In this paper, we present a review of the state of the art in memristor technologies. Along with ionic conducting devices [i.e., conductive bridging random access memory (CBRAM)], we include phase change, and organic/organo-metallic technologies, and we review the most recent advances in oxide-based memristor technologies. We present progress on 3-D integration techniques, and we discuss the behavior of more mature memristive technologies in extreme environments.

This paper proposes a probability model and efficient scheme to extract buildings object from high-resolution panchromatic image in dense urban area. The overall idea of this work is to segment the image into regions, to treat all extracted regions' contours as candidates, and to make use of specific probability model to select 'true' buildings. Discriminative features that characterize buildings are proposed. Application is performed on Quickbird images over Beijing city.

Three fusion methods are presented for landuse classification in this paper. They make fully use of the spectral and terrain information obtained from the optical image and DEM, and combine the remarkable features of each source in the final classification. Their performance is demonstrated by experiments and a comparison is made. Keywords-image fusion;landuse classification;DEM

The ability to estimate soil moisture in the surface layer by microwave remote sensing has been demonstrated. But its application to hydrological and agricultural sciences has been hampered by the complexity of the vegetation canopy and surface roughness which significantly affect the sensitivity of radar backscattering to soil moisture. In this study, we used a simple vegetation model to estimate soil moisture and evaluated the results using multi-temporal L-band AirSAR data

The ERS-1/2 wind scatterometer (WSC) has a resolution cell of about 50 km but provides a high repetition rate (less than four days) and makes measurements at multiple incidence angles. In order to estimate effective surface reflectivity (related to soil moisture content) over bare soil using this instrument, an original methodology based on the integral equation model (IEM) is presented that takes advantage of multiple view angular observations which means two independent observations at the same resolution cell and same time. The possibility of applying the inversion procedure to retrieve soil moisture is investigated using a set of data collected from the Intensive Observation Period (IOP '98) field campaign in 1998 of the Global Energy and Water Experiment (GEWEX) Asian Monsoon Experiment Tibet (GAME/Tibet). The retrieved values obtained for the bare surface are consistent with ground measurements collected in these areas.

Radar backscattering response has the potential of retrieving desired snow parameters, such as snow water equivalence, snow depth, liquid water content which are important factors in hydrological investigation. The objective of this study is to develop an algorithm which can decompose the scattering of wet snow and also develop new description of surface scattering. There are two scattering sources - the volume scattering component from snow pack and the air-snow surface scattering component - for radar backscattering while observing wet snow. Depending upon which scattering component is dominant and then controls the response to snow wetness, an algorithm can be developed to quantitatively describe the relationship between this two scattering sources and snow wetness. We have established a model - simulated at C-bandata-base by using two scattering components. The database covers the most possible wet snow physical properties and surface roughness conditions. Using this data-base, an inversion algorithm can be developed for using C-band multi-polarization measurements. The newly developed algorithm mainly involved two steps: 1) decomposes the surface and volume scattering signals, and 2) then use each scattering component to estimate snow wetness