1. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications Mariví Tello, Carlos López-Martínez, Jordi J. Mallorquí. Remote Sensing Laboratory (RSLab) Signal Theory and Telecommunications Department Universitat Politècnica de Catalunya Barcelona, SPAIN Contributive Processing Methods Integrated in a Robust Tool for Ocean Monitoring from SAR Imagery

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

3. 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) 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.

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

5. 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 Transform LL HL LH HH Input Input to next it.

6. 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.

7. 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.

8. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications Background noiseStructure 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… Automatic Spot Detection (I)

9. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications Input Image ResultOCWT - low correlation in the background - local dependencies on the ship Original RADARSAT image Result * * Direct result, no threshold applied Intermediate oriented subbands Based on the previous hypothesis, the following algorithm is proposed: Automatic Spot Detection (II)

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

11. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications 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. Input Output * 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: - depends on the local correlation ENVISAT ASAR image, IM mode, VV pol.

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

13. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications Automatic extraction of linear features (I) SWT- 1 it. Max Point wise Product SWT- 2 it. Max Low pass Horizontal bandpass Vertical bandpass Diagonal bandpass Low pass Horizontal 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) * Direct result – no treshold applied

14. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications Automatic extraction of linear features (II) Comparison of the performance of our algorithm with one of the Sobel filter : ENVISAT imageSobel filter resultAlgorithm proposed * - edges greatly enhanced - background noise noticeably reduced * Direct result – no treshold applied

15. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications Automatic extraction of linear features (III) ENVISAT image Sobel filter result Proposed algorithm * RADARSAT image * Direct result – no treshold applied

16. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications Automatic extraction of linear features (IV) After the edge enhancement phase, the decision is performed by means of a snake algorithm.

17. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications Automatic extraction of linear features (IV) Rivers and inland waters. Oil spills. ENVISAT image Result * * Direct result – no treshold applied

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

19. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications Estimating local roughness The 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 log 2 |Wf(u,s)| as a function of log 2 s along the maxima lines converging to that point. Different decays

20. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications Combined Estimation Horizontal roughness Vertical roughness Diagonal roughness SAR image SWT 2D Vertical components (HL) Horizontal components (LH) Diagonal components (LL) How to estimate local roughness in SAR images? Flowchart of the proposed algorithm

21. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications Analysis of texture A technique to infere the very local regularity (Hölder or Lipschitz exponent) on a SAR image has been designed. Hölder exponent Multifractional Brownian motion Hölder exponent retrieved

22. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications Analysis of texture The algorithm provides an estimate of the local regularity which is independent from the mean value. Simulated speckle image A A*5 Local regularity retrieved Despite the difference of mean intensity of the input, the output matrix is exactly the same.

23. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications Analysis of texture 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 now be discriminated through thresholding.

24. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications Analysis of texture SAR image with an oil spill Local estimation of the Hölder exponents Egypt, ERS1 pri image, august 92.

25. Remote Sensing Lab (RSLab) Dept. of Signal Theory & Communications Integration of the algorithms From a computational implementation point of view, the algorithms presented rely on the same principle: WT Combination 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

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

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