The fundamental goal of the GeoVISTA Studio project is to improve geoscientific analysis by providing an environment that operationally integrates a wide range of analysis activities, including those both computationally and visually based. We argue here that improving the infrastructure used in analysis has far-reaching potential to better integrate human-based and computationally-based expertise, and so ultimately improve scientific outcomes. But to address these challenges, some difficult system design and software engineering problems must be overcome. This paper illustrates the design of a component-oriented system, GeoVISTA Studio, as a means to overcome such difficulties by using state-of-the-art component-based software engineering techniques. Advantages described include: ease of program construction (visual programming), an open (non-proprietary) architecture, simple component-based integration and advanced deployment methods. This versatility has the potential to change the nature of systems development for the geosciences, providing better mechanisms to coordinate complex functionality, and as a consequence, to improve analysis by closer integration of software tools and better engagement of the human expert. Two example applications are presented to illustrate the potentia l of the Studio environment for exploring and better understanding large, complex geographical datasets and for supporting complex visual and computational analysis. Keywords: visual programming, exploratory data analysis (EDA), knowledge construction, Java, component-oriented programming (COP). 1 1

INTRODUCTION The Support Vector Machine #SVM# is a relatively recent approachintroduced by Boser, Guyon, and Vapnik #Boser et al., 1992#, #Vapnik, 1995# for solving supervised classi#cation and regression problems, or more colloquially learning from examples. In the following we will discuss only classi#cation and its application to hyperspectral data from AVIRIS. Traditionally, classi#ers model the underlying densityofthevarious classes and then #nd a separating surface. However density estimation in high-dimensional spaces su#ers from the Hughes e#ect #Hughes, 1968#, #Landgrebe, 1999#: For a #xed amount of training data the classi#cation accuracy as a function of number of bands reaches a maximum and then declines, because there is limited amount of training data to estimate the large number of parameters needed. Thus usually, a feature selection step is #rst performed on the high-dimensional data to reduce its dimensionality. As we will demon

The advancement of GIS data models to allow the effective utilization of very large heterogeneous geographic databases requires a new approach that incorporates models of human cognition. The ultimate goal is to provide a cooperative human-computer environment for spatial analysis. We describe the Pyramid framework as an example of this new approach within the context of some important aspects of how humans conceptually store spatial information. The proposed framework provides the means to create multiple structural interpretations of observed geographic data and the ability to build knowledge hierarchies through the application of data mining and other statistical techniques. 1.

Geospatial databases are needed for many tasks in civilian and military applications. Automated building detection and description systems attempt to construct 3-D models using primarily PAN (panchromatic) images. These systems can make use of cues derived from other sensor modalities to make the task easier and more robust. The recent development of hyperspectral sensors such as HYDICE (HYperspectral Digital Imagery Collection Experiment) can provide reasonably accurate thematic maps. Such data, however, tends to be of lower resolution, have geometric distortions and camera models are needed to map points between the different sensors. We use the thematic map to provide cues for presence of buildings in the PAN images for accurate delineation. It is shown that such cues can not only greatly improve the efficiency of the automatic building detection system but also improve the quality of the results. Quantitative evaluations are given. Key Words: Information Integration, Sensor Fusion...

Classification of land cover based on hyperspectral data is very challenging because typically tens of classes with uneven priors are involved, the inputs are high dimensional, and there is often scarcity of labeled data. Several researchers have observed that it is often preferable to decompose a multi-class problem into multiple two-class problems, solve each such sub-problem using a suitable binary classifier, and then combine the outputs of this collection of classifiers in a suitable manner to obtain the answer to the original multi-class problem. This approach is taken by the popular error correcting output codes (ECOC) technique, as well by the binary hierarchical classifier (BHC). Classical techniques for dealing with small sample sizes include regularization of covariance matrices and feature reduction. In this paper we address the twin problems of small sample sizes and multi-class settings by proposing a feature reduction scheme that adaptively adjusts to the amount of labeled data available. This scheme can be used in conjunction with ECOC and the BHC, as well as other approaches such as round-robin classification that decompose a multi-class problem into a number of two (meta)-class problems. In particular, we develop the best-basis binary hierarchical classifier (BB-BHC) and best basis

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Comparison in the RGB domain is not suitable for precise color matching, due to the strong dependency of this domain on factors like spectral power distribution of the light source and object geometry. We have studied the use of multispectral or hyperspectral images for color matching, since it can be proven that hyperspectral images can be made independent of the light source (color constancy) and object geometry (normalized color constancy). Hyperspectral images have the disadvantage that they are large compared to regular RGB-images, which makes it infeasible to use them for image matching across the Internet. For red roses, it is possible to reduce the large number of bands (>100) of the spectral images to only three bands, the same number as of an RGB-image, using Principal Component Analysis, while maintaining 99% of the original variation. The obtained PCA-images of the roses can be matched using for example histogram cross correlation. From the principal coordinates plot, obta...