Spatial resolution enhancement of SSM/I data

One of the limitations in using Special Sensor Microwave/Imager (SSM/I) data for land and vegetation studies is the relatively low-spatial resolution. To ameliorate this limitation, resolution-enhancement algorithms can be applied to the data. In this paper, the Backus-Gilbert inversion (BGI) technique and the scatterometer image-reconstruction (SIR) algorithm are investigated as possible methods for creating enhanced resolution images from SSM/I data. The two algorithms are compared via both the simulation and the actual SSM/I data. The algorithms offer similar resolution enhancement, though SIR requires significantly less computation. Sample results over two land regions of South America are presented     

Spatial resolution enhancement of terrestrial features usingdeconvolved SSM/I microwave brightness temperatures

A method for enhancing the 19-, 22-, and 37-GHz measurements of the SSM/I (Special Sensor Microwave/Imager) to the spatial resolution and sampling density of the high-resolution 85-GHz channel is presented. An objective technique for specifying the tuning parameter, which balances the tradeoff between resolution and noise, is developed in terms of maximizing cross-channel correlations. Various validation procedures are performed to demonstrate the effectiveness of the method, which, it is hoped, will provide researchers with a valuable tool in multispectral applications of satellite radiometer data     

SSM/I instrument evaluation

The Special Sensor Microwave/Imager (SSM/I) instrument and scan geometry are briefly described. The results of investigations of the stability of the gain, calibration targets and spin rate, the radiometer noise and sensitivity, the coregistration, the beam width and main-beam efficiency of the antenna beams, and the absolute calibration and geolocation of the instrument are presented. The results of this effort demonstrate that the SSM/I is a stable, sensitive, and well-calibrated microwave radiometric system capable of providing accurate brightness temperatures for microwave images of the Earth and for use by environmental product retrieval algorithms. It is predicted that this SSM/I and the 11 future ones currently built or to be built will provide high-performance microwave measurements for determination of global weather and critical atmospheric, oceanographic, and land parameters to operational forecasters and users and the research community for the next two decades     

Spatial resolution improvement of MeRIS images by fusion with TMimages

The medium resolution imaging spectrometer (MeRIS) is ready to be launched. Considering its spatial resolution (240 m), potential users will have to face the mixed pixel problem. The authors propose to improve its resolution by merging MeRIS images with Landsat TM images. The best characteristics of the two images (spatial, spectral, temporal) are then preserved     

A technique for enhancing and matching the resolution of microwavemeasurements from the SSM/I instrument

The authors describe and apply a correction technique for matching the resolution of all the frequencies of the Special Sensor Microwave/Imager (SSM/I) to the 25-km spatial resolution of the 37-GHz channel. To accomplish this, it is necessary to increase the spatial resolution of the 19- and 22-GHz channels while degrading that of the 85-GHz channel. It is found that the approach produces adequate enhancement of the spatial resolution to make such a correction worthwhile. Results suggest that this technique decreases brightness temperature differences stemming solely from spatial resolution differences by over 50% for the low resolution channels with only a modest increase in random noise. The correction can also help to better resolve small features which would otherwise be lost due to the lack of resolution     

Interpretation of SSM/I measurements over Greenland

Multispectral brightness temperature (T_B) measurements over Greenland are obtained from the Special Sensor Microwave Imager (SSM/I), which are flown aboard the DMSP satellites. This paper examines the different spectral characteristics over Greenland throughout the year. Although snow covers the vast majority of Greenland, the southern regions rarely exhibit the spectral characteristics associated with snowcover (i.e., T_B decreases at higher frequencies). In fact, the SSM/I polarization and frequency measurements over southern Greenland are more indicative of water than a snow-covered surface (i.e., T_B increases at higher frequencies). A simplified physical model is developed to help explain the anomalous measurements over southern Greenland. Model results indicate that high frequency radiation is mainly scattered by snow grains residing above the subsurface ice layers, whereas low frequency radiation is scattered throughout a much greater depth. Since low frequencies are scattered throughout a greater volume, they are depressed relative to high frequencies, and the typical snowcover signature is absent     

A contrast and comparison of near-sea surface airtemperature/humidity from GMS and SSM/I data with an improved algorithm

With data sets gained from Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) microwave channels, Geostationary Meteorological Satellite (GMS-5) infrared channels, and ship-measured data, the statistical algorithms to estimate sea surface temperature and near-sea surface air humidity around Taiwan and the South China Sea areas are developed. Then a new, improved method to estimate near-sea surface air temperature based on the algorithm proposed by Konda et al. (1996) is established in this study. The results estimated with SSM/I data show that the root mean square error (RMSE) of SST, near-sea surface air humidity and air temperature over the oceans around Taiwan and the South China Sea are 1.2 K,1.43 g/kg, and 1.6 K, respectively. The results with GMS data are 1.7 K,1.71 g/kg and 1.7 K, respectively. The results also show that the improvements in the algorithm of Konda et al. simplify the computation scheme, improve the accuracy, and match the regional ocean-atmosphere properties in retrieving near-sea surface air temperature. The estimate produced using SSM/I and GMS data also show good consistency between them, both in temporal and spatial variations. Basically, the accuracy of this result implies strong potential for application of satellite data to relative studies and operational work in the ocean-atmosphere interaction     

Global identification of snowcover using SSM/I measurements

Visible satellite sensors have monitored snowcover throughout the Northern Hemisphere for almost thirty years. These sensors can detect snowcover during daylight, cloud-free conditions. The operational procedure developed by NOAA/NESDIS requires an analyst to manually view the images in order to subjectively distinguish between clouds and snowcover. Because this procedure is manually intensive, it is only performed weekly. Since microwave sensors see through nonprecipitating clouds, snowcover can be determined objectively without the intervention of an analyst. Furthermore, microwave sensors can provide daily analysis of snowcover in real-time, which is essential for operational forecast models and regional hydrologic monitoring. Snowcover measurements are obtained from the Special Sensor Microwave Imager (SSM/I), flown aboard the DMSP satellites. A decision tree, containing various filters, is used to separate the scattering signature of snowcover from other scattering signatures. Problem areas are discussed and when possible, a filter is developed to eliminate biases. The finalized decision tree is an objective algorithm to monitor the global distribution of snowcover. Comparisons are made between the SSM/I snowcover product and the NOAA/NESDIS subjectively analyzed weekly product     

Spatial resolution properties of motion-compensated tomographic image reconstruction methods

Many motion-compensated image reconstruction (MCIR) methods have been proposed to correct for subject motion in medical imaging. MCIR methods incorporate motion models to improve image quality by reducing motion artifacts and noise. This paper analyzes the spatial resolution properties of MCIR methods and shows that nonrigid local motion can lead to nonuniform and anisotropic spatial resolution for conventional quadratic regularizers. This undesirable property is akin to the known effects of interactions between heteroscedastic log-likelihoods (e.g., Poisson likelihood) and quadratic regularizers. This effect may lead to quantification errors in small or narrow structures (such as small lesions or rings) of reconstructed images. This paper proposes novel spatial regularization design methods for three different MCIR methods that account for known nonrigid motion. We develop MCIR regularization designs that provide approximately uniform and isotropic spatial resolution and that match a user-specified target spatial resolution. Two-dimensional PET simulations demonstrate the performance and benefits of the proposed spatial regularization design methods.     

星载微波遥感对中国1998年洪涝的观测统计研究

在星载微波ＳＳＭ／Ｉ１９９８年７－８月间对中国长江流域洪涝区域的观测中,用３７ＧＨｚ,８５ＧＨｚ二通道水平极化辐射亮度温度之差定义的洪涝指数ＦＩ,作为洪涝区域的识别特征指数,描绘了７－８月间ＦＩ在该区域空间分布的变化。Ｇ－空间自相关统计方法用某点局域上变量的权重之和与整个区域中变量之和的比例,来描述该变量值的空间分布相关与类聚程度。     

Multigrid tomographic inversion with variable resolution data and image spaces.

A multigrid inversion approach that uses variable resolutions of both the data space and the image space is proposed. Since the computational complexity of inverse problems typically increases with a larger number of unknown image pixels and a larger number of measurements, the proposed algorithm further reduces the computation relative to conventional multigrid approaches, which change only the image space resolution at coarse scales. The advantage is particularly important for data-rich applications, where data resolutions may differ for different scales. Applications of the approach to Bayesian reconstruction algorithms in transmission and emission tomography with a generalized Gaussian Markov random field image prior are presented, both with a Poisson noise model and with a quadratic data term. Simulation results indicate that the proposed multigrid approach results in significant improvement in convergence speed compared to the fixed-grid iterative coordinate descent method and a multigrid method with fixed-data resolution.     

The potential of combining SSM/I and SSM/T2 measurements to improvethe identification of snowcover and precipitation

The potential of passive microwave radiometry for classifying snowcover and precipitation using measurements from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Water Vapor Profiler (SSM/T2) is investigated by modelling the radiative transfer for different surface types and atmospheric conditions. The model accounts for various land surfaces and vegetation covers, different snow types as well as wind roughened ocean water. The atmospheric part includes multiple scattering and depolarization by cloud droplets and precipitating water as well as ice spheres. It was found, that the combination of a window channel (91 GHz) and an atmospheric sounding channel (183±7 GHz) can improve the separation of snowcover and precipitation which is difficult by using only SSM/I channels. The 183±7 GHz channel is strongly influenced by the water vapor distribution which makes its use difficult for warm rain cases and low cloud tops. Then, the signature at this frequency is not unique and the above relation gives no further improvement of the classification. However, the identification of rainfall over cold land backgrounds can be significantly improved, which is illustrated by the application of a combined SSM/I-SSM/T2 algorithm to two satellite datasets when compared to the SSM/I algorithm and to operational surface weather maps     

应用DBS技术在SAR/GMTI模式下高分辨率成像

多普勒波束锐化成像技术(DBS)能为检测出的动目标定位提供参考，从而有效地实现精确打击。DBS技术在弹道导航、地形匹配等许多方面有着广泛的应用。首先简述了DBS成像方法的一般工作原理，经分析得出结论：DBS成像在SAR，GMTI模式下应用时，如果引入聚焦型的SAR处理方法．即通过去距离单元走动、去方位向调频斜率等方法可有效提高雷达成像质量。最后利用雷达实测数据进行处理．得出了较满意的高分辨DBS成像结果。     

Metamorphic signature of snow revealed in SSM/I measurements

Brightness temperatures (19, 22, 37, 85 GHz) measured by the special sensor microwave/imager (SSM/I) are analyzed using data from the snow monitoring network within the former Soviet Union during the 1987-1988 winter period. It is shown that in the beginning of winter, the SSM/I measurements display the classical snow scattering signature, i.e., the brightness temperatures decrease with increasing depth, and the largest decrease occurs at the highest frequency. Dramatic deviations from this pattern are observed in the middle of winter, where the brightness temperature approaches a minimum and then begins to increase despite the fact that the snow depth remains constant or continues to grow. The two-stream radiative transfer model is combined with results from dense media theory to help explain the phenomenon. Model results suggest that the increase in brightness temperature is due to a decrease of the single scattering albedo as the snowpack ages. This decrease of the albedo is related to changes in the snow crystalline structure due to metamorphism. Consequences for the interpretation of satellite measurements and development of algorithms for deriving snow water equivalent are discussed     

Determination of oceanic total precipitable water from the SSM/I

Results are presented of calibration/validation studies showing the ability of the Special Sensor Microwave/Imager (SSM/I) to measure total precipitable water in the atmosphere over the ocean. Comparisons between radiosondes and the SSM/I are presented for three different algorithms. The results show the possibility of a distinct improvement in the retrieval of total precipitable water over the ocean. The global, nonlinear algorithm is more sensitive to cloud liquid water content, rain, and sea ice. The additional sensitivity is due to the screening of rain and sea ice from the dependent data and the squared term in the retrieval algorithm. Thus, it will be very important to have good screening procedures for identifying these conditions. The linear algorithm overestimates in the mid-range and underestimates at large values of total precipitable water. The explanation for this effect is probably related to the selection of the center of the water vapor line as the operating frequency of the SSM/I water vapor channel. The line center is most likely to exhibit a saturation effect at large water vapor amounts, and pressure and temperature effects can also be important, depending on the distribution of water vapor in the atmosphere     

Determination of cloud liquid water content using the SSM/I

As part of a calibration/validation effort for the Special Sensor Microwave/Imager (SSM/I), coincident observations of SSM/I brightness temperatures and surface-based observations of cloud liquid water were obtained. These observations were used to validate initial algorithms and to derive an improved algorithm. The initial algorithms were divided into latitudinal-, seasonal-, and surface-type zones. It was found that these initial algorithms, which were of the D-matrix type, did not yield sufficiently accurate results. The surface-based measurements of channels were investigated; however, the 85 V channel was excluded because of excessive noise. It was found that there is no significant correlation between the SSM/I brightness temperatures and the surface-based cloud liquid water determination when the background surface is land or snow. A high correlation was found between brightness temperatures and ground-based measurements over the ocean     

Validation of sea ice motion from QuikSCAT with those from SSM/I and buoy

Arctic sea ice motion for the period from October 1999 to March 2000 derived from QuikSCAT and ocean buoy observations.Special Sensor Microwave/Imager (SSM/I) data using the wavelet analysis method agrees well with ocean buoy observations. Results from QuikSCAT and SSM/I are compatible when compared with buoy observations and complement each other. Sea ice drift merged from daily results from QuikSCAT, SSM/I, and buoy data gives more complete coverage of sea ice motion. Based on observations of six months of sea ice motion maps, the sea ice motion maps in the Arctic derived from QuikSCAT data appear to have smoother (less noisy) patterns than those from NSCAT, especially in boundary areas, possibly due to constant radar scanning incidence angle. For late summer, QuikSCAT data can provide good sea ice motion information in the Arctic as early as the beginning of September. For early summer, QuikSCAT can provide at least partial sea ice motion information until mid-June. In the Antarctic, a case study shows that sea ice motion derived from QuikSCAT data is consistent with pressure field contours.     

Spectral linear mixing model in low spatial resolution image data

Different ways to estimate the spectral reflectance for the component classes in a mixture problem have been proposed in the literature (pure pixels, spectral library, field measurements). One of the most common approaches consists in the use of pure pixels, i.e., pixels that are covered by a single component class. This approach presents the advantage of allowing the extraction of the components' reflectance directly from the image data. This approach, however, is generally not feasible in the case of low spatial resolution image data, due to the large ground area covered by a single pixel. In this paper, a methodology aiming to overcome this limitation is proposed. The proposed approach makes use of the spectral linear mixing model. In the proposed methodology, the components' proportions in image data are estimated using a medium spatial resolution image as auxiliary data. The linear mixing model is then solved for the unknown spectral reflectances. Experiments are presented, using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus, as low and medium spatial resolution image data, respectively, acquired on the same date over the Tapajos study site, Brazilian Amazon. Three component classes or endmembers are present in the scene covered by the experiment, namely vegetation, exposed soil, and shade. The components' spectral reflectance for the Terra MODIS spectral bands were then estimated by applying the proposed methodology. The reliability of these estimates is appraised by analyzing scatter diagrams produced by the Terra MODIS spectral bands and also by comparing the fraction images produced using both image datasets. This methodology appears appropriate for up-scaling information for regional and global studies.     

Techniques for flexible image/video resolution conversion with heterogeneous terminals

Multimedia capturing and display devices of different resolutions and aspect ratios can be easily connected by networks and, thus, there is a great need to develop techniques that facilitate flexible image/video format conversion and content adaptation among these heterogeneous terminals. Quality degradation due to downsampling, up-sampling, coding/decoding, and some content adaptation mechanism (say, image mosaicking) in the transmission process is inevitable. It is desirable that multimedia contents can be easily captured, displayed, and seamlessly composed. Challenges and techniques to achieve this goal are reviewed first. Then, two specific topics, i.e., image/video mosaicking and super resolution (SR) conversion, are highlighted. As compared with previous work developed for these problems, the challenge under the current context is to strike a balance between low computational complexity and high quality of resultant image/video. Several new developments along this line are discussed     

Generative bayesian image super resolution with natural image prior

We propose a new single image super resolution (SR) algorithm via Bayesian modeling with a natural image prior modeled by a high-order Markov random field (MRF). SR is one of the long-standing and active topics in image processing community. It is of great use in many practical applications, such as astronomical observation, medical imaging, and the adaptation of low-resolution contents onto high-resolution displays. One category of the conventional approaches for image SR is formulating the problem with Bayesian modeling techniques and then obtaining its maximum-a-posteriori solution, which actually boils down to a regularized regression task. Although straightforward, this approach cannot exploit the full potential offered by the probabilistic modeling, as only the posterior mode is sought. On the other hand, current Bayesian SR approaches using the posterior mean estimation typically use very simple prior models for natural images to ensure the computational tractability. In this paper, we present a Bayesian image SR approach with a flexible high-order MRF model as the prior for natural images. The minimum mean square error (MMSE) criteria are used for estimating the HR image. A Markov chain Monte Carlo-based sampling algorithm is presented for obtaining the MMSE solution. The proposed method cannot only enjoy the benefits offered by the flexible prior, but also has the advantage of making use of the probabilistic modeling to perform a posterior mean estimation, thus is less sensitive to the local minima problem as the MAP solution. Experimental results indicate that the proposed method can generate competitive or better results than state-of-the-art SR algorithms.