ABSTRACT:Recently, the use of linear features for processing remote sensing images has shown its importance in applications. As one of typical linear targets, road is a hot spot of remote sensing image interpretation. Since extracting road by manual processing is too expensive and time consuming, researches based on automatic and semi-automatic have become more and more popular. Such interest is motivated by the requirements for civilian and military applications, such as road maps, traffic monitoring, navigation applications, and topographic mapping. How to extract road accurately and efficiently from SAR images is a key problem. In this paper, through analyzing characteristics of road, semi-automatic road extraction based on Extend Kalman Filtering (EKF) and Particles Filtering (PF), is presented. These two methods have the same algorithm flow which is an iterative approach based on prediction and update. The specific procedure as follows: at prediction stage, we obtain prior probability density function by the prior stage and prediction model, and through prior probability density function and the new measurement, at update stage we obtain the posterior probability density function which is the optimal estimation of road system state. Both EKF and PF repeat the steps above until the extracting tasks are finished. We use these two methods to extract road respectively. The effectiveness of the proposed method is demonstrated through the experiments from Howland by UAVSAR in L-band. And through contrast experiments, we discover that extracting difference complexity of road based on different methods can improve accuracy and efficiency. The results show that EKF has better performance on road with middle noise and PF has better performance on road with high noise.

ABSTRACT:Accurate and timely change detection of Earth's surface features is extremely important for understanding relationships and interactions between people and natural phenomena. Many traditional methods of change detection only use a part of polarization information and the supervised threshold selection. Those methods are insufficiency and time-costing. In this paper, we present a novel unsupervised change-detection method based on quad-polarimetric SAR data and automatic threshold selection to solve the problem of change detection. First, speckle noise is removed for the two registered SAR images. Second, the similarity measure is calculated by the test statistic, and automatic threshold selection of KI is introduced to obtain the change map. The efficiency of the proposed method is demonstrated by the quad-pol SAR images acquired by Radarsat-2 over Wuhan of China

ABSTRACT:This paper focuses on backscattering mechanisms selection and supervised classification works for CETC38-X PolSAR image. Thanks to the high radar resolution, many classes of man-made objects are visible in the images. So, land-use classification becomes a more meanful application using PolSAR image, but it involves the selection of classifiers and backscattering mechanisms. In this paper we apply SVM as the classifier and GA as the features selection method. Finally, after we find the best parameters and the suitable polarimetric information, the overall accuracy is up to 97.49%. The result shows SVM is an effective algorithm compared to Wishart and BP classifiers.