Unsupervised Oil Spill Detection in SAR Imagery through an Estimator of Local Regularity

Transcription

1 Unsupervised Oil Spill Detection in SAR Imagery through an Estimator of Local Regularity Mariví Tello,, Carlos López-Martínez,, Jordi J. Mallorquí. Remote Sensing Laboratory (RSLab( RSLab) Signal Theory and Telecommunications Department Universitat Politècnica de Catalunya Barcelona, SPAIN Alessandro Danisi,, Gerardo Di Martino, Antonio Iodice,, Giuseppe Ruello,, Daniele Riccio. Department of Electronic and Telecommunication Engineering Università di Napoli Federico II Napoli, ITALY

2 Introduction Most often, interpretation of SAR images is performed manually: slow, unpractical and hardly reproducible procedure: computerised schemes are desirable. Barcelona - Our objective is to develop methods, specifically conceived to deal with the characteristic properties of SAR imagery - Extract as much information as possible, automatically, from every single image, single polarization, with no use of external auxiliary data Barcelona, ERS image, PRI, March 97.

3 Some preliminary considerations (I) Due to speckle, a SAR image is one realization of an underlying stochastic nonhomogeneous process. 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 Our purpose is to establish a specific framework for the automatic exploitation of SAR imagery.

4 Some preliminary considerations (II) 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. (, ) = γ (, ) (, ) = γ (, ) (, ) u x r x r u x r x r u x r 0 i 0 i Random sum of the contributions of all the scatterers within a resolution cell (random walk process). A SAR image is one realization of an underlying stochastic non-stationary process. SAR images are spiky, with a large dynamic range and they involve non-stationary processes. Analysis tools have to be inscribed in a statistical framework, but preserving contextual information.

5 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.

6 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.

7 Automatic Spot Detection 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. Horizontal profile Histogram target OCWT Input image - background noise reduced because the OCWT is sparse Output image * - discontinuities target background enhanced in each direction separately Situation not resolvable by a CFAR approach! 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. X Horizontal profile Histogram * Direct result, no threshold applied target Situation solved by the proposed algorithm!

8 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.

9 Automatic extraction of linear features ENVISAT image Sobel filter result Proposed algorithm * * Direct result no treshold applied RADARSAT image

10 Automatic extraction of linear features Rivers and inland waters. Oil spills. * Direct result no treshold applied ENVISAT image Result * ENVISAT image Result *

11 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.

12 Why an estimator local roughness in SAR images? - The appearance of oil spills is subject to a great diversity: assumption of a priori models is not efficient, training of algorithms based on neural networks are time consuming, algorithms exclusively based on morphological features are not robust - Oil spills and look-alikes can present remarkable similarities. Examples of oil spills Examples of look-alikes

13 Why an estimator local roughness in SAR images? The roughness of the sea is the local distribution of its height. The roughness in the SAR image is the spatial variability of grey level in the neighbourhood of each pixel. Our objective is to provide a quantitative measure as local as possible of the regularity of the SAR signal.

14 Why an estimator local roughness in SAR images? The roughness of the sea is the local distribution of its height. The roughness in the SAR image is the spatial variability of grey level in the neighbourhood of each pixel. Our objective is to provide a quantitative measure as local as possible of the regularity of the SAR signal.

15 Why an estimator local roughness in SAR images? The roughness of the sea is the local distribution of its height. The roughness in the SAR image is the spatial variability of grey level in the neighbourhood of each pixel. Our objective is to provide a quantitative measure as local as possible of the regularity of the SAR signal.

16 How to estimate local roughness in SAR images? The regularity of a single isolated pixel is nonsense. It depends on its intensity value relative to that of its neighbours. Multiscale concept

17 How to estimate local roughness in SAR images? The regularity of a single isolated pixel is nonsense. It depends on its intensity value relative to that of its neighbours. Multiscale concept

18 How to estimate local roughness in SAR images? The regularity of a single isolated pixel is nonsense. It depends on its intensity value relative to that of its neighbours. Multiscale concept By following the scale to scale energy variations the local regularity can be infered.

19 How to estimate local roughness in SAR images? The decay of the Wavelet Transform amplitude across scales is related to the uniform and pointwise Lipschitz regularity of the signal. Homogeneous decay The Lipschitz or Hölder exponent at a point is the maximum slope of log 2 Wf(u,s) as a function of log 2 s along the maxima lines converging to that point. Projection of the pointwise evolution across scales (obtained from a WT) of a cut of a homogeneous sea area. Different decays Projection of the pointwise evolution across scales (obtained from a WT) of a cut intercepting an oil spill.

20 How to estimate local roughness in SAR images? Horizontal components (LH) SAR image DWT 2D Vertical components (HL) Diagonal components (LL) Horizontal roughness α H Vertical roughness α V Diagonal roughness α D Combined Estimation Flowchart of the proposed algorithm

21 Results In order to validate the algorithm, it is first run on a simulated surface. Hölder exponent Multifractional Brownian motion Hölder exponent retrieved

22 Results The algorithm provides an estimate of the local regularity which is independent from the mean value. A Simulated speckle image A*5 Local regularity retrieved Despite the difference of mean intensity of the input, the output matrix is exactly the same.

23 Results Tests 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 be discriminated through thresholding.

24 Results SAR image with an oil spill Egypt, ERS1 pri image, august 92. Egypt, ERS1 pri image, august 92. Egypt, ERS1 pri image, august 92. Local estimation of the Hölder exponents

25 Results SAR image with an oil spill Egypt, ERS1 pri image, august 92. Egypt, ERS1 pri image, august 92. Egypt, ERS1 pri image, august 92. Local estimation of the Hölder exponents α MAR < α VERTIDO

26 Analysis of texture Local estimation of the Hölder exponents SAR image

27 Conclusions An algorithm based on the Hölder exponent has been introduced for automatic detection of oil spills candidates in the sea surface in SAR imagery. Completely unsupervised. No training is required. No previous filtering (no degradation of the resolution, nor blurring) Multiscale capability Tests on simulated images have proven its capacity to discriminate between oil spills and look alikes with the same damping.

28 Integration of the algorithms From a computational implementation point of view, the algorithms presented rely on the same principle: Input SAR image WT Combination of wavelet coefficients Output From the point of view of the applications, they are closely linked by mutual contributions: Spot detection Contour detection - Ship detection - Coastline extraction - Extraction of oil spills contour - False alarms in oil spill detection discarded - Mask of oil spills candidates Texture analysis - Oil spill characterization

29 D= 1.13