Target Detection In Sar Images Based On Sub-Aperture Coherence And Phase Congruency

Abstract For target detection in SAR images, the sub-aperture coherence analysis is employed widely by calculating coefficient of coherence to express the differences of the target signals in sub-aperture images. However the calculation of coherence coefficients is non-adaptive so that when the amplitude difference of coherence coefficient between a target and background is small target detection probability is low. In this paper, with the region growing algorithm, we improve the adaptability of coherence coefficient. We introduce phase congruency algorithm based on sub-aperture coherent method to realize target detection, which also uses the differences of texture feature in sub-aperture images. Experimental results demonstrate that detection probability is as high as 75.8% under the false alarm probability of 0%. The largest area under an ROC curve is 0.9175.