Poster COAST Bordeaux SPAGNOLI MINGHELLI 2 sc(1)(1) .pdf
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Automatic method to extract the Shoreline
using WorldView-2 images, containing foam
J. Spagnoli1, A. Minghelli1, S. Charmasson2
1LSIS,
University of Toulon, La Garde, France
2IRSN, PSE-SRTE/LRTA, La Seyne sur Mer, France
Objective
Results of classifications
• Study the impact of the tsunami of Fukushima (Japan, 2011) on the shoreline
• Find an automatic method to extract the shoreline from satellite images in
presence of foam
• Estimate the evolution of both erosion and accretion areas between 2 dates
: before the tsunami (8 November 2010) and after the tsunami (18 March
2011)
2010
Water
Land
Foam
Multispectral data
• WorldView-2
images
2011
• 2m resolution
2010
2011
Results of classifications and the corresponding land/water maps
Comparisons (false-positive, false-negative)
2011
2010
ED
ED
MLE
SAM
closing
opening
No operations
closing
No operations
closing
opening
No operations
closing
closing
opening
SAM
MLE
MLE gives the best results concerning EP and EN for each date
PIR
Erosion and accretion
Methodology
Multispectral image
Erosion
Classification
Fusion water/foam classes
Morphologic
operation
Segmentation
No operations
EN
opening
EP
No operations
Foam leads to
errors after
thresholding
opening
160 000
140 000
120 000
100 000
80 000
60 000
40 000
20 000
0
160 000
140 000
120 000
100 000
80 000
60 000
40 000
20 000
0
closing
Problem statement
opening
• Color composite :
B : band 1 (425 nm)
G : band 3 (545 nm)
R : band 6 (725 nm)
No operations
• 8 spectral bands
(400 – 1040 nm )
(fermeture ou ouverture)
100%
90%
90%
80%
80%
70%
70%
60%
60%
50%
50%
40%
40%
30%
30%
20%
20%
10%
10%
0%
0%
Supervised-classifications distances
Spectral Angle Mapper
(SAM)
Maximum Likelihood
Estimation (MLE)
SAM
MLE
closing
ED
Accretion
SAM
Concerning erosion area, MLE gives the best results
Concerning accretion area, all methods give equivalent results
Land/water map
i : the number of the spectral band
k : the number of the class
Rk : the covariance matrix of class k
x : the spectral profile of the pixel
𝑟𝑘ҧ : the mean spectral profile of class k
Opening
100%
ED
Euclidean Distance (ED)
No operations
𝑁
𝑑 𝑥, 𝑟𝑘 =
𝑥(i) − 𝑟ഥ𝑘 (i)
2
i=1
𝛼(𝑥, 𝑟𝑘 ) = cos −1
Conclusion
•
•
•
•
Morphologic operations do not enhance the results
Maximum likelihood gives the best results to extract the shoreline
Erosion area : 410 604 m²
Accretion area : 174 804 m²
σ𝑁
𝑖=1 𝑥(i) ∗ 𝑟𝑘ҧ (i)
σ𝑁
𝑖=1 𝑥(𝑖)² ∗
𝑔 𝑥, 𝑟𝑘 = − ln 𝑅𝑘
MLE
− 𝑥 − 𝑟𝑘ҧ
𝑇
σ𝑁
𝑖=1 𝑟𝑘ҧ (i)²
∗ 𝑅𝑘−1 ∗ 𝑥 − 𝑟𝑘ҧ
Reference
• Richards, John A, and Xiuping Jia. 2006. Remote Sensing Digital Image Analysis-Hardback. Springer, Berlin/Heidelberg.
• Kruse, FA, AB Lefkoff, JW Boardman, KB Heidebrecht, AT Shapiro, PJ Barloon, and AFH Goetz. 1993. “The Spectral
Image Processing System (SIPS)—interactive Visualization and Analysis of Imaging Spectrometer Data.” Remote
Sensing of Environment 44 (2–3): 145–163.
