Post on 12-Oct-2020
RGB SAR product exploiting multitemporal: general processing and applications
D. Amitrano, G. Di Martino, A. Iodice, D. Riccio, G. Ruello University of Napoli Federico II
Multitemp Conference Brugge, 27 June 2017
Classic approach
Dielectric constant
Soil moisture
Land cover
DEM
Fractal dimension
Thermal dilatation
Ice composition
Phenology
Water pollution
Deformation maps
Processing
Level-1 products
Level-2 products
Bathymetry
Raw signal
Level-0 products
Our approach
Dielectric constant
Soil moisture
Land cover
DEM
Fractal dimension
Thermal dilatation
Ice composition
Phenology
Water pollution
Deformation maps
Processing
Level-1 products
Level-2 products
Level-1α products
Bathymetry
Raw signal
Level-0 products
A paradox
A paradox
MAP3 framework
D. Amitrano et al., “A new framework for SAR multitemporal data RGB representation: Rationale and products,” IEEE Trans. Geosci. Remote Sens., vol. 53, no. 1, pp. 117–133, 2015
RGB-1α: semi-arid environment
June, 2011 August, 2011
Bidi basin \\ 20 km north of Ouahigouya
RGB-1α: semi-arid environment
0
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200
Min Max
Reference Image
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0 1
Coherence
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100
200
Min Max
Test Image
Vegetation
Water
Trees
Soil
Villages
RGB-1α: temperate environment
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100
200
Min Max
Reference Image
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200
0 1
Coherence
0
100
200
Min Max
Test Image
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Min Max
Reference Image
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0 1
Coherence Texture
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Min Max
Test Image
Growing vegetation
Water
Unchanged
Built-up
Too much blue?
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Min Max
Coherence Texture
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0 1
Reference Image
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200
Min Max
Test Image
MAP3 framework
N images N-1 Level-1α products N images 1 Level-1β product
RGB-1β: temperate zone
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Min Max
Time series span + coherence
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0 1
Time series variance
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Min Max
Time series mean Geometric registration
Radiometric calibration
Despeckling
Representation
Geometric registration
Internal calibration
Multitemporal De Grandi filter
Cross-calibration (VALE)
Re-quantization
Band selection
R: Time series variance
G: Time series mean
B: Time series span + coherence
Sea (Bragg)
Unchanged
Crops Built-up
Saxony (Germany), Sentinel-1 Level-1β product
D. Amitrano et al., “Multitemporal Level-1β Products: Definitions, Interpretation, and Applications,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 11, pp. 6545–6562, 2016
Level-1α products – Change detection
D. Amitrano et al., “A new framework for SAR multitemporal data RGB representation: Rationale and products,” IEEE Trans. Geosci. Remote Sens., vol. 53, no. 1, pp. 117–133, 2015
Water resources management
June 2010 July 2010 August 2010
March 2011 December 2010 Binary mask
Working with band ratio 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = 𝐺𝐺�2
𝐵𝐵 − 𝐺𝐺𝐵𝐵 + 𝐺𝐺
, 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 ∈ [−1,1]
𝐺𝐺� = 1 −𝐺𝐺
255
Method T OA FA
P (%) O PxE-4 O
SWPP 0,3 87,4 22/22 2,38 37
BR 2,25 85,7 22/22 4,26 68
ML na 78,4 20/22 7,36 134
kmean 9 89,8 22/22 5,89 120
D. Amitrano et al., “Small Reservoirs Extraction in Semiarid Regions Using Multitemporal Synthetic Aperture Radar Images,” IEEE J. Sel. Topics Appl. Earth Observ., In press
Summary
There is a strong need of new techniques for RS data analysis looking towards the end-user community We proposed a new framework for SAR data analysis for the
definition of two new classes of multitemporal SAR products favoring interpretation and exploitable in application through simple algorithms for information extraction, such as radiometric change-detection indices This index was successfully applied to map small reservoirs in semi-
arid enviroment with encouraging results (high accuracy, reduction of the false alarm rate, robustness) compared with those given by literature techniques of the same complexity
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