| dc.description.abstract | Target identification and recognition of SAR images require good feature selection and enhancement. Due to the coherent process, it is difficult to discriminate the SAR target feature properties simply using the shape, shadow, tone, color and texture, to name a few. The scattering center is one of the important properties for extracting the SAR feature. This involves working on raw data (amplitude and phase) as part of image formation. In this paper, we modified an algorithm based a conjugate gradient (CG) optimum method originally proposed for spot mode SAR images, in order to enhance the Stripmap mode SAR targets. The SAR image reconstruction method for spotlight mode uses the range profile data that displays on spatial frequency domain in polar form. By the conventional method it is resampled to be in rectangular form, followed by using inverse Fourier transform with properly weighted window. In papers the recompiling method is done by introducing a projection operator kernel. Directly applying CG method, one has to change the data format to range profile of polar form but suffer from information loss in this recompiling processing. To avoid this information degradation, it has been suggested to replace SAR projection operator kernel by Fourier transform kernel, and let it have suitability for Stripmap mode data.
To validate the effectiveness and efficiency of the modified method, a series of RADARSAT SAR images at fine mode were tested with ground truth available overpass the image acquisition. In addition to, we import the fully polarization SAR data from NASA/JPL AIRSAR acquired from south Taiwan when Sep. 27, 2000. We also compared the performance with MV (Minimum Variance) and MUSIC (Multiple Signal Classification) methods. Performance indices include target to clutter ratio, 3 dB mainlobe width and CPU time. From the logarithmic probability density distribution and column ordering plot of enhanced image and original image, it was demonstrated that modified method provides the best performance among the three methods besides CPU time. | en_US |