High-resolution PolSAR image interpretation based on human images cognition mechanism

Compared to traditional SAR, high-resolution PolSAR not only can provide texture and geometry information, but also can provide polarimetric information, which have been used extensively for various surface features recognition. Traditional methods only based on images characteristics which don´t apply to high-resolution PolSAR images interpretation, causing the algorithm redundancy and low recognition rate. However, human´s image cognition system is an efficient and intelligent image processing system, which nothing can be comparable to in targets recognition. Based on human image cognition mechanism, a new method for features recognition in PolSAR images is proposed in this paper to overcome above shortcomings. The proposed method utilizes hierarchical cognition model to identify different features: the first layer is visual cognition, the second layer is logical cognition and the third layer is psychology cognition. Image segmentation and visual sensitive features extraction and integration are applied in visual cognition to derive preliminary recognition results. Based on the results from first procedure, fuzzy logic theory and Neural Network Model are both utilized in logical cognition. Background characteristics are utilized to identify features precisely in psychology cognition. The whole cognitive procedure is under the guidance of a priori knowledge, which is represented in accordance with production rules. Experiments are conducted over the EMISAR L-band PolSAR data and the E-SAR L-band PolSAR data. The results show that the proposed method can effectively and precisely identify different features in PolSAR images.