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Abstract. The information content of remote sensing imagery depends upon various factors such as spatial and radiometric resolutions, spatial scale of the features to be imaged, radiometric contrast between diVerent target types, and also the nal application for which the imagery has been acquired. Various textural measures are used to characterize the image information content, based upon which diVerent image processing algorithms are employed to enhance this quantity. Previous work in this area has resulted in three diVerent approaches for quantifying image information content, primarily based on interpretability, mutual information, and entropy. These approaches, although well re ned, are diYcult to apply to all types of remote sensing imagery. Our approach to quantifying image information content is based upon classi cation accuracy. We propose an exponential model for information content based upon target-background contrast, and target size relative to pixel size. The model is seen to be applicable for relating information content to spatial resolution for real Landsat Thematic Mapper (TM) as well as Shuttle Imaging Radar-C (SIR-C) images. An interesting conclusion that emerges from this model is that although the TM image has higher information content than the SIR-C image at smaller pixel sizes, the opposite is true at larger pixel sizes. The transition occurs at a pixel size of about 720 m. This tells us that for applications that require high resolution (or smaller pixel sizes), the TM sensor is more useful for terrain classi cation, while for applications involving lower resolutions (or larger pixel sizes), the SIR-C sensor has an advantage. Thus, the model is useful in comparing diVerent sensor types for diVerent applications.

Comprehensive two-dimensional gas chromatography (GC × GC) is amongst the most powerful separation technologies currently existing. Since its advent in early 1990, it has become an established method which is readily available. However, one of its most challenging aspects, especially in hyphenation with mass spectrometry is the high amount of chemical information it provides for each measurement. The GC × GC community agrees that there, the highest demand for action is found. In response, the number of software packages allowing for in-depth data processing of GC × GC data has risen over the last couple of years. These packages provide sophisticated tools and algorithms allowing for more streamlined data evaluation. However, these tools/algorithms and their respective specific functionalities differ drastically within the available software packages and might result in various levels of findings if not appropriately implemented by the end users. This study focuses on two main objectives. First, to propose a data analysis framework and second to propose an open-source dataset for benchmarking software options and their specificities. Thus, allowing for an unanimous and comprehensive evaluation of GC × GC software. Thereby, the benchmark data includes a set of standard compound measurements and a set of chocolate aroma profiles. On this foundation, eight readily available GC × GC software packages were anonymously investigated for fundamental and advanced functionalities such as retention and detection device derived parameters, revealing differences in the determination of e.g. retention times and mass spectra.

Abstract-This paper describes a technique for restoring and reconstructing a scene from overlapping images. In situations where there are multiple, overlapping images of the same scene, it may be desirable to create a single image that most closely approximates the scene, based on the data in all of the available images. For example, successive swaths acquired by NASA's moderate imaging spectrometer (MODIS) will overlap, particularly at wide scan angles, creating a severe visual artifact in the output image. Resampling the overlapping swaths to produce a more accurate image on a uniform grid requires restoration and reconstruction. The one-pass restoration and reconstruction technique developed in this paper yields mean-square optimal resampling, based on a comprehensive end-to-end system model that accounts for image overlap and is subject to user-defined and data-availability constraints on the spatial support of the filter.

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