remote sensing lab (rslab) dept. of signal theory & communications mariví tello, carlos...

Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications

Mariví TelloMariví Tello, Carlos López-Martínez, Jordi J. Mallorquí. , Carlos López-Martínez, Jordi J. Mallorquí.

Remote Sensing Laboratory (RSLab)Remote Sensing Laboratory (RSLab)Signal Theory and Telecommunications Department

Universitat Politècnica de CatalunyaBarcelona, SPAIN

Contributive Processing Methods Integrated Contributive Processing Methods Integrated in a Robust Tool for Ocean Monitoring from in a Robust Tool for Ocean Monitoring from

SAR ImagerySAR Imagery


IntroductionIntroduction

Global Monitoring for Environment and Security (GMES) plans a global framework for remote sensing applications from space.

The Marine Core Service has been identified by GMES as one of the 3 “fast track” services. Among its priorities:

- monitoring of fisheries (control of fishing activities, improvement of safety and efficiency in maritime transport, prosecution of responsibilities in illegal oil spills in the ocean…)- monitoring of the coastline (coastal management and planning, coastal flooding and erosion…)- monitoring of the effects of environmental hazards and pollution crisis


• unlike optical imagery, interpretation of radar images is not consistent with common visual perception

• most of the tools of image processing are conceived from an “optical” point of view

Some preliminary considerations (I)Some preliminary considerations (I)

Our purpose is to establish a specific framework for the automatic exploitation of

SAR imagery.

Due to speckle, a SAR image is one realization

of an underlying stochastic non-homogeneous

process.


0 0, , , , ,ii

u x r x r u x r x r u x r

The SAR image can be modelled as the convolution of the local complex reflectivity of the observed area with the impulse response of the SAR system.

Random sum of the contributions of all the scatterers within a resolution cell

(random walk process).

Analysis tools have to be inscribed in a statistical framework, but preserving

contextual information.

SAR images are spiky, with a large dynamic range and they involve non-stationary

processes.

A SAR image is one realization of an underlying stochastic non-stationary process.

Some preliminary considerations (II)Some preliminary considerations (II)


From a signal processing point of view, in 2D, the multiscale time – frequency analysis can be seen as the iterative application of a filter bank separately in each dimension.

Input image

after high pass filtering in the horizontal

dimension

after high pass filtering in the vertical

dimension

after low pass filtering in both

dimensions

Enhancement of discontinuities

Wavelet TransformWavelet Transform

LL

HL

LH

HH

Input

Input to next it.


The brain attaches high information content to vertices, linear structures and edges.

Inspired on the human vision system, our workplan to achieve a specific framework for the automatic interpretation of SAR imagery is:

• Spot detection, directly applied to ship detection.

• Extraction of linear features, directly applied to coastline extraction.

• Texture analysis, applied to oil spill detection.


Background noise Structure

Spatial correlation is low.Intuitively, pixels are usually not related to each other; the probability of one pixel to have a similar intensity that its neighbours is low.

Spatial correlation is high.Intuitively, pixels are usually related to each other; the probability of one pixel to have a similar intensity that its neighbours is high. OCWT

Horizontal passband

Vertical passband

Bidimensional lowpass

The probability of co-occurrence of local maxima is low.

The probability of co-occurrence of local maxima is high.

Zoom-in on details… Zoom-in on details…

Automatic Spot Detection (I)Automatic Spot Detection (I)


Input Image

ResultOCWT

11 2,JD z z

21 2,JD z z

1 2,JX z z

- low correlation in the background- local dependencies on the ship

Original RADARSAT image

Result *

* Dire

ct re

sult,

no

thre

shol

d ap

plie

d

Intermediate oriented subbands

Based on the previous hypothesis, the following algorithm is proposed:

Automatic Spot Detection (II)Automatic Spot Detection (II)


The proposed algorithm faces the detection not only taking exclusively into account the intensity characteristics of the image but also studying its very localized statistical behaviour.

* Dire

ct re

sult,

no

thre

shol

d ap

plie

d

target

OCWT XX

Input image Output image *- background noise reduced because the OCWT is sparse- discontinuities target – background enhanced in each direction separately

Situation not resolvable by a CFAR approach !

Hor

izon

tal p

rofil

eH

isto

gram

Hor

izon

tal p

rofil

e

Situation solved by the proposed algorithm !

His

togr

am

target

Region in which a threshold would provide a correct detection (target detected with no false alarms). As a consequence, larger coloured region represents a higher detectability rate.

Automatic Spot Detection (III)Automatic Spot Detection (III)


Automatic Spot Detection (IV)Automatic Spot Detection (IV)

Preservation of the spatial resolution

Detection less dependent on the intensity

Vessel to clutter contrast enhancement

- smaller vessels are not penalized

RADARSAT image, SGF mode, HH pol.

Inpu

t O

utpu

t *

ENIVISAT ASAR image, IM mode, VV pol.

In order to quantify the difficulty of performing a correct detection, a contrast parameter, the significance, is defined:

peak_of_the_target - background_meanSignificance

background_standard_deviation

- depends on the local correlation

6.3s

24.3s

ENVISAT ASAR image, IM mode, VV pol.


Some preliminary considerations (III)Some preliminary considerations (III)







Automatic extraction of linear features (I)Automatic extraction of linear features (I)

SWT- 1 it.

Max

Point wise Product

SWT- 2 it.

Max

Low passHorizontal bandpass

Vertical bandpass

Diagonal bandpass

Low passHorizontal bandpass

Vertical bandpass

Diagonal bandpass

…

Output image *

Input image

We propose a novel specific algorithm for the extraction of linear features on SAR imagery:

- low computational cost (few operations)- multiscale capability- completely unsupervised- no training, no a priori information to merge- no previous filtering (reduces resolution)

* Dire

ct re

sult

– no

tres

hold

app

lied


Automatic extraction of linear features (II)Automatic extraction of linear features (II)

Comparison of the performance of our algorithm with one of the Sobel filter:

ENVISAT image Sobel filter result Algorithm proposed *

- edges greatly enhanced- background noise noticeably reduced

* Dire

ct re

sult

– no

tres

hold

app

lied


Automatic extraction of linear features (III)Automatic extraction of linear features (III)

ENVISAT image Sobel filter result Proposed algorithm *

RADARSAT image

* Dire

ct re

sult

– no

tres

hold

app

lied


Automatic extraction of linear features (IV)Automatic extraction of linear features (IV)

After the edge enhancement

phase, the decision is

performed by means of a snake

algorithm.


Automatic extraction of linear features (IV)Automatic extraction of linear features (IV)

Rivers and inland waters. Oil spills.

ENVISAT image ENVISAT imageResult * Result *

* Dire

ct re

sult

– no

tres

hold

app

lied


Some preliminary considerations (III)Some preliminary considerations (III)







Estimating local roughnessEstimating local roughnessThe decay of the Wavelet Transform amplitude across scales is related to the uniform and pointwise Lipschitz regularity of the signal.

Projection of the pointwise evolution across scales (obtained from a WT) of a cut of a homogeneous sea area.

Homogeneous decay

Projection of the pointwise evolution across scales (obtained from a WT) of a cut intercepting an oil spill.

The Lipschitz or Hölder exponent at a point is the maximum slope of log2|Wf(u,s)| as a function of log2s along the maxima lines converging to that point.

Different decays


Combined Estimation

Combined Estimation

Horizontal roughness

Horizontal roughness Vertical

roughness

Vertical roughness Diagonal

roughness

Diagonalroughness

SAR imageSAR image

SWT 2D Vertical components (HL)

Horizontal components (LH)

Diagonal components (LL)

H V D

How to estimate local roughness in SAR images?How to estimate local roughness in SAR images?

Flowchart of the proposed algorithm


Analysis of textureAnalysis of texture

A technique to infere the very local regularity (Hölder or Lipschitz exponent) on a SAR image has been designed.

Höld

er e

xpon

ent

Mul

tifra

ctio

nal B

rown

ian

mot

ion

Hölder exponent retrieved



The algorithm provides an estimate of the local regularity which is independent from the mean value.

Sim

ulat

ed s

peck

le im

age

A

A*5 Loca

l reg

ular

ity re

triev

ed

Despite the difference of mean

intensity of the input, the output matrix is exactly

the same.


Analysis of textureAnalysis of textureTests on a simulated SAR image. Two dark patches with the same mean damping, one simulated with the parameters corresponding to an artificial oil spill, the other one simulated with those corresponding to a low wind area.

Artificial oil spill

Low wind area

The 2 patches can’t be discriminated through thresholding.

The 2 patches can now be discriminated through thresholding.



SAR

imag

e wi

th a

n oi

l spi

llLo

cal e

stim

atio

n of

the

Höld

er

expo

nent

s

Egypt, ERS1 pri image, august 92.Egypt, ERS1 pri image, august 92.Egypt, ERS1 pri image, august 92.


Integration of the algorithmsIntegration of the algorithmsFrom a computational implementation point of view, the algorithms presented rely on the same principle:

WTCombination of

wavelet coefficients

Input SAR image Output

Spot detection

From the point of view of the applications, they are closely linked by mutual contributions:

Contour detection

Texture analysis

- Ship detection - Coastline extraction

- Oil spill characterization

- Extraction of oil spills contour

- False alarms in oil spill detection discarded

- Mask of oil spills candidates


ConclusionsConclusions

RSLab Tool

SA

R Im

age C

oast

line

laye

r

Oil

spill

laye

r

Shi

psla

yer

RSLab Tool

SA

R Im

age C

oast

line

laye

r

Oil

spill

laye

r

Shi

psla

yer

The algorithms have been merged in an operational tool:

A framework based on multiscale tools has been designed to provide a reliable interpretation of oceanic SAR images. 3 complementary algorithms have been considered:

Completely unsupervised.

No training is required.

No previous filtering (no degradation of the resolution, nor blurring)

Multiscale capability


Questions?Questions?

remote sensing lab (rslab) dept. of signal theory & communications mariví tello, carlos...

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