A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data

This study proposes a new four-component algorithm for land use and land cover (LULC) classification using RADARSAT-2 polarimetric SAR (PolSAR) data. These four components are polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms. First, polarimetric decomposition can be used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects. Second, PolSAR interferometry is used to extract polarimetric interferometric information to support LULC classification. Third, the main purposes of object-oriented image analysis are delineating image objects, as well as extracting various textural and spatial features from image objects to improve classification accuracy. Finally, a decision tree algorithm provides an efficient way to select features and implement classification. A comparison between the proposed method and the Wishart supervised classification which is based on the coherency matrix was made to test the performance of the proposed method. The overall accuracy of the proposed method was 86.64%, whereas that of the Wishart supervised classification was 69.66%. The kappa value of the proposed method was 0.84, much higher than that of the Wishart supervised classification, which exhibited a kappa value of 0.65. The results indicate that the proposed method exhibits much better performance than the Wishart supervised classification for LULC classification. Further investigation was carried out on the respective contribution of the four components to LULC classification using RADARSAT-2 PolSAR data, and it indicates that all the four components have important contribution to the classification. Polarimetric information has significant implications for identifying different vegetation types and distinguishing between vegetation and urban/built-up. The polarimetric interferometric information extracted from repeat-pass RADARSAT-2 images is important in reducing the confusion between urban/built-up and vegetation and that between barren/sparsely vegetated land and vegetation. Object-oriented image analysis is very helpful in reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects is helpful in distinguishing between water and lawn. The decision tree algorithm can achieve higher classification accuracy than the nearest neighbor classification implemented using Definiens Developer 7.0, and the accuracy of the decision tree algorithm is similar with that of the support vector classification which is implemented based on the features selected using genetic algorithms. Compared with the nearest neighbor and support vector classification, the decision tree algorithm is more efficient to select features and implement classification. Furthermore, the decision tree algorithm can provide clear classification rules that can be easily interpreted based on the physical meaning of the features used in the classification. This can provide physical insight for LULC classification using PolSAR data.     

Microstructural development and electrical properties of lead zirconate titanate composite films

Dihydroxyalcohols (DIOL route) and 2-methoxyethanol (MOE route) are two of the most commonly used solvents in preparing sol-gel lead zirconate titanate (PZT) films. The employment of the PT(MOE) film as a buffer layer for PZT(DIOL) films not only offers advantages of more precise film thickness and better adhesion to substrate, but also reduces the crystallization temperature, improves the surface smoothness, and increases the grain size of the PZT(DIOL)/PT(MOE) composite films. The PT (MOE) buffer layer also improves electrical properties, such as polarization-electric field behavior, fatigue endurance, and leakage behavior, of PZT films.     

The acculturation gap-distress hypothesis among high-risk Mexican American families.

The authors tested the acculturation gap-distress hypothesis by examining whether parent-adolescent acculturation gaps were associated with greater conflict and youth conduct problems among 260 high-risk Mexican American families. The authors operationalized acculturation gaps in 2 ways: parent-youth mismatches in acculturation style, and parent-youth discrepancies in acculturation toward both mainstream and heritage cultures. Acculturation gaps were common, but results of hierarchical regression analyses indicated that parent-youth discrepancies in acculturation toward mainstream and heritage cultures were not related to increased conflict or youth conduct problems. Conduct problems were no higher in families in which the adolescent was more aligned with mainstream culture than the parent. Unexpectedly, the authors found more youth conduct problems in families in which the youth was more aligned with traditional culture than the parent. The results call into question the assumption that the more rapid acculturation of adolescents to American culture inevitably leads to distress in minority families. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Multitemporal SAR images for monitoring cultivation systems using case-based reasoning

This paper demonstrates that multitemporal satellite SAR images are most suitable for monitoring the rapid changes of cultivation systems in a subtropical region. A new method is proposed by applying case-based reasoning (CBR) techniques to the classification of SAR images. Stratified sampling is carried out to collect the cases so that the variations of backscatters within a class can be appropriately captured. The use of discrete cases can conveniently represent the internal changes of a class under complicated situations, such as spatial changes in soil conditions and terrain features. These spatial variations are difficult to represent by using rules or mathematical equations. The proposed method has better classification performance than supervised classification methods in the study area. The case library is reusable for time-independent classification when the SAR images are acquired at the same time of the crop growth cycles for different years. The proposed method has been tested in the Pearl River Delta in South China.     

Seismic Retrofit of Hollow Rectangular Bridge Columns

The seismic performance of rectangular hollow bridge columns is a significant issue of the high-speed rail project in Taiwan. The flexural ductility and shear capacity of such columns with the configuration of lateral reinforcement used in Taiwan have been studied recently. This paper reports that hollow rectangular bridge columns retrofitted with fiber-reinforced polymer (FRP) sheets were tested under a constant axial load and a cyclically reversed horizontal load to investigate their seismic behavior, including flexural ductility, dissipated energy, and shear capacity. An analytical model was also developed to predict the moment-curvature curve of sections and the load-displacement relationship of columns. Based on the test results, the seismic behavior of such columns will be presented. The test results were also compared to the proposed analytical model. It was found that the ductility factors of the tested piers are in the range from 3.4 to 6.3, and the proposed analytical model can predict the load-displacement relationship of such columns with acceptable accuracy. All in all, FRP sheets can effectively improve both the ductility factor and shear capacity of hollow rectangular bridge columns.     

Image contrast enhancement for preserving mean brightness without losing image features

Histogram equalization is a well-known and effective technique for improving the contrast of images. However, the traditional histogram equalization (HE) method usually results in extreme contrast enhancement, which causes an unnatural look and visual artifacts of the processed image. In this paper, we propose a novel histogram equalization method that is composed of an automatic histogram separation module and an intensity transformation module. First, the proposed histogram separation module is a combination of the proposed prompt multiple thresholding procedure and an optimum peak signal-to-noise ratio (PSNR) calculation to separate the histogram in small-scale detail. As the final step of the proposed process, the use of the intensity transformation module can enhance the image with complete brightness preservation for each generated sub-histogram. Experimental results show that the proposed method not only retains the shape features of the original histogram but also enhances the contrast effectively.