Asar Image Target Recognition Based on the Combined Wavelet Transformation

ASAR image target detection and recognition has been always a hot research. In this paper, on the foundation of the previous studies, a target recognition method based on combined wavelet transformation and cloud model is proposed. According to the good local characteristic, multi-resolution, effectiveness and the sensitivity to both direction and texture of the combined wavelet transformation, the detection method based on combined wavelet restrained the image noise and the errors, such as the object missing and false warning, caused by the use of the detection method based on the brightness alone. Then in order to solve the uncertainty of pixels, especially the object edge, an integrated method named soft segmentation for image segmentation based on the linguistic cloud model, which is a model for the conversion between qualitative and quantitative in the field of artificial intelligence is applied. Accurate target recognition is achieved after further processing eventually, such as mathematical morphology analysis. This integrated mechanism is applied to the ASAR data acquired from Zhuhai, China for carrying on the fish ponds recognition as well as the comparative experiments with classical methods. Results show that this approach can recognize the target more accurately and quick, which indicate that the synthetic scheme for target detection and recognition is flexible and robust and the advantages to the traditional detection operators or crisp segmentation methods are distinct.