Classification of features selected through Optimum Index Factor (OIF) for improving classification accuracy

The present investigation was performed to determine if the features selected through Optimum Index Factor （OIF） could provide improved classification accuracy of the various categories on the satellite images of the individual years as well as stacked images of two different years as compared to all the features considered together. Further, in order to determine if there occurs increase in the classification accuracy of the different categories with corresponding increase in the OIF values of the features extracted from both the individual years＇ and stacked images, we performed linear regression between the producer＇s accuracy （PA） of the various categories with the OIF values of the different combinations of the features. The investigations demonstrated that there occurs significant improvement in the PA of two impervious categories viz. moderate built-up and low density built-up determined from the classification of the bands and principal components associated with the highest OIF value as compared to all the bands and principal components for both the individual years＇ and stacked images respectively. Regression analyses exhibited positive trends between the regression coeffi- cients and OIF values for the various categories determined for the individual years＇ and stacked images respectively signifying the prevalence of direct relationship between the increase in the information content with corresponding increase in the OIF values. The research proved that features extracted through OIF from both the individual years＇ and stacked images are capable of providing significantly improved PA as compared to all the features pooled together.

SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS

In order to apply Satellite Remote Sensing (RS) to mining areas, some key issues should be solved. Based on an introduction to relative studying background, related key issues are proposed and analyzed oriented to the development of RS information science and demands of mining areas. Band selection and combination optimization of Landsat TM is discussed firstly, and it proved that the combination of Band 3, Band 4 and Band 5 has the largest information amount in all three-band combination schemes by both N-dimensional entropy method and Genetic Algorithm (GA). After that the filtering of Radarsat image is discussed. Different filtering methods are experimented and compared, and adaptive methods are more efficient than others. Finally the classification of satellite RS image is studied, and some new methods including classification by improved BPNN(Back Propagation Neural Network) and classification based on GIS and knowledge are proposed.

Quantum confinement effects and source-to-drain tunneling in ultra-scaled double-gate silicon n-MOSFETs

By using the linear combination of bulk band （LCBB） method incorporated with the top of the barrier splitting （TBS） model, we present a comprehensive study on the quantum confinement effects and the source-to-drain tunneling in the ultra-scaled double-gate （DG） metal-oxide semiconductor field-effect transistors （MOSFETs）. A critical body thickness value of 5 nm is found, below which severe valley splittings among different X valleys for the occupied charge density and the current contributions occur in ultra-thin silicon body structures. It is also found that the tunneling current could be nearly 100% with an ultra-scaled channel length. Different from the previous simulation results, it is found that the source-to-drain tunneling could be effectively suppressed in the ultra-thin body thickness （2.0 nm and below） by the quantum confinement and the tunneling could be suppressed down to below 5% when the channel length approaches 16 nm regardless of the body thickness.

Information extraction and dynamic evaluation of soil salinization with a remote sensing method in a typical county on the Huang-Huai-Hai Plain of China

Monitoring the dynamics of soil salinization is of great importance for agricultural production.This study selected Yucheng County,a typical county on the Huang-Huai-Hai Plain(HHHP)of China,as the study area and evaluated the spatial and temporal variation of soil salinization.Three methods,consisting of principal component analysis(PCA)transformation,tasseled cap(TC)transformation,and optimal band combination(OBC),were used to extract information from an early Landsat multispectral scanner(MSS)image from 1984,and their advantages were compared.In addition,OBC was used on a thematic mapper(TM)image from 2009.An iteratively self-organizing data analysis algorithm was used together with prior knowledge of likely classifications to interpret the MSS and TM images for data classification.Finally,a transfer matrix method was used to assess the spatial and temporal variability of soil salinization and analyze the driving factors of soil salinization.Compared to PCA transformation and OBC,TC transformation was a more effective method for extracting soil salinization information from the MSS sensor.The results indicate that a soil area of approximately 298 km^2was affected by salinity in 1984 in Yucheng County,of which 5.40%,11.96%,and 12.75%were classified as being subject to slight,moderate,and severe salinization,respectively.In 2009,the saline area was reduced to only 146 km^2,of which 10.70%and 3.75%were characterized by slight to moderate salinization and no severe salinization,respectively.The saline land decreased at an average rate of 6 km^2per year.This decrease was probably a result of lower groundwater depth,increased organic fertilizer or crop straw in soil,changed land use type,and increased vegetation coverage.