OPERATIONAL SHIP DETECTION & RAPID URBAN MAPPING : EXPLORING DIVERSE METHODOLOGICAL APPROACHES IN OBJECT RECOGNTION AND SATELLITE IMAGE CLASSIFICATION

Transcription

1 Journées ORFEO Méthodo Janvier 2008 OPERATIONAL SHIP DETECTION & RAPID URBAN MAPPING : EXPLORING DIVERSE METHODOLOGICAL APPROACHES IN OBJECT RECOGNTION AND SATELLITE IMAGE CLASSIFICATION Michel Petit, Christina Corbane, François Marques, Jean-François Faure, Laurent Demagistri corbane@mpl.ird.fr AERONAUTICS & SPACE SPACE 2005 FP Space-1 / GMES Security

2 LIMES Integrated Project Slide 2 2 LIMES Land and Sea Integrated Monitoring for European Security Sixth Framework Program GMES Security (Global Monitoring for Environment and Security) Budget: 21 Meuro 49 Partners 20/25 main users Project Period: Dec 2006 May 2010 Project Overview

3 LIMES GOALS Slide 3 3 GLOBAL MONITORING for ENVIRONMENT and SECURITY To define and develop pre-operational versions of : Services to support security management in EU Decision Support Tools Platforms for the provision of services (interfaces and components) Project Overview

4 LIMES GOALS Slide 4 4 Development of satellite-based services decision-support support tools in relation to the following domains: Organization and distribution of humanitarian relief & reconstruction Surveillance of the EU borders (land and sea) Surveillance and protection of maritime transport for sensitive cargo Protection against emerging security threats Project Overview

5 WP5000: sub-tasks Slide 5 5 WP5330 EO processing chain (IRD)

6 WORK PLAN: Coordinated by IRD Slide 6 6 WP5330 EO processing chain (IRD) P03 : SAR/optical imagery coupling for urban mapping P04 : Automatic optical ship detector and classificator Project Overview IRD IRD

7 SAR/OPTICAL COUPLING FOR RAPID URBAN MAPPING Slide 7 7 OPTICAL (HR) SPOT, LANDSAT... Image Database SAR ERS, RADARSAT... Images pre-processing, co-registration Texture Texture analysis analysis through chain throughmarkovian Markovian chain modelling modelling Urban mask obtained from optical imagery ORFEO Toolbox? Urban mask obtained from radar imagery Results integration (segmentation fusion) Enhanced delimitation of urban mask METHOD OVERVIEW

8 SAR/OPTICAL COUPLING FOR RAPID URBAN MAPPING Slide 8 8 Image in 8 bits With urban areas Without urban areas Directional comet tail (Descombes and Prêteux, 1993) Texture analysis (conditional variance) Unmarked image Marked image Renormalization group technique Derivation of texture parameters Modified unsupervised classification (K-means) Regularization by a Markovian Modelling Elimination of false alarms Two clusters detected by the modified One single class when there Image of classification markers is no urban area K-means Contour of urban area Texture analysis

9 SAR/OPTICAL COUPLING FOR RAPID URBAN MAPPING Slide 9 9 Urban mask obtained from optical imagery Cayenne, Guiana Cayenne, Guiana ASAR (VV); 30 m resolution ; SPOT5 Panchromatic; 5 m resolution ; METHOD IMPLEMENTATION

10 SAR/OPTICAL COUPLING FOR RAPID URBAN MAPPING Slide 1010 Image Database Images pre-processing, co-registration Texture analysis through Markovian chain modelling Urban mask obtained from optical imagery Results Results integration integration (segmentation (segmentation fusion) Urban mask obtained from radar imagery fusion) Enhanced delimitation of urban mask METHOD IMPLEMENTATION

11 SAR/OPTICAL COUPLING FOR RAPID URBAN MAPPING Slide 1111 Enhanced urban area detection METHOD IMPLEMENTATION

12 SAR/OPTICAL COUPLING FOR RAPID URBAN MAPPING Slide 1212 Need for an automatic co-registration of optical and radar imagery; Need for strategy for coupling the results obtained form SAR and optical data : e.g. methods for fusion of urban contours obtained from the two sensors; Search for alternative approach in rapid urban mapping for comparison purposes in terms of : Results accuracy, performance, degree of automation, cost- effectiveness, etc. Research needs

13 AUTOMATIC OPTICAL SHIP DETECTOR & CLASSIFIER Slide 1313 PROGRAMMING ACQUISITION DETECTION AUTOMATIC REPORTING COUPLING MANUAL Optical EO system Receiving station VMS position Automatic ship detection & classification Coupling Manual verification Automatic ship detection & classification Information bulletin XML format WMS Optical imagery SAR ship detection T0 T0 + 1 h: Image Generation & Production METHOD OVERVIEW T0 + 2 h

14 AUTOMATIC OPTICAL SHIP DETECTOR & CLASSIFIER Slide 1414 Operational Phase Learning phase Other Learning set Ship Feature extraction Segmentation (Predetection) Neural Net classifier Segmentation Feature extraction Land Masking Optimization using Genetic Algorithm Error rate Neural Network Classifier Trained classifier METHOD OVERVIEW

15 AUTOMATIC OPTICAL SHIP DETECTOR & CLASSIFIER Slide 1515 Operational Phase Learning phase Other Learning set Ship Feature extraction Segmentation Segmentation (Predetection) (Predetection) Neural Net classifier Segmentation Feature extraction Land Masking Optimization using Genetic Algorithm Error rate Neural Network Classifier Trained classifier SEGMENTATION

16 AUTOMATIC OPTICAL SHIP DETECTOR & CLASSIFIER Moving window adaptive threshold Slide 1616 X i, j σ µ oc oc Threshold Noise removal: morphological opening 2*2 pixels 25 m op= [ aθb] b Region-growing growing segmentation 255 SEGMENTATION

17 AUTOMATIC OPTICAL SHIP DETECTOR & CLASSIFIER Slide 1717 Learning phase Operational Phase Other Learning set Ship Feature extraction Segmentation (Predetection) Neural Net classifier Segmentation Feature Feature extraction extraction Land Masking Optimization using Genetic Algorithm Error rate Neural Network Classifier Trained classifier FEATURE EXTRACTION

18 AUTOMATIC OPTICAL SHIP DETECTOR & CLASSIFIER Slide m spectral, shape & texture features derived for image objects 6 image objects obtained by region-growing growing image segmentation. Texture Shape Spectral Number of pixels 39 Mean 189,41 Standard deviation 57,05 Perimeter 23,90 Area 39,00 Compactness 0,86 Elongation 2,44 M1 * 0,23 Major axe 11,14 Minor axe 4,54 Ratio Major/Minor 2,46 GLCM* mean 150,00 GLCM variance 1,08E+06 GLCM uniformity 404,00 GLCM inertia 4,97E+05 FEATURE EXTRACTION

19 AUTOMATIC OPTICAL SHIP DETECTOR & CLASSIFIER Evolving neural network with a Genetic Algorithm Slide 1919 Object features Coding Decoding NN image classification

20 AUTOMATIC OPTICAL SHIP DETECTOR & CLASSIFIER Slide 2020 Learning phase Operational Phase Other Learning set Ship Feature extraction Segmentation (Predetection) Neural Net classifier Segmentation Feature extraction Land Masking Optimization using Genetic Algorithm Error rate Neural Network Neural Classifier Trained classifier NN image classification

21 AUTOMATIC OPTICAL SHIP DETECTOR & CLASSIFIER Slide 2121 Learning Phase Shrimp boat detection 5 SPOT5 images used for the learning phase Training set : 200 sample objects (68 shrimp boats) NN trained for up to 4000 epochs with the GA 8 extracted features ; 4 hidden nodes ( Number of pixels, Mean, Standard deviation, Minimum, Maximum, Variance,Ratio Major/Minor and Texture uniformity ) METHOD IMPLEMENTATION

22 AUTOMATIC OPTICAL SHIP DETECTOR & CLASSIFIER Slide 2222 Operational Phase Suriname French Guiana Brazil 16 shrimp boats correctly detected vs 31 ships identified by human experts 10 ships less than 14 m long & 1 moored ship False alarm rate = 0 Detection rate = 80% METHOD IMPLEMENTATION

23 AUTOMATIC OPTICAL SHIP DETECTOR & CLASSIFIER Slide 2323 Appropriateness of feature-based approach for ship detection, Viability of utilizing NN evolved by GA in classifying shrimp boats, Reliable ship detection, Minimum operator intervention, Feasibility of ship detection from high spatial resolution optical satellite imagery, Contribution to the detection of illegal fishing activities (XML report). Advantages of the approach

24 AUTOMATIC OPTICAL SHIP DETECTOR & CLASSIFIER Failure to detect ships with less 14 m length Difficulty in separating a ship from its wake Overestimation of ships length Slide 2424 Algorithm tested in conditions of calm sea : no false alarms, 100 m Necessity to test the algorithm under different weathering conditions e.g.: In cloudy conditions : high false alarm rates due to misclassification of clouds into ship class. Major limitations

25 ACTUAL & PLANNED COLLABORATION Slide 2525

26 THANK YOU Slide 2626

27 Slide 2727 Unmarked image Marked image Image of markers