from time to time, although wind

direction remains the most appealing problem since

the 1980s (Heron, 1986), (Hildebrand, 1994); later,

(Kerkmann, 1998) mentioned four different methods

for removing the direction ambiguity, all involving a

human operator or a trained meteorologist, one of

them autonomous in the sense that no external data is

needed. In the 2000s two main methods were being

used to wind retrieval – those based on gradient-

oriented histogram (Koch, 2004), and wavelets based

(Du, 2002), (Fichaux, 2002), followed by

improvements from the latter as in (Corazza, 2020),

who use the Radon transform. Some adaptation of

successful methods also took place, like (Horstmann,

2004) who adapts the CMOD4, originally developed

for ERS-1 and 2 to ENVISAT-ASAR images with

success, while (Kerbaol, 2005) uses coastal

information. (Young, 2006) concludes that automatic

and semi-automatic extraction of wind direction are

complimentary and ensure a higher liability in wind

direction retrieval from SAR images. (Koch, 2004) in

the same paper mentioned above uses a

semiautomatic removal of the ambiguity by

combining manual selecting of unique directions on a

set of subimages and automatically choosing the best

aligned directions in the remaining subimages, while

(Song, 2006) uses buoy data to solve the ambiguity in

a comparation of two algorithms for wind speed.

The ambiguity in the direction retrieval was not an

appealing subject for automation, but still seems

possible to implement, at least in areas near the coast.

The image processing methodology exposed here

allowed the identification of anchor vectors near the

shoreline that could act together with global methods

to ensure the wind direction ambiguity is

automatically assessed in the whole wind field, which

could be useful in preliminary studies for offshore

wind farms settings.

2 MATERIALS AND

METHODOLOGY

The image used is a medium resolution synthetic

aperture radar image that was acquired by Envisat

Proença, M.

Removing Automatically the Ambiguity in Wind Direction Retrieved from SAR Images.

DOI: 10.5220/0010934000003209

In Proceedings of the 2nd International Conference on Image Processing and Vision Engineering (IMPROVE 2022), pages 93-100

ISBN: 978-989-758-563-0; ISSN: 2795-4943

ASAR – Wide Swath Mode (WSM) instrument, with

a nominal resolution of 150x150 m (range x azimuth),

a pixel spacing of 75 m and covering 400X400 km

(https://earth.esa.int) acquired over Corsica at 2007-

11-13 (Figure 1-a).

To make the successive processing steps of the

methodology more perceptible, we will be using the

sub-image identified in red (Figure 1-b) in the

ENVISAT image whenever we consider more useful

that the detail is observed to illustrate the reasoning.

Figure 1: SAR image acquired by ENVISAT mission over

Corsica (a) and zoom on the area which will be used to

illustrate the image processing operations (b).

The first step involves calibration and

computation of a land mask, and it was achieved with

ESA open-source software Next ESA SAR

Toolbox (NEST). A land mask is a binary image to

discriminate between land and water, with two values

usually 0 and 1), where we can attribute the value 1

to the subject of interest to be altered in subsequent

morphological operations until we have the suitable

mask to apply to the work image.

From the land mask obtained (Figure 2), a

sequence of morphological and logical operations is

needed to obtain a “ribbon mask”. The procedure is

schematically detailed in Figure 3.

Figure 2: Land mask obtained using NEST: binary image

where the land is represented with the value 1 (white) and

the sea area has the value 0 (black).

Using the initial land mask here represented by a

white triangle in a black background (Figure 3-a), two

binary images are computed: the first one by dilation,

a morphological operation that enlarge the areas with

value 1 presented in white (Pratt, 2001) to obtain a

new mask (Figure 3-b), and the second one by

inversion: the value 1 in the initial image becomes 0

and vice-versa (Figure 3-c).

Figure 3: Schematic representation of the sequence of

operations to obtain a “ribbon” mask: (a) initial land mask,

(b) dilation of (a), (c) inversion or negation of (a) and (d)

logical AND between (b) and (c) – only the areas where

both masks have value 1 will receive a positive value.

IMPROVE 2022 - 2nd International Conference on Image Processing and Vision Engineering

The structural element used for dilation in the

ENVISAT image was a disk of radius 25 pixel,

applied successively the number of times needed to

encompass all the area containing shadows – to

automatize this step, a maximum width for the mask

should be assessed from a bigger dataset of images of

the same sensor.

When those masks (Figure 3-b and c) are

combined by a logical AND operation, the result is a

“ribbon mask” (Figure 3-d).

With the new mask a corridor near the coastline

can be isolated (Figure 4-a), where the wind shadows

are now visible as dark spots near the shoreline

(Figure 4-b).

Figure 4: The “ribbon” mask is applied to the original image

(a), isolating the area of water near the shoreline, where

wind shadows are apparent (b).

Next step is to isolate the shadows as individual

objects, which is made by thresholding the image

(Figure 5-a), giving the value 1 to the pixels that are

between the thresholds 0 (corresponding to the area

masked) and an appropriate radiometric level, that

will depend on the image codification – pixels in 16

bits images are in the range [0, 65 535], while in 8 bits

images only 256 levels are possible, in the range [0,

255]. The threshold is computed using the subset of

pixels belonging to the corridor and assigned to the

average less two standard deviations of the intensities

present.

Once the threshold is applied to the image, the

resulting binary image is ready for the morphological

operations needed to consolidate each shadow, that

will consist in a sequence of dilation followed by

erosion with the same structuring element, usually

called closing (Figure 5-b), achieved with a smaller

structural element to preserve the form - here a disk

of radius 11.

Figure 5: Candidates for wind shadows isolated by intensity

thresholding (a) and consolidated using morphological

operations (b).

After shadow localization, we looked for the

digital elevation model (from SRTM, characterized

below) to analyse each shadow and its immediate

neighbourhood on land, to determine its credibility as

a wind shadow (Figure 6). Three validation criteria

are proposed: a bay factor, an abrupt cliff analysis and

the shadow eccentricity, detailed and evaluated in the

next section.

Removing Automatically the Ambiguity in Wind Direction Retrieved from SAR Images

Figure 6: The candidates to wind shadows in different

colours and the digital elevation model on land side.

3 VALIDATION CRITERIA

The criteria proposed for validation of the wind

shadows as such are not exhaustive but worked in the

range of conditions present in the image and can be

applied to any similar coastline, as the three are based

in common physiographic and natural effects.

3.1 Bay Factor

The rationale for the Bay factor sits in the fact that an

open bay will not provoke a wind shadow, while a

more closed bay will usually induce an area of

shadow in the near water.

This was transformed in a quantitative index

using the quotient between the number of pixels that

constitute the bay envelope and the number of pixels

belonging to the shadow envelope, schematically

identified in Figure 7.

Figure 7: Definition of the pixels forming the bay envelope

in pink in the land side, and the pixels belonging to the

shadow envelope, in green, in turn of the blue shadow over

the water.

The Bay factor computed this way (eq. 1) will be

bigger for a closed bay, and low for an open bay.

Bay factor =

∑

,

, ∈ 

∑

,

, ∈ 

(1)

Examples of the values obtained for different

forms of bays with this definition and the shadows

previously processed are shown in Figure 8.

Figure 8: Examples of shadows and bays present in the

image. Land is white and water is grey, and the bay and

shadow envelopes follow the colour code in Figure 7. The

values for the bay factor are 0.09 (a), 0.28 (b), 0.50 (c) and

0.79 (d).

A very closed bay such as the one in Figure 8-c

will have a high Bay factor, but this configuration

probably is enough cause for a calm water, observed

as shadow in a SAR image, so shadows scoring high

Bay factors will not be considered wind shadows.

IMPROVE 2022 - 2nd International Conference on Image Processing and Vision Engineering

3.2 Abrupt Cliff Index

The altimetry came from the digital elevation data

(DEM) obtained by the Shuttle Radar Topography

Mission (SRTM), an international project

spearheaded by the U.S. National Geospatial-

Intelligence Agency (NGA) and the U.S. National

Aeronautics and Space Administration (NASA). The

project covered more than 80% of the Earth’s solid

surface during a 11-day mission of the Space Shuttle

Endeavour in February 2000. The SRTM data is

available as 3 arc second (approx. 90 m ground

resolution) and has a vertical error reported to be less

than 16 m (https://www.usgs.gov).

This digital elevation model was used to compute

the local gradient near each shadow. A flat area will

have a low local gradient, and a steepest area will

have a higher value (Figure 9).

Figure 9: The flat area on the left will have a low value for

the local gradient while the abrupt cliffs on the right will

have a high gradient.

The cliff index is computed considering the local

elevation from the DEM (Figure 10-a), its gradient

(Figure 10-b), and the absolute value of this gradient

(Figure 10-c). The roughness of the terrain becomes

more apparent with these operations.

The ACliff index is the sum of the pixels

belonging to the seashore shadow track (sst) in the

image containing the absolute value of the gradient of

the elevation (eq. 2).

ACliff =

∑

absgradelevation ))

,∈ 

(2)

Figure 10: The sequence of images needed to compute the

Abrupt Cliff index: the local elevation from the DEM (a),

the gradient of the local elevation (b) and its absolute value

(c).

As the terrain becomes steeper, the abrupt cliff

index increases. When a shadow is near a flat area

(Figure 11-a), the cliff index will be low, and will

increase as the terrain roughness increases.

Removing Automatically the Ambiguity in Wind Direction Retrieved from SAR Images

Figure 11: Examples of three shadows (red) near terrain

with different characteristic – the CliffIndex is 1.1 for flat

terrain (a), 4.8 for the shadow near median elevation (b),

and 11.4 for the shadow near abrupt cliffs (c).

3.3 Shadow Eccentricity

The last indicator we consider for the localization of

wind shadows is the eccentricity, computed as the

eccentricity of the ellipsoid enveloping the shadow,

as demonstrated in Figure 12.

Figure 12: Three shadows with different eccentricity values

associated: a) 0.49, b) 0.69 and c) 0.93.

IMPROVE 2022 - 2nd International Conference on Image Processing and Vision Engineering

The eccentricity of the ellipsoid is computed as

the square root of the difference between the squared

values of the lengths of the semi-major axis (a) and

semi-minor axis (b) divided by the first (eq. 3).

Eccentricity =







 





(3)

The information needed is the direction of the

anchor vectors, that will be established from the

centre of the shadows accepted (end point for the

vector) and the nearest point in the coastline,

considering the extension in contact with the ellipsoid

enveloping the shadow (initial point for the vector).

The magnitude of these vectors will be dependent of

the wind field local intensity, and is usually computed

automatically (Rufenach, 1998) since late 90’s.

Figure 13: The seven anchor vectors built from the wind

shadows in the SAR image (a) and the reference wind field

for that date (b).

With these tree indicators, we build a criterion to

accept/reject each candidate shadow as a trustful wind

shadow, with a rationale including a high Cliff index

to identify abrupt cliffs in the proximity of each

shadow that can be the leading cause of the wind

shadow, a low Bay factor to eliminate shadows in

almost enclosed bay areas, and the eccentricity of the

ellipses to refine admissible shadows and find the

line-of-sight in the direction of the shadow to give an

orientation for the anchor vector.

All these criteria and previous location of areas of

interest can be automatically implemented in a single

procedure, avoiding external data and human curation

with inherent subjectivity. To do so, the estimation of

the areas containing shadows can be done with a fixed

maximum width for the ribbon mask.

From the 28 shadow candidates, only 7 verify the

criteria (Figure 13-a).

Considering the positioning of the seven vectors

obtained from the wind shadows and comparing with

the wind vectors in approximately the same positions

in the reference wind field provided by ESA for that

date (Figure 13-b), we can see the orientation of the

seven vectors agree in a reasonable extend with the

local orientation of the wind field.

4 CONCLUSIONS

Wind field monitoring is especially important in the

preliminary phase to select among the best locations

for wind farms and becomes more difficult when

offshore wind farms are the goal.

This case study intended to show that SAR images

allow to directly extract sets of vectors near the

coastline that could be used to unwrap the wind

direction ambiguity in large areas automatically,

complementing the wind direction retrieval that is

already automatized, with a reasonable confidence.

With this kind of procedure, all the operations for

wind retrieval offshore could be completed without

the need of in-situ data (buoy or other external data),

directly from the remote sensed images.

ACKNOWLEDGEMENTS

Envisat-ASAR image and reference wind field were

both courtesy of Alexis Mouche, with CLS at the

time.

Removing Automatically the Ambiguity in Wind Direction Retrieved from SAR Images

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