Sequential Monte Carlo samplers provide consistent approximations of sequences of probability distributions and of their normalizing constants, via particles obtained with a combination of importance weights and Markov transitions. This article presents this class of methods and a number of recent advances, with the goal of helping statisticians assess the applicability and usefulness of these methods for their purposes. Our presentation emphasizes the role of bridging distributions for computational and statistical purposes. Numerical experiments are provided on simple settings such as multivariate Normals, logistic regression and a basic susceptible-infected-recovered model, illustrating the impact of the dimension, the ability to perform inference sequentially and the estimation of normalizing constants.

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Building extraction and change detection are two important tasks in the remote sensing domain. Change detection between airborne laser scanning data and photogrammetric data is vulnerable to dense matching errors, mis-alignment errors and data gaps. This paper proposes an unsupervised object-based method for integrated building extraction and change detection. Firstly, terrain, roofs and vegetation are extracted from the precise laser point cloud, based on “bottom-up” segmentation and clustering. Secondly, change detection is performed in an object-based bidirectional manner: Heightened buildings and demolished buildings are detected by taking the laser scanning data as reference, while newly-built buildings are detected by taking the dense matching data as reference. Experiments on two urban data sets demonstrate its effectiveness and robustness. The object-based change detection achieves a recall rate of 92.31% and a precision rate of 88.89% for the Rotterdam dataset; it achieves a recall rate of 85.71% and a precision rate of 100% for the Enschede dataset. It can not only extract unchanged building footprints, but also assign heightened or demolished labels to the changed buildings.

In digital photogrammetry, single stereo image matching models, which may obtain multiple solutions, have low matching reliability and successful rate. Therefore, subsequent productions are greatly constrained. Based on object-space-based ADS40 multiple-image matching models, image-space-based AMMGC multiple-image matching model is proposed. AMMGC model is appropriate for the matching of three-line-array digital images, synthesizes all the information of matching images to avoid blind information area, and obtains a single solution. It checks matching results by image checking strategy and optimizes image matching results by adaptive window extension strategy. Experimental results prove that AMMGC model can obtain reliable matching results for similar feature points and discontinuous feature points, and thus, possesses greatly practicability.

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