Change detection in Tai’an city of eastern China using a pair of qual-polarimetric Advanced Land Observing Satellite phased array type L-band synthetic aperture radar (ALOS PALSAR) data was studied. The procedures consisted of polarimetric features extraction, optimal polarimetric feature group selection, supervised classification, and result accuracy assessment. Feature extraction from PALSAR data was performed first, and then the polarimetric features were categorized into several groups. Polarimetric optimum index factor (POIF) and distance factor (DF) were selected to measure and evaluate the suitability of each feature group for urban change detection. The best group of features was identified including linear polarization correlation coefficient (ρ HH–VV ), right–left (R–L) circular polarization correlation coefficient (ρ RR–LL ), total power (TP), and cross-polarization isolation (XPI). Afterward, four difference images of the identified features extracted from the two PALSAR data were derived, respectively. Then, the random forest (RF) classifier was employed to perform a supervised classification of the four difference images. Three classes were quantified, including no-change, change from undeveloped area to developed area, and vice versa. The overall accuracy of change detection was about 84% and Cohen’s Kappa coefficient was 0.71. Consequently, satisfactory outcomes were obtained in the application of the polarimetric ALOS PALSAR data of moderate resolution in detecting urban land use and land cover type changes.
Geocoding is crucial for using remotely sensed data in almost all applications, especially when a combination of multiple data sources is required. However, geocoding a synthetic-aperture radar image with the standard range-Doppler method is a time-consuming process due to unavoidable iterations. Using a replacement sensor model, for example the rational polynomial camera (RPC) model, can significantly reduce the calculation time cost. Another way to improve the calculation efficiency is to use massively parallel processing techniques. Modern graphics processing units (GPU) can be used as parallel processing units for computationally intensive applications. Using NVIDIA's Compute Unified Device Architecture (CUDA) the implementation of the range-Doppler and RPC methods on GPUs is easy, because the existing C/C++ code can be reused. With further optimizations for GPU processing, tremendous improvements can be achieved. The CUDA implementations run about 10 to 30 times faster compared with similar implementations on the central processing unit (CPU), and almost 200 times faster if particularly optimized for GPU computing.
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, 2n 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.
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.
Change detection is a key topic in land use/land cover related studies and significant efforts have been made in the development of methods for change detection. In this article a multivariate analysis method based on canonical transformation is introduced into change detection using multi-temporal remote sensing imageries. Afterwards an automatic unsupervised discriminating technique based on the Bayes Rule of Minimum Error is employed for changed areas identification in the difference image. Experimental results of a case study using Landsat TM imageries are presented to demonstrate the effectiveness of our method.
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