The production of geospatial information from overhead imagery is generally a labor-intensive process. Analysts must accurately delineate and extract important features, such as buildings, roads, and landcover from the imagery. Automated feature extraction (AFE) tools offer the prospect of reducing analyst's workload. This paper presents a new tool, called iMVS, for extracting buildings and discusses user testing conducted by the National Geospatial-Intelligence Agency (NGA). Using a semi-automated approach, iMVS processes two or more images to form a set of hypothesized 3-D buildings. When the user clicks on one of the building vertices, the system determines which hypothesis is the best fit and extracts the building. A set of powerful editing tools support rapid clean-up of the extraction, including extraction of complex buildings. User testing of iMVS provides an assessment of the benefits and identifies areas for system improvement.

Automatically extracting object models from images is a complex task. We describe research in extracting 3D models of buildings from aerial images. This work has resulted in several related systems including assisted extraction (minimal manual interaction to guide automatic processing), automatic extraction with limited imagery and limited building models, and automatic extraction with very good imagery and digital elevation models and more complex building models. Some results are provided for the assisted system and one of the automatic systems.

A technique to analyze patterns in terms of individual texture primitives and their spatial relationships is described. The technique is applied to natural textures. The descriptions consist of the primitive sizes and their repetition pattern if any. The derived descriptions can be used for recognition or reconstruction of the pattern.* INTRODUCTION Areas of an image are better characterized by descriptions of their texture than by pure intensity information. Texture is most easily described as the pattern of the spatial arrangement of different intensities (or colors). The different textures in an image are usually very apparent to a human observer, but automatic description of these patterns has proved to be very complex. In this research, we are concerned with a description of the texture which corresponds, in some sense, to a description produced by a person looking at the image. Many statistical textural measures have been proposed in the past [1-4]. Reference 1 gives a good review of various texture analysis methods. Among the statistical measures which have been discussed, and used, analysis of generalized gray-level co-occurrence matrices [2], analysis of edge directions with co-occurrence matrices [3], and analysis of the edges (or micro-edges) in a subwindow [4]. The statistical methods, by themselves, do not produce descriptions in the form which we desire. Some of the measures may indicate certain underlying structures in the pattern, but do not produce a general description. The Fourier transform has been used to determine some structural descriptions but was only partially successful for more complex patterns [5].

There have been many different approaches to texture description, primarily statistical techniques although there has been some work on structural texture analysis all along. We present here a technique which can be used to easily derive parts of the structural description-the regularity information in particular. Some limits on this method and its use in an overall texture description system are discussed.