Automatic segmentation of high resolution satellite (HRS) imagery is the first step and a very important part of object-oriented approaches. As the resolution of satellite imagery increases, the spectral within-field heterogeneity and the structural or spatial details increase at the same time. Spatial features are important to HRS image analysis in addition to spectral information. This paper presents a novel feature extraction method and evaluates its performance on segmentation of HRS images and color texture images. The first two principal component (PC) images are obtained by principal component analysis (PCA) of a multispectral image. Two texture labeled images are calculated pixel-by-pixel on the PC images through a rotation invariant local binary pattern (LBP) form that we present in this paper. The two texture labeled images are used to calculate the discrete two-dimensional texture histogram of the image. The spectral distribution of a region is the joint distribution of the pixel values of its two PC images after normalization. Then the two histograms are regarded as the texture and spectral distributions of the region and used to calculate the texture and spectral similarity between two regions which is used to determine whether to split a region or merge adjacency regions in the split and merge segmentation framework.
The spatial and temporal characteristics of the data used to describe moving objects' movement make them large in quantity and complex to manage. Different queries to motion data ask for various organization methods. According to the needs of most applications, general motion model is used to represent the translation and rotation of moving objects during a period of time. Because the motion data are multidimensional in space and time dimension, 2<sup>n</sup> tree is employed to construct the main part of the index to these data. Meanwhile other kinds of index algorithms should be added to the index structure so as to meet the needs of queries other than state queries only related to a specific epoch. Thus, motion data index structure (MDIS) is constructed as a multi-entry multi-level index structure for the organization of motion data. Each index within MDIS may work alone or cooperate with each other to process different kinds of queries. The extra space needed for MDIS is only about 5%~6% of the total storage space of motion data themselves. And the respond time to each query is much decreased and acceptable to most applications dealing with moving objects.
Registration of two or more images of the same scene is an important procedure in InSAR image processing that seeks to extract differential phase information exactly between two images. Meanwhile, the efficiency for large volume data processing is also a key point in the operational InSAR data processing chain. In this paper, some conventional registration methods are analyzed in detail and the parallel algorithm for registration is investigated. Combining parallel computing model with the intrinsic properties of InSAR data, the authors puts forward an image parallel registration scheme over distributed cluster of PCs. The preliminary experiment will be implemented and the result demonstrates feasibility and effectiveness of the proposed scheme.
During the last decades, land subsidence has occurred in three main river deltas of China, especially in some metropolitans. With the assistance of some geodetic technologies, such as digital leveling, GPS, total station, and so on, repeat-pass radar interferometry has the capability to monitor land subsidence in 3 dimensions with high accuracy. The potential and limitation of repeat-pass radar interferometry are still being evaluated till now. Considering the operational application of this technique, this paper describes applications of D-InSAR the urban subsidence of China. The case study with a small data stack in Hong Kong presents about 1cm height change in two test sites. The two test sites have mainly been investigated, and some of the key steps of this technique have been discussed in detail. The evolution of the D-InSAR and the future research involve more accurate and robust measurement with large data stacks in a long time series.
Digital change detection using multi-temporal remotely sensed imagery is a key topic in the studies of the global environmental changes. Significant efforts have been made in the development of methods for digital change detection. Among the methods, the multivariate alteration detection (MAD) shows great promising. However, the use of mean and covariance matrix of feature vectors in the method makes the detection non-robust because the mean and covariance matrix are influenced by the presence of outliers. In this article two schemes are proposed to improve the robustness of the MAD method. The two schemes, based on different strategies of outlier handling, consist of a two-pass and a one-pass processing, respectively. Finally a preliminary study was carried out to evaluate the feasibility and effectiveness of the proposed schemes.
A novel unsupervised classification scheme called spatial fuzzy C-means clustering is proposed in this article. Based on conventional fuzzy C-means algorithm, our scheme takes spatial homogeneity into consideration by introducing spatial membership and applying SMNF, thus improved robustness against noises or outliers. Preliminary experimental results are also shown to demonstrate effectiveness of our method.