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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
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
• 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.
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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)
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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.
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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.
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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.
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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)
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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)
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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)
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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.
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
Some preliminary considerations (III)Some preliminary considerations (III)
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.
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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.
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
Some preliminary considerations (III)Some preliminary considerations (III)
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.
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
Analysis of textureAnalysis of texture
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.
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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.
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
Analysis of textureAnalysis of texture
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.
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
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
Remote Sensing Lab (RSLab)Dept. of Signal Theory & Communications
Questions?Questions?