Chongwen DUAN, Weidong HU, Xiaoyong DU ATR Key Laboratory, National University of Defense Technology IGARSS 2011, Vancouver

OUTLINE Introduction background the idea of the paper 3D geometrical feature (GF) extraction algorithm Experiment results & Conclusions 2

INTRODUCTION SAR image of an object down range resolution: pulse compression cross range resolution: coherent processing of consecutive echoes Orthographic projection Geometrical features 3

GEOMETRICAL FEATURE Including: shape parameters (length, width, LWR, and height) relative angle with radar 2D features are sometimes ambiguous and the 3D ones are preferred. Two kinds of techniques. 4

3D FEATURE EXTRACTION Signal processing techniques: Echo phase differences Optical image processing techniques: operates with the amplitude images Scattering center matching is difficult. unfocused distortion scattering scintillation 5

SAR IMAGES OF OCEAN SHIPS 6

3D GF EXTRACTION ALGORITHM SAR image preprocessing. Feature extraction of the projected ellipses. Azimuth estimation using Least Square estimation. Geometrical feature extraction. 7

SAR IMAGE PREPROCESSING 8

RELATIONSHIP BETWEEN PARAMETERS 9 azimuth angle unknown estimation errors

AZIMUTH ESTIMATION N images of the same ship estimated from images Elevations known : evaluated 10

NONLINEAR LEAST SQUARE 11

EXPERIMENT 1: EM DATA Model: Fishing ship on rough surface Simulated SAR images (Object region) 12

RESULT OF EXPERIMENT 1 View 1View 2View 3 Real value Estimated LengthWidthHeight Real value Estimated Table 2 Geometrical parameters of the ship (m) Table 1 Estimation of azimuth (°)

EXPERIMENT 2: MONTE CARLO Ellipsoid: 180×25×25 View angles: Noise level: 0~6 14 (a) Length (b) Width (c) Height

CONCLUSIONS Geometrical features are important for ocean ship recognition. Ellipsoid-Ellipse simplification can effectually extract 3D GF from SAR images. Preprocessing is necessary. More efforts are required to improve the accuracy of the width and height estimations. 15

16 THANKS Contact by