Research On The Sar Image Classification And Change Detection Based On The Markov Random Field

Remote sensing observation techniques as a non-contact is a very important means to obtain spatial information with a multi-band, multi-temporal, a short revisit cycle, rich information. Synthetic aperture radar (Synthetic Aperture Radar, SAR) is a microwave remote sensing imaging systems, which has the all-day, all-weather, has a certain penetration characteristics.Classification is very important for polarimetric synthetic aperture radar(POLSAR) interpretation. Compared with traditional synthetic aperture radar (SAR), fully POLSAR can acquire more information because it obtains the complete polarization state of electromagnetic waves. Target properties, such as material and geometry, affect the polarization state of the electromagnetic wave; thus, POLSAR has significant advantages in target detection, identification, and classification. POLSAR classification algorithm could be classified to two categrary: based on physical backscattering and statistic method. POLSAR classification algorithm can be divided into two categories, known as the classification method based on physical backscattering, and algorithms based on statistical properties. The current popular classification algorithm is the combination of physical scattering characteristics and statistical properties. Target decomposition combined with Markov Random Filed(MRF) is researched in this paper to reduce the impact caused by speckle noise on classification results. Using very high resolution SAR images after earthquake and very high resolution optical images before earthquake, SAR simulation technique is adopted to change detection.The contribution of this paper could be summaried as:1) SAR imaging system, Markov Random Field, POLSAR classification method, Change detection are introduced.2) Methods are proposed by combining target decomposition and MRF on pixel level, which use agglomerative hierarchical clustering algorithm to decrease class number. A new classification algorithm is proposed for POLSAR images using multi-scale Markov Random Field on pixel level. Mean-Shift algorithm is applied to get the initial classification for the coarsest scale, and then Markov Random Field is introduced to achieve the classification result. Classification result on a coarser scale is employed as the initial classification of the nearest finer scale. Meanwhile, the Wishart distribution is employed to model the observed field, and then the iterative conditional modes algorithm is adopted to implement the maximum a posteriori estimation of pixel labels for each scale.3) An unsupervised classification algorithm is proposed for polarimetric SAR images based on Mean Shift Segmentation and MRF. First, polarimetric features are exacted by target decomposition for Mean Shift segmentation. An initial classification is executed by using target decomposition and agglomerative hierarchical clustering algorithm. This is followed by a classification step based on MRF using the mean coherence matrices obtained for each segment. Under the MRF framework, the smoothness term is defined according to the distance between neighboring areas.4) A damage assessment method is proposed by using pre-event very-high resolution(VHR) optical and post-event synthetic aperture radar(SAR) images to detect buildings damaged in an earthquake. First, by using the ENVI software, the length, width, height and other3-D parameters of a rectangle building are extracted by pre-event VHR optical image. Second, a image-based GPU ray-tracing approach is used to simulate SAR images based on the exacted3-D parameters. Finally, the similarity between the simulated SAR images and post-event actual SAR images is analyzed to determine whether the building is damaged.