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A Beowulf-type cluster can: (1) mitigate many issues associated with the analysis of large, complex remotely sensed data sets; (2) shorten the response time of operational agencies to crisis-management situations; and (3) expedite the reanalysis of large archives of satellite data. Whereas most Beowulf-type designs support modeling applications, the Parallel Image Processing Environment (PIPE) addresses the unique requirements of remote sensing applications. PIPE has four hierarchical layers: hardware, operating system, middleware and applications. Rocks, a middleware sublayer, manages the cluster. DIAL-developed interprocess communication and control daemons form the second middleware sublayer. They encapsulate user-defined applications and thereby support automated, user-transparent parallelization of satellite data analyses, implemented in the applications layer using generalized constructs. The daemons also monitor resource (computational and I/O) utilization on a node/thread basis, a feature not supported by other generally available monitoring utilities. The application support libraries are fully extensible, facilitate the reuse of modular and commonly used software functions in new applications and thereby reduce both the cost and time to implement new applications. Two applications (signal analysis, image classification) show PIPE's versatility and performance characteristics. PIPE is intrinsically scalable, reliable and can be incrementally implemented. A comparison with other embarrassingly parallel systems is also provided. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Polar sea ice plays a critical role in regulating the global climate. Seasonal variation in sea ice extent, however, coupled with the difficulties associated with in situ observations of polar sea ice, makes remote sensing the only practical way to estimate this important climatic variable on the space and time scales required. Unfortunately, accurate retrieval of sea ice extent from satellite data is a difficult task. Sea ice and high cold clouds have similar visible reflectance, but some other types of clouds can appear darker than sea ice. Moreover, strong atmospheric inversions and isothermal structures, both common in winter at some polar locations, further complicate the classification. This paper uses a combination of feed-forward neural networks and 1.6-µm data from the new Chinese Fengyun-1C satellite to mitigate these difficulties. The 1.6-µm data are especially useful for detecting illuminated water clouds in polar regions because 1) at 1.6 µm, the reflectance of water droplets is significantly higher than that of snow or ice and 2) 1.6-µm data are unaffected by atmospheric inversions. Validation data confirm the accuracy of the new classification technique. Application to other sensors with 1.6-µm capabilities also is discussed. [ABSTRACT FROM AUTHOR]

Presents information on a study which investigated the bidirectional reflectance anisotropy in Advanced Very High Resolution Radiometer (AVHRR) Channel 1 and 2 spectral regions for snow- and ice-covered surfaces. Description and development of the radiative transfer model; Modeling of the bidirectional reflectance for snow and sea ice surfaces; Conclusion.